Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
5,300 | 5,799 | A hybrid sampler for Poisson-Kingman mixture
models
Mar??a Lomel??
Gatsby Unit
University College London
mlomeli@gatsby.ucl.ac.uk
Stefano Favaro
Department of Economics and Statistics
University of Torino and Collegio Carlo Alberto
stefano.favaro@unito.it
Yee Whye Teh
Department of Statistics
University of Oxford
y.w... | 5799 |@word dtk:1 flexiblity:1 km:1 confirms:1 simulation:5 calculus:1 p0:4 pick:1 carry:1 initial:1 efficacy:1 outperforms:2 existing:5 current:1 si:7 dx:1 reminiscent:1 written:1 readily:1 tilted:3 subsequent:1 partition:8 happen:1 dtq:2 update:2 generative:3 intelligence:1 es:8 ith:1 math:1 evy:5 toronto:1 firstly:1... |
5,301 | 58 | 730
Analysis of distributed representation of
constituent structure in connectionist systems
Paul Smolensky
Department of Computer Science, University of Colorado, Boulder, CO 80309-0430
Abstract
A general method, the tensor product representation, is described for the distributed representation of
value/variable bin... | 58 |@word cu:1 loading:1 seems:1 simulation:1 prominence:1 decomposition:21 rol:1 fonn:1 series:1 past:1 existing:4 imaginary:1 activation:2 must:6 readily:1 numerical:1 realistic:1 enables:1 designed:1 treating:1 intelligence:3 item:2 utterly:1 characterization:1 provides:2 node:1 successive:2 preference:1 simpler:3 m... |
5,302 | 580 | Multi-State Time Delay Neural Networks
for Continuous Speech Recognition
Alex Waibel
Patrick Haffner
CNET Lannion A TSSIRCP
22301 LANNION, FRANCE
haffner@lannion.cnet.fr
Carnegie Mellon University
Pittsburgh, PA 15213
ahw@cs.cmu.edu
Abstract
We present the "Multi-State Time Delay Neural Network" (MS-TDNN) as an
ext... | 580 |@word nd:3 closure:1 tried:1 covariance:1 q1:1 tr:1 substitution:2 contains:1 score:19 subword:1 existing:1 current:1 comparing:1 activation:2 yet:2 lang:1 must:1 written:1 entrance:1 cindy:1 designed:2 interpretable:1 discrimination:2 tenn:1 selected:1 parameterization:1 beginning:1 denis:1 toronto:1 sigmoidal:1 ... |
5,303 | 5,800 | Tree-Guided MCMC Inference for Normalized
Random Measure Mixture Models
Juho Lee and Seungjin Choi
Department of Computer Science and Engineering
Pohang University of Science and Technology
77 Cheongam-ro, Nam-gu, Pohang 37673, Korea
{stonecold,seungjin}@postech.ac.kr
Abstract
Normalized random measures (NRMs) provide... | 5800 |@word trial:2 cox:1 middle:2 briefly:1 version:1 c0:27 calculus:1 simulation:1 covariance:2 pick:3 tr:5 shot:1 recursively:2 contains:1 selecting:1 document:1 interestingly:1 existing:7 current:2 comparing:3 si:3 scatter:1 dx:8 written:5 must:1 partition:26 designed:1 plot:4 update:3 hash:1 generative:4 leaf:6 gr... |
5,304 | 5,801 | Reflection, Refraction, and Hamiltonian Monte Carlo
Justin Domke
National ICT Australia (NICTA) &
Australian National University
Canberra, ACT 0200
Justin.Domke@nicta.com.au
Hadi Mohasel Afshar
Research School of Computer Science
Australian National University
Canberra, ACT 0200
hadi.afshar@anu.edu.au
Abstract
Hamil... | 5801 |@word bounced:1 determinant:4 version:1 lgorithms:1 simulation:5 accounting:1 p0:32 q1:55 carry:2 contains:1 tuned:11 outperforms:1 current:1 com:1 discretization:1 must:5 john:1 numerical:4 partition:2 stationary:5 half:3 fewer:3 advancement:1 intelligence:1 plane:5 isotropic:2 hamiltonian:33 accepting:2 detecti... |
5,305 | 5,802 | Planar Ultrametrics for Image Segmentation
Charless C. Fowlkes
Department of Computer Science
University of California Irvine
fowlkes@ics.uci.edu
Julian Yarkony
Experian Data Lab
San Diego, CA 92130
julian.yarkony@experian.com
Abstract
We study the problem of hierarchical clustering on planar graphs. We formulate
th... | 5802 |@word kohli:2 proportion:1 glue:1 termination:1 barahona:3 decomposition:1 paid:1 brightness:1 briggman:1 reduction:2 series:1 contains:3 current:2 com:1 yet:2 must:2 connectomics:1 written:4 additive:1 partition:7 kothe:3 eleven:1 designed:1 plot:1 cue:1 fewer:1 plane:10 core:1 iterates:1 provides:4 node:2 zhang... |
5,306 | 5,803 | Learning Bayesian Networks with Thousands of
Variables
Mauro Scanagatta
IDSIA? , SUPSI? , USI?
Lugano, Switzerland
mauro@idsia.ch
Cassio P. de Campos
Queen?s University Belfast
Northern Ireland, UK
c.decampos@qub.ac.uk
Giorgio Corani
IDSIA? , SUPSI? , USI?
Lugano, Switzerland
giorgio@idsia.ch
Marco Zaffalon
IDSIA?
L... | 5803 |@word polynomial:1 stronger:1 seems:1 nd:1 open:6 pick:1 moment:1 cyclic:1 contains:2 score:50 liu:1 outperforms:9 existing:1 mishra:1 current:4 comparing:1 com:2 yet:1 happen:1 v:2 implying:1 greedy:9 intelligence:9 malone:3 plane:1 provides:1 node:22 firstly:1 zhang:1 five:3 dn:1 along:1 descendant:7 yuan:3 int... |
5,307 | 5,804 | Parallel Predictive Entropy Search for Batch Global
Optimization of Expensive Objective Functions
Amar Shah
Department of Engineering
Cambridge University
as793@cam.ac.uk
Zoubin Ghahramani
Department of Engineering
University of Cambridge
zoubin@eng.cam.ac.uk
Abstract
We develop parallel predictive entropy search (P... | 5804 |@word exploitation:2 version:1 repository:1 pcc:1 mockus:1 simulation:3 eng:1 covariance:2 accounting:1 tr:1 ld:2 reduction:1 initial:1 contains:1 lichman:1 selecting:4 rippel:1 series:1 denoting:1 outperforms:1 freitas:1 current:2 obp:1 dx:1 must:2 written:1 yet:2 wx:1 analytic:4 burdick:1 plot:1 drop:1 sundaram... |
5,308 | 5,805 | Large-scale probabilistic predictors with and without
guarantees of validity
?
Vladimir Vovk? , Ivan Petej? , and Valentina Fedorova?
Department of Computer Science, Royal Holloway, University of London, UK
?
Yandex, Moscow, Russia
{volodya.vovk,ivan.petej,alushaf}@gmail.com
Abstract
This paper studies theoretically... | 5805 |@word illustrating:1 briefly:1 version:5 repository:2 essay:1 decomposition:1 p0:15 contains:1 score:28 daniel:2 existing:3 com:1 si:5 gmail:1 chu:1 gurevich:1 john:1 ronald:1 partition:1 s21:1 plot:6 update:1 half:1 intelligence:1 unacceptably:1 ntrain:3 xk:2 wolfram:1 papadopoulos:2 institution:1 provides:1 mul... |
5,309 | 5,806 | On the Accuracy of Self-Normalized
Log-Linear Models
Jacob Andreas?, Maxim Rabinovich?, Michael I. Jordan, Dan Klein
Computer Science Division, University of California, Berkeley
{jda,rabinovich,jordan,klein}@cs.berkeley.edu
Abstract
Calculation of the log-normalizer is a major computational obstacle in applications o... | 5806 |@word version:1 seems:2 norm:4 tedious:1 open:4 unif:3 hyv:1 seek:3 jacob:1 covariance:1 contrastive:2 dramatic:1 accommodate:1 initial:1 liu:1 series:1 score:3 efficacy:1 freitas:1 activation:1 yet:2 attracted:1 must:3 bd:1 john:1 plot:1 v:1 intelligence:3 prohibitive:2 leaf:1 parameterization:2 mccallum:1 begin... |
5,310 | 5,807 | Policy Evaluation Using the ?-Return
Scott Niekum
University of Texas at Austin
Philip S. Thomas
University of Massachusetts Amherst
Carnegie Mellon University
Georgios Theocharous
Adobe Research
George Konidaris
Duke University
Abstract
We propose the ?-return as an alternative to the ?-return currently used by th... | 5807 |@word open:1 seek:1 r:17 covariance:9 accounting:2 solid:4 initial:2 selecting:1 document:1 outperforms:1 existing:1 past:1 hasselt:1 comparing:1 written:1 must:5 subsequent:1 plm:1 treating:1 drop:1 depict:2 intelligence:3 parameterization:1 beginning:1 ith:1 short:1 farther:1 provides:1 revisited:1 five:1 direc... |
5,311 | 5,808 | Community Detection via Measure Space Embedding
Shie Mannor
The Technion, Haifa, Israel
shie@ee.technion.ac.il
Mark Kozdoba
The Technion, Haifa, Israel
markk@tx.technion.ac.il
Abstract
We present a new algorithm for community detection. The algorithm uses random walks to embed the graph in a space of measures, after... | 5808 |@word version:4 compression:2 seems:1 c0:2 dekel:1 condon:1 decomposition:1 anthropological:1 initial:2 configuration:1 contains:1 score:3 daniel:1 ours:2 current:1 anne:1 si:2 yet:1 partition:34 informative:1 enables:1 analytic:1 designed:1 farkas:1 stationary:1 generative:2 half:2 short:1 santo:5 blei:1 detecti... |
5,312 | 5,809 | The Consistency of Common Neighbors for
Link Prediction in Stochastic Blockmodels
Deepayan Chakrabarti
IROM, McCombs School of Business
University of Texas at Austin
deepay@utexas.edu
Purnamrita Sarkar
Department of Statistics
University of Texas at Austin
purnamritas@austin.utexas.edu
Peter Bickel
Department of Stat... | 5809 |@word cu:6 stronger:1 simulation:2 crucially:1 decomposition:3 citeseer:2 pick:2 harder:3 series:1 score:8 efficacy:1 current:1 z2:1 recovered:1 surprising:1 universality:1 router:1 attracted:2 must:2 partition:2 enables:1 plot:1 spec:1 vanishing:1 olhede:1 farther:1 detecting:1 node:88 simpler:1 become:2 chakrab... |
5,313 | 581 | Burst Synchronization Without
Frequency-Locking in a Completely Solvable
Network Model
Heinz Schuster
Institut fur theoretische Physik
Universitat Kiel
OlshausenstraBe 40
2300 Kiel 1, Germany
Christof Koch
Computation and Neural System Program
California Institute of Technology
Pasadena, California 91125, USA
Abstra... | 581 |@word physik:1 calculus:1 simulation:3 covariance:1 excited:1 initial:5 configuration:1 contains:1 attracted:1 written:2 subsequent:2 realistic:1 underly:1 dampened:1 stationary:1 indefinitely:1 math:1 arctan:1 simpler:1 kiel:2 five:1 burst:21 qualitative:1 prove:1 consists:2 olfactory:2 introduce:2 manner:2 inter... |
5,314 | 5,810 | Inference for determinantal point processes
without spectral knowledge
R?
emi Bardenet?
CNRS & CRIStAL
UMR 9189, Univ. Lille, France
remi.bardenet@gmail.com
?
Michalis K. Titsias?
Department of Informatics
Athens Univ. of Economics and Business, Greece
mtitsias@aueb.gr
Both authors contributed equally to this work.
... | 5810 |@word determinant:13 compression:1 polynomial:1 nd:2 open:1 d2:1 simulation:1 decomposition:8 covariance:1 nystr:2 solid:1 tr:4 moment:1 initial:2 contains:2 exclusively:1 hardy:1 interestingly:1 ka:2 com:1 comparing:1 arkk:1 gmail:1 vere:1 determinantal:11 numerical:1 cheap:5 drop:1 interpretable:1 fasshauer:1 g... |
5,315 | 5,811 | Sample Complexity of Learning Mahalanobis
Distance Metrics
Kristin Branson
Janelia Research Campus, HHMI
bransonk@janelia.hhmi.org
Nakul Verma
Janelia Research Campus, HHMI
verman@janelia.hhmi.org
Abstract
Metric learning seeks a transformation of the feature space that enhances prediction quality for a given task. I... | 5811 |@word kulis:1 repository:1 version:5 norm:18 proportion:1 km:6 d2:2 seek:1 additively:1 accounting:1 elisseeff:1 pick:2 solid:4 efficacy:1 exclusively:1 lichman:1 tuned:3 suppressing:2 existing:3 err:20 z2:4 activation:1 must:1 partition:2 informative:3 remove:1 intelligence:1 fewer:1 selected:1 provides:5 charac... |
5,316 | 5,812 | Matrix Manifold Optimization for Gaussian Mixtures
Reshad Hosseini
School of ECE
College of Engineering
University of Tehran, Tehran, Iran
reshad.hosseini@ut.ac.ir
Suvrit Sra
Laboratory for Information and Decision Systems
Massachusetts Institute of Technology
Cambridge, MA.
suvrit@mit.edu
Abstract
We take a new loo... | 5812 |@word repository:1 briefly:1 version:5 polynomial:2 seems:2 stronger:1 middle:1 duda:1 nd:1 open:3 termination:1 wiesel:1 confirms:1 seek:1 covariance:8 decomposition:4 jacob:1 dramatic:1 mention:1 tr:4 incarnation:1 harder:1 minus:1 sepulchre:3 initial:4 configuration:1 series:1 mishra:1 current:2 must:1 import:... |
5,317 | 5,813 | Scale Up Nonlinear Component Analysis with
Doubly Stochastic Gradients
Bo Xie1 , Yingyu Liang2 , Le Song1
1
Georgia Institute of Technology
bo.xie@gatech.edu, lsong@cc.gatech.edu
2
Princeton University
yingyul@cs.princeton.edu
Abstract
Nonlinear component analysis such as kernel Principle Component Analysis
(KPCA) an... | 5813 |@word version:1 polynomial:1 norm:3 zkf:2 cos2:11 crucially:1 covariance:8 decomposition:1 tr:1 carry:1 reduction:3 initial:1 liu:1 contains:2 rkhs:7 existing:1 err:1 current:1 ka:5 comparing:1 recovered:2 must:1 regenerating:2 john:1 subsequent:1 realistic:1 shape:1 hofmann:1 update:35 half:1 prohibitive:1 intel... |
5,318 | 5,814 | Parallel Correlation Clustering on Big Graphs
Xinghao Pan?,? , Dimitris Papailiopoulos?,? , Samet Oymak?,? ,
Benjamin Recht?,?, , Kannan Ramchandran? , and Michael I. Jordan?,?,
?
AMPLab, ? EECS at UC Berkeley, Statistics at UC Berkeley
Abstract
Given a similarity graph between items, correlation clustering (CC) group... | 5814 |@word worsens:1 cu:5 version:2 milenkovic:1 compression:1 seems:2 pick:6 incurs:1 solid:1 harder:1 reduction:1 liu:1 series:1 nii:2 interestingly:1 prefix:2 e2b:2 bilal:1 com:1 comparing:1 assigning:1 yet:2 written:1 boldi:3 partition:2 happen:1 remove:5 plot:1 drop:1 greedy:3 selected:2 item:9 amir:1 core:5 shor... |
5,319 | 5,815 | Fast Bidirectional Probability Estimation in Markov
Models
Siddhartha Banerjee ?
sbanerjee@cornell.edu
Peter Lofgren?
plofgren@cs.stanford.edu
Abstract
We develop a new bidirectional algorithm for estimating Markov chain multi-step
transition probabilities: given a Markov chain, we want to estimate the probability of... | 5815 |@word h:1 version:3 briefly:1 pw:3 vi1:1 simulation:2 crucially:1 uncovers:1 jacob:1 tr:4 solid:2 doeblin:6 initial:1 celebrated:1 uncovered:3 score:4 ecole:1 ours:1 document:2 existing:2 current:1 whp:1 michal:1 lang:1 boldi:1 john:1 numerical:1 shlomo:1 christian:1 update:5 v:1 stationary:9 operationally:1 webs... |
5,320 | 5,816 | Evaluating the statistical significance of biclusters
Jason D. Lee, Yuekai Sun, and Jonathan Taylor
Institute of Computational and Mathematical Engineering
Stanford University
Stanford, CA 94305
{jdl17,yuekai,jonathan.taylor}@stanford.edu
Abstract
Biclustering (also known as submatrix localization) is a problem of hi... | 5816 |@word manageable:1 pw:2 unif:5 seek:2 covariance:1 asks:1 tr:24 contains:1 score:13 zij:1 selecting:3 genetic:1 outperforms:1 parameter1:1 attracted:2 readily:2 must:3 partition:1 plot:1 greedy:23 selected:17 characterization:2 location:1 ames:2 mathematical:3 consists:1 polyhedral:7 manner:1 growing:2 jm:4 consi... |
5,321 | 5,817 | Regularization Path of
Cross-Validation Error Lower Bounds
Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama, and Ichiro Takeuchi
Nagoya Institute of Technology
Nagoya, 466-8555, Japan
{shibagaki.a.mllab.nit,suzuki.mllab.nit}@gmail.com
{karasuyama,takeuchi.ichiro}@nitech.ac.jp
Abstract
Careful tuning of a regular... | 5817 |@word cu:13 illustrating:1 repository:1 norm:1 advantageous:2 nd:1 seems:1 d2:4 simplifying:1 hsieh:1 liblinear:2 initial:1 liu:2 score:8 hereafter:1 ours:1 existing:2 current:5 com:2 comparing:2 gmail:1 written:2 must:1 numerical:1 plot:2 larization:1 update:1 selected:3 svmguide1:1 core:2 rabbani:1 direct:1 inc... |
5,322 | 5,818 | Collaboratively Learning Preferences from Ordinal
Data
Sewoong Oh , Kiran K. Thekumparampil
University of Illinois at Urbana-Champaign
{swoh,thekump2}@illinois.edu
Jiaming Xu
The Wharton School, UPenn
jiamingx@wharton.upenn.edu
Abstract
In personalized recommendation systems, it is important to predict preferences of... | 5818 |@word version:2 polynomial:3 norm:20 seems:1 logit:7 c0:8 achievable:2 d2:70 confirms:1 seek:1 decomposition:1 jacob:1 liu:1 daniel:1 neeman:1 past:2 existing:2 bradley:3 si:32 yet:1 ij1:1 written:1 john:1 numerical:3 realistic:1 j1:14 engg:1 rd2:1 item:41 record:1 parkes:2 provides:4 math:1 preference:29 simpler... |
5,323 | 5,819 | SGD Algorithms based on Incomplete U -statistics:
Large-Scale Minimization of Empirical Risk
Guillaume Papa, St?ephan Cl?emenc?on
LTCI, CNRS, T?el?ecom ParisTech
Universit?e Paris-Saclay, 75013 Paris, France
first.last@telecom-paristech.fr
Aur?elien Bellet
Magnet Team, INRIA Lille - Nord Europe
59650 Villeneuve d?Asc... | 5819 |@word version:2 briefly:1 norm:1 km:1 seek:1 bn:1 accounting:1 decomposition:2 covariance:1 sgd:26 solid:2 reduction:4 initial:2 chervonenkis:1 janson:1 outperforms:1 existing:1 scatter:1 dx:3 numerical:3 partition:1 subsequent:1 cheap:1 n0:2 juditsky:1 selected:1 xk:7 wahrsch:1 provides:1 zhang:3 along:1 c2:1 ik... |
5,324 | 582 | Combined Neural Network and Rule-Based
Framework for Probabilistic Pattern Recognition
and Discovery
Hayit K. Greenspan and Rodney Goodman
Department of Electrical Engineering
California Institute of Technology, 116-81
Pasadena, CA 91125
Rama Chellappa
Department of Electrical Engineering
Institute for Advanced Comput... | 582 |@word version:1 simulation:2 decomposition:1 initial:6 contains:3 existing:2 discretization:1 com:1 yet:1 reminiscent:1 herring:3 informative:4 enables:6 grass:4 discrimination:1 intelligence:1 calf:1 filtered:2 quantized:3 node:4 contribute:1 five:1 consists:2 prove:1 behavior:1 encouraging:1 window:2 becomes:1 p... |
5,325 | 5,820 | ??????????? ???????????? ??? ?????????? ????????
???? ????????????? ???????
??????? ????
????????? ????????? ?????
?????????????????????
????? ??????
?????????? ?? ????????? ??? ?????? ???
?????????????????
????????
?? ?????????? ???????? ????????? ????????????? ??????? ??? ????????????? ????????
??????????? ?? ?? ?... | 5820 |@word |
5,326 | 5,821 | On Variance Reduction in Stochastic Gradient
Descent and its Asynchronous Variants
Sashank J. Reddi
Carnegie Mellon University
sjakkamr@cs.cmu.edu
Ahmed Hefny
Carnegie Mellon University
ahefny@cs.cmu.edu
Suvrit Sra
Massachusetts Institute of Technology
suvrit@mit.edu
Barnab?as P?oczos
Carnegie Mellon University
bapoc... | 5821 |@word briefly:1 version:10 stronger:1 nd:4 c0:2 dekel:1 urb:1 instruction:1 km:2 pick:1 incurs:1 sgd:8 thereby:3 incarnation:1 harder:1 carry:1 reduction:13 liu:3 tuned:1 outperforms:2 current:1 comparing:1 si:5 numerical:1 plot:2 update:25 juditsky:1 indicative:1 xk:8 iso:1 core:4 provides:4 iterates:6 bittorf:1... |
5,327 | 5,822 | Subset Selection by Pareto Optimization
Chao Qian
Yang Yu
Zhi-Hua Zhou
National Key Laboratory for Novel Software Technology, Nanjing University
Collaborative Innovation Center of Novel Software Technology and Industrialization
Nanjing 210023, China
{qianc,yuy,zhouzh}@lamda.nju.edu.cn
Abstract
Selecting the optimal s... | 5822 |@word kong:1 version:1 polynomial:1 norm:11 nd:1 triazine:2 r:4 covariance:5 bellevue:1 initial:1 configuration:1 series:2 selecting:5 pub:1 o2:8 current:1 comparing:1 si:4 assigning:1 mushroom:2 must:5 distant:1 subsequent:1 plot:2 alone:1 greedy:13 selected:1 intelligence:2 zhang:5 five:1 unacceptable:1 prove:5... |
5,328 | 5,823 | Interpolating Convex and Non-Convex Tensor
Decompositions via the Subspace Norm
Ryota Tomioka
Toyota Technological Institute at Chicago
tomioka@ttic.edu
Qinqing Zheng
University of Chicago
qinqing@cs.uchicago.edu
Abstract
We consider the problem of recovering a low-rank tensor from its noisy observation. Previous wo... | 5823 |@word mild:1 version:4 polynomial:1 norm:54 c0:2 hu:1 km:1 simulation:1 confirms:1 bn:2 decomposition:14 contains:1 interestingly:2 current:1 recovered:2 written:1 numerical:2 concatenate:1 realistic:1 chicago:2 confirming:1 plot:2 selected:1 xk:1 core:2 simpler:1 five:1 along:3 constructed:2 direct:1 become:2 ik... |
5,329 | 5,824 | Fast, Provable Algorithms for Isotonic Regression in
all `p-norms ?
Rasmus Kyng
Dept. of Computer Science
Yale University
rasmus.kyng@yale.edu
Anup Rao?
School of Computer Science
Georgia Tech
arao89@gatech.edu
Sushant Sachdeva
Dept. of Computer Science
Yale University
sachdeva@cs.yale.edu
Abstract
Given a directed... | 5824 |@word cpe:1 version:1 polynomial:1 norm:31 closure:1 seek:1 crucially:1 decomposition:1 pick:1 boundedness:1 ipm:27 reduction:6 initial:3 score:2 daniel:2 denoting:1 pprox:9 current:1 com:2 assigning:1 additive:1 realistic:1 kdd:1 shape:2 analytic:1 plot:1 update:2 maxv:1 n0:2 implying:1 fewer:1 iso:9 short:1 mat... |
5,330 | 5,825 | Semi-Proximal Mirror-Prox
for Nonsmooth Composite Minimization
Niao He
Georgia Institute of Technology
nhe6@gatech.edu
Zaid Harchaoui
NYU, Inria
firstname.lastname@nyu.edu
Abstract
We propose a new first-order optimization algorithm to solve high-dimensional
non-smooth composite minimization problems. Typical exampl... | 5825 |@word h:1 msr:1 cox:1 norm:15 mimick:1 d2:2 linearized:2 decomposition:1 u11:1 ev1:1 denoting:1 frankwolfe:1 outperforms:2 v21:1 si:2 yet:2 written:1 readily:1 john:1 partition:2 cheap:1 zaid:3 designed:1 update:1 juditsky:5 v:1 intelligence:1 selected:1 kyk:5 core:1 provides:2 certificate:5 iterates:1 node:1 mat... |
5,331 | 5,826 | A Universal Primal-Dual Convex Optimization Framework
Alp Yurtsever:
:
Quoc Tran-Dinh;
Volkan Cevher:
Laboratory for Information and Inference Systems, EPFL, Switzerland
{alp.yurtsever, volkan.cevher}@epfl.ch
;
Department of Statistics and Operations Research, UNC, USA
quoctd@email.unc.edu
Abstract
We propose a ne... | 5826 |@word trial:2 version:2 briefly:2 norm:11 seems:1 r:1 seek:1 decomposition:3 p0:1 pg:1 tr:3 solid:1 initial:3 liu:1 series:1 mag:1 frankwolfe:2 existing:2 optim:1 dx:4 chu:1 hoboken:1 numerical:5 additive:1 partition:1 plot:2 update:2 juditsky:1 v:2 greedy:1 fewer:1 xk:10 propack:3 dissertation:1 core:1 volkan:2 ... |
5,332 | 5,827 | Sample Complexity of Episodic Fixed-Horizon
Reinforcement Learning
Emma Brunskill
Computer Science Department
Carnegie Mellon University
ebrun@cs.cmu.edu
Christoph Dann
Machine Learning Department
Carnegie Mellon University
cdann@cdann.net
Abstract
Recently, there has been significant progress in understanding reinf... | 5827 |@word exploitation:1 briefly:2 version:1 polynomial:2 p0:7 pick:1 carry:1 kappen:1 initial:2 existing:7 current:2 whp:1 si:8 must:1 readily:2 john:2 ronald:3 additive:2 wiewiora:1 enables:2 update:4 stationary:6 generative:2 intelligence:1 xk:15 short:1 indefinitely:1 provides:1 mannor:2 knownness:5 simpler:1 ucr... |
5,333 | 5,828 | Private Graphon Estimation for Sparse Graphs?
Christian Borgs
Jennifer T. Chayes
Microsoft Research New England
Cambridge, MA, USA.
{cborgs,jchayes}@microsoft.com
Adam Smith
Pennsylvania State University
University Park, PA, USA.
asmith@psu.edu
Abstract
We design algorithms for fitting a high-dimensional statistical... | 5828 |@word private:38 faculty:1 version:8 polynomial:2 norm:11 stronger:1 hu:1 sheffet:1 homomorphism:1 boundedness:1 ld:1 moment:1 contains:2 score:29 selecting:1 united:1 series:1 ours:3 janson:2 miklau:1 existing:1 current:1 com:1 analysed:1 assigning:2 partition:2 christian:1 designed:1 implying:1 generative:2 smi... |
5,334 | 5,829 | HONOR: Hybrid Optimization for NOn-convex
Regularized problems
Jieping Ye
Univeristy of Michigan, Ann Arbor, MI 48109
jpye@umich.edu
Pinghua Gong
Univeristy of Michigan, Ann Arbor, MI 48109
gongp@umich.edu
Abstract
Recent years have witnessed the superiority of non-convex sparse learning formulations over their conv... | 5829 |@word trial:3 briefly:1 norm:1 semicontinuous:1 covariance:1 boundedness:1 denoting:1 interestingly:1 ati:1 duong:1 current:2 must:2 john:1 numerical:1 happen:1 plot:2 gist:20 designed:1 v:2 intelligence:1 xk:69 core:1 iterates:1 revisited:1 zhang:6 mathematical:1 along:3 ik:5 prove:2 introduce:1 solver2:1 x0:1 b... |
5,335 | 583 | Polynomial Uniform Convergence of
Relative Frequencies to Probabilities
Alberto Bertoni, Paola Carnpadelli~ Anna Morpurgo, Sandra Panizza
Dipartimento di Scienze dell'Informazione
Universita degli Studi di Milano
via Comelico, 39 - 20135 Milano - Italy
Abstract
We define the concept of polynomial uniform convergence ... | 583 |@word concept:3 briefly:1 implies:7 polynomial:29 verify:3 hence:2 universita:1 open:2 correct:1 fa:1 ehrenfeucht:1 milano:3 ll:1 said:5 bianco:1 sandra:1 concatenation:1 preliminary:2 chervonenkis:12 proposition:1 outline:1 elementary:1 dipartimento:1 studi:1 hold:3 index:2 ef:1 fi:4 jef:1 readily:2 fn:46 fe:2 su... |
5,336 | 5,830 | A Convergent Gradient Descent Algorithm for
Rank Minimization and Semidefinite Programming
from Random Linear Measurements
John Lafferty
University of Chicago
lafferty@galton.uchicago.edu
Qinqing Zheng
University of Chicago
qinqing@cs.uchicago.edu
Abstract
We propose a simple, scalable, and fast gradient descent algo... | 5830 |@word trial:1 briefly:1 compression:1 polynomial:1 norm:21 c0:2 confirms:1 decomposition:4 tr:9 carry:1 initial:1 outperforms:2 existing:1 current:2 ka:2 z2:1 surprising:1 yet:3 must:1 john:2 chicago:2 numerical:1 subsequent:2 happen:1 recasting:1 enables:1 remove:1 drop:1 update:5 v:5 core:1 blei:1 iterates:2 pr... |
5,337 | 5,831 | Combinatorial Bandits Revisited
Richard Combes?
M. Sadegh Talebi?
Alexandre Proutiere?
Marc Lelarge?
Centrale-Supelec, L2S, Gif-sur-Yvette, FRANCE
? Department of Automatic Control, KTH, Stockholm, SWEDEN
? INRIA & ENS, Paris, FRANCE
richard.combes@supelec.fr,{mstms,alepro}@kth.se,marc.lelarge@ens.fr
?
Abstract
This ... | 5831 |@word trial:1 exploitation:2 version:1 instrumental:1 suitably:1 cm2:1 km:5 decomposition:4 selecting:4 mi0:1 sherali:1 tuned:2 outperforms:3 existing:3 past:1 multiuser:1 yajun:1 must:1 written:1 numerical:2 enables:1 update:2 v:1 selected:4 warmuth:1 beginning:3 vanishing:1 short:3 manfred:1 provides:5 revisite... |
5,338 | 5,832 | On Elicitation Complexity
Rafael Frongillo
University of Colorado, Boulder
Ian A. Kash
Microsoft Research
raf@colorado.edu
iankash@microsoft.com
Abstract
Elicitation is the study of statistics or properties which are computable via empirical risk minimization. While several recent papers have approached the genera... | 5832 |@word version:1 stronger:4 norm:1 nd:1 open:4 calculus:2 tried:1 p0:8 kent:1 concise:1 moment:4 contains:1 score:1 interestingly:1 savage:2 com:1 z2:2 si:2 assigning:1 yet:2 must:4 dx:1 partition:1 plane:3 xk:3 institution:1 characterization:7 provides:3 math:1 zhang:1 height:1 mathematical:2 along:3 ik:5 consist... |
5,339 | 5,833 | Online Learning with Adversarial Delays
Kent Quanrud? and Daniel Khashabi?
Department of Computer Science
University of Illinois at Urbana-Champaign
Urbana, IL 61801
{quanrud2,khashab2}@illinois.edu
Abstract
We study the performance of standard online learning algorithms when the feedback is delayed by an adversary. W... | 5833 |@word proportion:2 c0:9 kent:1 pick:5 moment:3 liu:2 daniel:1 document:1 omniscient:1 kx0:1 existing:1 must:1 readily:1 realize:1 subsequent:1 numerical:1 additive:2 kdd:1 designed:1 icac:1 update:3 warmuth:2 sys:6 draft:1 math:2 herbrich:1 bittorf:1 along:1 direct:1 prove:2 introduce:2 pairwise:1 x0:2 expected:1... |
5,340 | 5,834 | Structured Estimation with Atomic Norms:
General Bounds and Applications
Sheng Chen
Arindam Banerjee
Dept. of Computer Science & Engg., University of Minnesota, Twin Cities
{shengc,banerjee}@cs.umn.edu
Abstract
For structured estimation problems with atomic norms, recent advances in the literature express sample comp... | 5834 |@word multitask:1 cu:3 briefly:1 norm:120 paredes:1 hu:4 closure:1 d2:1 decomposition:4 jacob:1 kz1:1 tr:8 contains:2 series:2 romera:1 existing:2 ka:19 si:1 written:1 nt1:3 readily:1 additive:1 engg:1 v:3 intelligence:2 characterization:4 provides:1 math:1 kv0:6 zhang:1 along:1 c2:2 kvk2:5 direct:3 yuan:1 prove:... |
5,341 | 5,835 | Subsampled Power Iteration: a Unified Algorithm for
Block Models and Planted CSP?s
Will Perkins
University of Birmingham
w.f.perkins@bham.ac.uk
Vitaly Feldman
IBM Research - Almaden
vitaly@post.harvard.edu
Santosh Vempala
Georgia Tech
vempala@cc.gatech.edu
Abstract
We present an algorithm for recovering planted solu... | 5835 |@word version:2 polynomial:4 norm:7 open:1 km:3 decomposition:1 reduction:9 initial:2 neeman:3 ours:1 outperforms:1 current:1 whp:2 si:1 yet:1 assigning:1 attracted:1 partition:16 gv:3 resampling:1 selected:1 ith:1 bipartitions:1 lr:2 ron:1 simpler:1 zhang:1 constructed:1 direct:1 focs:2 consists:1 krzakala:1 x0:... |
5,342 | 5,836 | Learning Theory and Algorithms for
Forecasting Non-Stationary Time Series
Vitaly Kuznetsov
Courant Institute
New York, NY 10011
Mehryar Mohri
Courant Institute and Google Research
New York, NY 10011
vitaly@cims.nyu.edu
mohri@cims.nyu.edu
Abstract
We present data-dependent learning bounds for the general scenario of... | 5836 |@word mild:3 version:2 norm:3 suitably:1 open:1 decomposition:1 q1:2 series:22 united:1 ktv:5 chervonenkis:1 denoting:1 existing:1 z2:1 must:1 realistic:1 stationary:35 generative:3 selected:2 recherche:1 boosting:2 node:1 simpler:1 unbounded:2 along:1 consists:3 prove:3 shorthand:1 manner:1 introduce:2 expected:... |
5,343 | 5,837 | Empirical Localization of Homogeneous Divergences
on Discrete Sample Spaces
Takashi Takenouchi
Department of Complex and Intelligent Systems
Future University Hakodate
116-2 Kamedanakano, Hakodate, Hokkaido, 040-8655, Japan
ttakashi@fun.ac.jp
Takafumi Kanamori
Department of Computer Science and Mathematical Informatic... | 5837 |@word trial:3 hyv:2 d2:3 covariance:1 contrastive:2 moment:2 initial:1 series:1 score:3 outperforms:1 hakodate:2 optim:1 written:9 numerical:1 visible:1 partition:1 shape:1 plot:1 implying:3 core:1 characterization:2 toronto:1 firstly:1 mathematical:2 constructed:2 consists:1 behavior:1 frequently:3 salakhutdinov... |
5,344 | 5,838 | Multi-Layer Feature Reduction for Tree Structured
Group Lasso via Hierarchical Projection
Jie Wang1 , Jieping Ye1,2
Computational Medicine and Bioinformatics
2
Department of Electrical Engineering and Computer Science
University of Michigan, Ann Arbor, MI 48109
{jwangumi, jpye}@umich.edu
1
Abstract
Tree structured gr... | 5838 |@word briefly:1 stronger:1 norm:2 pillar:1 grey:2 simulation:1 decomposition:1 p0:1 reduction:7 liu:4 contains:1 series:3 sherali:1 past:1 existing:4 nt:1 bd:1 j1:1 remove:1 n0:1 a1k:1 intelligence:1 leaf:18 ith:3 smith:2 provides:1 node:72 simpler:1 zhang:2 height:1 mathematical:1 along:3 rabbani:1 direct:1 init... |
5,345 | 5,839 | Optimal Testing for Properties of Distributions
Jayadev Acharya, Constantinos Daskalakis, Gautam Kamath
EECS, MIT
{jayadev, costis, g}@mit.edu
Abstract
Given samples from an unknown discrete distribution p, is it possible to distinguish whether p belongs to some class of distributions C versus p being far from
every d... | 5839 |@word version:2 compression:1 clts:1 stronger:1 nd:12 justice:1 decomposition:3 invoking:1 mention:2 jafarpour:3 harder:1 accommodate:1 contains:1 bhattacharyya:1 must:4 mqi:3 class1:1 additive:1 partition:1 j1:1 shape:4 implying:1 fewer:1 ith:1 core:1 provides:1 characterization:1 gautam:1 ron:1 org:1 simpler:1 ... |
5,346 | 584 | Software for ANN training on a Ring Array Processor
Phil Kohn, Jeff Bilmes, Nelson Morgan, James Beck
International Computer Science Institute,
1947 Center St., Berkeley CA 94704, USA
Abstract
Experimental research on Artificial Neural Network (ANN) algorithms requires
either writing variations on the same program or... | 584 |@word version:3 open:2 instruction:1 propagate:3 tr:5 configuration:2 contains:5 series:1 daring:1 existing:1 err:2 current:3 activation:15 must:4 written:3 designed:1 fvec:3 update:4 leaf:2 device:1 selected:1 desktop:1 pointer:1 math:1 node:9 tinker:1 kingsbury:1 along:1 direct:1 become:2 driver:1 consists:3 sus... |
5,347 | 5,840 | Market Scoring Rules Act As Opinion Pools For
Risk-Averse Agents
Mithun Chakraborty, Sanmay Das
Department of Computer Science and Engineering
Washington University in St. Louis
St. Louis, MO 63130
{mithunchakraborty,sanmay}@wustl.edu
Abstract
A market scoring rule (MSR) ? a popular tool for designing algorithmic pre... | 5840 |@word mild:3 msr:26 unaltered:1 private:5 briefly:1 chakraborty:2 version:1 seems:2 c0:2 open:1 adrian:1 willing:3 hu:3 simulation:2 noregret:1 jacob:1 p0:11 citeseer:1 q1:2 attainable:1 shot:4 boundedness:2 recursively:2 carry:1 initial:3 uncovered:1 score:3 selecting:1 offering:3 interestingly:1 subjective:16 c... |
5,348 | 5,841 | Information-theoretic lower bounds for convex
optimization with erroneous oracles
Jan Vondr?ak
IBM Almaden Research Center
San Jose, CA 95120
jvondrak@us.ibm.com
Yaron Singer
Harvard University
Cambridge, MA 02138
yaron@seas.harvard.edu
Abstract
We consider the problem of optimizing convex and concave functions with... | 5841 |@word faculty:1 version:3 polynomial:1 seems:2 dekel:1 grey:1 nemirovsky:1 xout:4 asks:1 cyclic:1 selecting:1 united:1 daniel:1 interestingly:1 com:1 john:1 additive:8 partition:3 benign:1 christian:1 intelligence:1 item:1 desh:1 core:2 provides:2 tahoe:1 unbounded:2 symposium:1 prove:1 artner:1 fitting:1 manner:... |
5,349 | 5,842 | Bandit Smooth Convex Optimization:
Improving the Bias-Variance Tradeoff
Ofer Dekel
Microsoft Research
Redmond, WA
oferd@microsoft.com
Ronen Eldan
Weizmann Institute
Rehovot, Israel
roneneldan@gmail.com
Tomer Koren
Technion
Haifa, Israel
tomerk@technion.ac.il
Abstract
Bandit convex optimization is one of the fundamen... | 5842 |@word version:1 polynomial:2 stronger:1 norm:22 dekel:4 suitably:1 open:2 reused:1 d2:6 that2:1 decomposition:2 incurs:2 reduction:1 current:7 com:2 dikin:5 surprising:1 gmail:1 yet:5 written:1 subsequent:1 numerical:1 analytic:1 designed:1 intelligence:1 guess:1 beginning:1 completeness:1 mathematical:1 become:1... |
5,350 | 5,843 | Accelerated Mirror Descent
in Continuous and Discrete Time
Walid Krichene
UC Berkeley
Alexandre M. Bayen
UC Berkeley
Peter L. Bartlett
UC Berkeley and QUT
walid@eecs.berkeley.edu
bayen@berkeley.edu
bartlett@berkeley.edu
Abstract
We study accelerated mirror descent dynamics in continuous and discrete time.
Combini... | 5843 |@word version:1 polynomial:1 norm:3 seems:1 dekel:1 nemirovsky:1 tr:5 reduction:1 initial:4 series:3 pub:1 kx0:2 discretization:13 written:4 john:1 numerical:4 update:8 juditsky:2 amir:2 hamiltonian:1 lr:5 provides:3 equi:2 iterates:1 mathematical:4 along:1 differential:8 become:1 prove:8 introductory:1 x0:21 exp... |
5,351 | 5,844 | Adaptive Online Learning
Dylan J. Foster ?
Cornell University
Alexander Rakhlin ?
University of Pennsylvania
Karthik Sridharan ?
Cornell University
Abstract
We propose a general framework for studying adaptive regret bounds in the online
learning setting, subsuming model selection and data-dependent bounds. Given a
... | 5844 |@word mild:1 version:5 briefly:1 achievable:24 norm:8 open:1 d2:2 gradual:1 bn:57 initial:1 celebrated:1 selecting:2 daniel:1 erven:1 existing:2 err:2 di2:1 nt:7 surprising:1 luo:1 si:4 yet:2 dx:1 readily:1 john:1 remove:1 v:2 guess:1 chiang:1 provides:5 unbounded:1 along:1 c2:3 direct:1 prove:3 inside:1 manner:1... |
5,352 | 5,845 | Deep Visual Analogy-Making
Scott Reed Yi Zhang Yuting Zhang Honglak Lee
University of Michigan, Ann Arbor, MI 48109, USA
{reedscot,yeezhang,yutingzh,honglak}@umich.edu
Abstract
In addition to identifying the content within a single image, relating images and
generating related images are critical tasks for image unde... | 5845 |@word kohli:1 cnn:2 version:2 loading:1 sex:1 open:1 seek:1 jacob:1 sgd:3 wjf:1 shot:4 wrapper:1 animated:2 outperforms:2 greave:1 existing:1 current:1 ka:1 guadarrama:1 cad:1 luo:1 yet:1 written:1 gpu:1 additive:4 thrust:3 shape:12 enables:2 extrapolating:1 update:2 alone:1 generative:4 discovering:1 accordingly... |
5,353 | 5,846 | End-To-End Memory Networks
Sainbayar Sukhbaatar
Dept. of Computer Science
Courant Institute, New York University
sainbar@cs.nyu.edu
Arthur Szlam
Jason Weston
Rob Fergus
Facebook AI Research
New York
{aszlam,jase,robfergus}@fb.com
Abstract
We introduce a neural network with a recurrent attention model over a possibly... | 5846 |@word armand:1 version:9 norm:4 seems:2 out1:1 pick:2 initial:1 contains:1 score:1 daniel:1 tuned:3 ours:1 interestingly:1 o2:1 past:1 err:2 current:3 com:2 outperforms:1 activation:3 must:2 john:7 realistic:1 subsequent:1 drop:2 update:4 v:7 sukhbaatar:1 intelligence:1 alone:1 half:1 hallway:5 ith:1 short:1 reco... |
5,354 | 5,847 | Attention-Based Models for Speech Recognition
Dzmitry Bahdanau
Jacobs University Bremen, Germany
Jan Chorowski
University of Wroc?aw, Poland
jan.chorowski@ii.uni.wroc.pl
Dmitriy Serdyuk
Universit?e de Montr?eal
Kyunghyun Cho
Universit?e de Montr?eal
Yoshua Bengio
Universit?e de Montr?eal
CIFAR Senior Fellow
Abstra... | 5847 |@word middle:1 version:1 seems:1 norm:3 nd:1 reused:1 open:1 jacob:1 solid:1 initial:1 fragment:2 selecting:2 score:9 tuned:1 document:1 existing:1 contextual:1 si:14 activation:2 gpu:1 planet:1 happen:1 hypothesize:1 plot:1 update:1 sukhbaatar:2 selected:2 inspection:1 beginning:3 short:7 core:1 colored:1 math:1... |
5,355 | 5,848 | Where are they looking?
Adri`a Recasens?
Aditya Khosla?
Carl Vondrick
Massachusetts Institute of Technology
Antonio Torralba
{recasens, khosla, vondrick, torralba}@csail.mit.edu
(* - indicates equal contribution)
Abstract
Humans have the remarkable ability to follow the gaze of other people to identify
what they a... | 5848 |@word cnn:3 norm:1 nd:1 everingham:1 open:1 seek:1 attended:2 pick:2 harder:1 loc:2 jimenez:1 interestingly:2 outperforms:2 activation:3 must:2 realize:1 visible:1 concatenate:1 partition:1 remove:1 designed:1 cue:1 selected:1 accordingly:1 plane:1 recasens:2 detecting:2 quantized:2 contribute:1 location:30 org:1... |
5,356 | 5,849 | Semi-supervised Convolutional Neural Networks for
Text Categorization via Region Embedding
Rie Johnson
RJ Research Consulting
Tarrytown, NY, USA
riejohnson@gmail.com
Tong Zhang?
Baidu Inc., Beijing, China
Rutgers University, Piscataway, NJ, USA
tzhang@stat.rutgers.edu
Abstract
This paper presents a new semi-supervis... | 5849 |@word multitask:1 cnn:62 version:1 eliminating:2 confirms:2 seek:2 simplifying:1 pavel:1 q1:4 sgd:1 accommodate:1 reduction:1 electronics:1 liu:2 tuned:1 document:11 outperforms:2 existing:1 com:2 comparing:2 gmail:1 ronan:2 confirming:1 enables:1 christian:1 remove:1 plot:8 update:1 v:2 half:1 leaf:1 fewer:1 gen... |
5,357 | 585 | Fast, Robust Adaptive Control by Learning only
Forward Models
Andrew W. Moore
MIT Artificial Intelligence Laboratory
545 Technology Square, Cambridge, MA 02139
awmGai.JD.it.edu
Abstract
A large class of motor control tasks requires that on each cycle the controller is told its current state and must choose an action ... | 585 |@word trial:2 version:3 inversion:4 proportion:1 stronger:1 duda:1 lwk:2 instruction:1 simulation:2 ronchetti:1 shot:4 catastrophically:1 initial:5 series:1 contains:1 interestingly:1 current:2 must:1 john:1 subsequent:4 numerical:5 benign:2 motor:2 designed:1 update:8 stationary:2 intelligence:2 selected:2 cue:8 ... |
5,358 | 5,850 | Training Very Deep Networks
Rupesh Kumar Srivastava
Klaus Greff
?
Jurgen
Schmidhuber
The Swiss AI Lab IDSIA / USI / SUPSI
{rupesh, klaus, juergen}@idsia.ch
Abstract
Theoretical and empirical evidence indicates that the depth of neural networks
is crucial for their success. However, training becomes more difficult ... | 5850 |@word cnn:1 version:1 compression:1 suitably:1 open:1 shuicheng:1 sgd:4 harder:1 carry:2 initial:4 configuration:1 series:1 liu:1 current:1 com:1 comparing:1 guadarrama:1 activation:10 dx:1 must:1 romero:4 enables:1 christian:1 designed:2 intelligence:2 selected:1 fewer:2 shut:1 ivo:1 beginning:1 ith:2 vanishing:... |
5,359 | 5,851 | Deep Convolutional Inverse Graphics Network
Tejas D. Kulkarni*1 , William F. Whitney*2 ,
Pushmeet Kohli3 , Joshua B. Tenenbaum4
1,2,4
Massachusetts Institute of Technology, Cambridge, USA
3
Microsoft Research, Cambridge, UK
1
2
tejask@mit.edu wwhitney@mit.edu 3 pkohli@microsoft.com 4 jbt@mit.edu
* First two authors co... | 5851 |@word kohli:2 seems:2 open:1 out1:2 thereby:1 configuration:1 contains:2 efficacy:2 series:1 liu:1 interestingly:3 existing:1 com:1 z2:5 surprising:1 cad:3 must:1 unpooling:3 happen:1 shape:12 designed:2 interpretable:8 plot:1 generative:6 selected:2 guess:1 intelligence:2 accordingly:1 plane:2 parametrization:1 ... |
5,360 | 5,852 | Learning to Segment Object Candidates
Pedro O. Pinheiro?
Ronan Collobert
Piotr Doll?ar
pedro@opinheiro.com
locronan@fb.com
pdollar@fb.com
Facebook AI Research
Abstract
Recent object detection systems rely on two critical steps: (1) a set of object proposals is predicted as efficiently as possible, and (2) this s... | 5852 |@word cnn:8 version:2 eliminating:1 dalal:1 kokkinos:1 everingham:1 triggs:1 tried:1 rgb:2 shot:1 reduction:1 liu:1 contains:7 score:21 ecole:1 ours:1 tuned:1 document:1 outperforms:3 existing:2 current:1 com:3 surprising:1 must:2 gpu:2 ronan:1 informative:1 remove:1 designed:2 drop:2 aside:1 cue:1 fewer:2 select... |
5,361 | 5,853 | The Return of the Gating Network:
Combining Generative Models and Discriminative
Training in Natural Image Priors
Yair Weiss
School of Computer Science and Engineering
Hebrew University of Jerusalem
Dan Rosenbaum
School of Computer Science and Engineering
Hebrew University of Jerusalem
Abstract
In recent years, appr... | 5853 |@word kohli:1 version:1 middle:1 compression:1 d2:3 seek:4 covariance:5 simplifying:2 jacob:1 eng:1 dramatic:1 generatively:3 score:1 daniel:2 outperforms:1 existing:1 current:2 comparing:1 michal:1 nowlan:1 rnade:1 uria:1 numerical:1 blur:4 christian:2 remove:2 v:3 alone:1 generative:27 leaf:1 guess:2 short:1 do... |
5,362 | 5,854 | Spatial Transformer Networks
Max Jaderberg
Karen Simonyan
Andrew Zisserman
Koray Kavukcuoglu
Google DeepMind, London, UK
{jaderberg,simonyan,zisserman,korayk}@google.com
Abstract
Convolutional Neural Networks define an exceptionally powerful class of models,
but are still limited by the lack of ability to be spat... | 5854 |@word deformed:1 cnn:56 version:2 determinant:1 crucially:1 contraction:1 jacob:1 attended:1 sgd:2 moment:1 contains:2 tuned:1 ours:2 interestingly:2 document:1 outperforms:1 existing:1 com:1 activation:3 assigning:1 must:2 written:1 gpu:1 subsequent:7 informative:1 shape:2 localise:1 generative:5 discovering:1 r... |
5,363 | 5,855 | A Reduced-Dimension fMRI Shared Response Model
Po-Hsuan Chen1 , Janice Chen2 , Yaara Yeshurun2 ,
Uri Hasson2 , James V. Haxby3 , Peter J. Ramadge1
1
Department of Electrical Engineering, Princeton University
2
Princeton Neuroscience Institute and Department of Psychology, Princeton University
3
Department of Psycholog... | 5855 |@word version:2 mri:2 loading:3 stronger:1 norm:2 open:2 hyv:1 seek:1 covariance:2 tr:9 reduction:3 moment:1 plentiful:1 series:7 initial:2 selecting:2 existing:2 contextual:1 si:2 must:1 john:1 subsequent:2 concatenate:1 blur:1 informative:5 haxby:6 remove:4 plot:4 atlas:2 update:3 medial:1 v:2 implying:1 genera... |
5,364 | 5,856 | Attractor Network Dynamics Enable Preplay and
Rapid Path Planning in Maze?like Environments
Wulfram Gerstner
Laboratory of Computational Neuroscience
?
Ecole
Polytechnique F?ed?erale de Lausanne
CH-1015 Lausanne, Switzerland
wulfram.gerstner@epfl.ch
Dane Corneil
Laboratory of Computational Neuroscience
?
Ecole
Polyte... | 5856 |@word trial:1 version:1 hippocampus:18 grey:3 d2:1 simulation:1 shot:1 reduction:1 initial:5 ecole:2 past:1 current:4 recovered:4 activation:3 scatter:2 yet:1 must:1 john:3 ronald:1 distant:4 plasticity:5 christian:1 plot:4 update:2 depict:1 v:1 selected:2 plane:1 reappears:2 short:2 supplying:1 lr:1 provides:3 c... |
5,365 | 5,857 | Inferring Algorithmic Patterns with
Stack-Augmented Recurrent Nets
Tomas Mikolov
Facebook AI Research
770 Broadway, New York, USA.
tmikolov@fb.com
Armand Joulin
Facebook AI Research
770 Broadway, New York, USA.
ajoulin@fb.com
Abstract
Despite the recent achievements in machine learning, we are still very far from
ac... | 5857 |@word armand:1 version:1 seems:2 proportion:1 grey:1 bn:7 sgd:3 harder:2 carry:3 initial:1 ours:2 interestingly:2 crocker:1 document:2 past:2 existing:2 current:7 com:3 discretization:1 activation:3 must:1 written:1 parsing:1 numerical:1 concatenate:1 remove:2 designed:2 update:3 intelligence:2 selected:1 vanishi... |
5,366 | 5,858 | Decoupled Deep Neural Network for
Semi-supervised Semantic Segmentation
Seunghoon Hong? Hyeonwoo Noh? Bohyung Han
Dept. of Computer Science and Engineering, POSTECH, Pohang, Korea
{maga33,hyeonwoonoh ,bhhan}@postech.ac.kr
Abstract
We propose a novel deep neural network architecture for semi-supervised semantic segmen... | 5858 |@word cnn:3 briefly:1 advantageous:1 kokkinos:1 everingham:1 paredes:1 propagate:2 sgd:2 electronics:1 configuration:3 series:1 score:6 contains:2 deconvolutional:1 romera:1 outperforms:4 existing:4 guadarrama:1 nt:2 activation:22 yet:1 written:1 gpu:1 john:1 unpooling:3 concatenate:1 ronan:1 shape:2 enables:2 up... |
5,367 | 5,859 | Action-Conditional Video Prediction
using Deep Networks in Atari Games
Junhyuk Oh
Xiaoxiao Guo Honglak Lee Richard Lewis
Satinder Singh
University of Michigan, Ann Arbor, MI 48109, USA
{junhyuk,guoxiao,honglak,rickl,baveja}@umich.edu
Abstract
Motivated by vision-based reinforcement learning (RL) problems, in particu... | 5859 |@word cnn:11 nd:1 bptt:1 r:1 rgb:1 recursively:1 initial:1 liu:1 score:7 tuned:1 interestingly:1 ati:1 guadarrama:1 com:1 realistic:2 happen:1 shape:1 enables:1 hypothesize:1 generative:1 greedy:5 website:1 intelligence:1 reappears:1 talvitie:1 short:3 sudden:1 location:3 zhang:1 five:3 wierstra:1 along:1 corrido... |
5,368 | 586 | ANN Based Classification for Heart Defibrillators
M. Jabri, S. Pickard, P. Leong, Z. Chi, B. Flower, and Y. Xie
Sydney University Electrical Engineering
NSW 2006 Australia
Abstract
Current Intra-Cardia defibrillators make use of simple classification algorithms to determine patient conditions and subsequently to enab... | 586 |@word briefly:1 version:1 judgement:1 nsw:1 necessity:1 born:1 current:1 icds:3 discrimination:1 half:1 device:1 indicative:1 behavior:1 arrhythmia:19 multi:11 morphology:3 chi:6 what:2 fuzzy:1 developed:3 fabricated:2 perfonn:2 vtf:2 subclass:1 classifier:26 uk:1 positive:1 engineering:1 referenced:1 svt:5 timing... |
5,369 | 5,860 | On-the-Job Learning with Bayesian Decision Theory
Keenon Werling
Department of Computer Science
Stanford University
keenon@cs.stanford.edu
Arun Chaganty
Department of Computer Science
Stanford University
chaganty@cs.stanford.edu
Percy Liang
Department of Computer Science
Stanford University
pliang@cs.stanford.edu
C... | 5860 |@word private:1 exploitation:1 repository:1 pw:1 stronger:1 johansson:1 open:1 seek:2 simulation:1 q1:9 paid:2 asks:2 rj0:3 reduction:4 loc:7 contains:1 score:1 karger:2 daniel:1 document:1 prefix:1 outperforms:2 existing:2 current:6 com:2 comparing:3 si:3 yet:1 chu:1 must:4 subsequent:1 distant:2 informative:2 m... |
5,370 | 5,861 | Learning Wake-Sleep Recurrent Attention Models
Jimmy Ba
University of Toronto
Roger Grosse
University of Toronto
jimmy@psi.toronto.edu
rgrosse@cs.toronto.edu
Ruslan Salakhutdinov
University of Toronto
Brendan Frey
University of Toronto
rsalskhu@cs.toronto.edu
frey@psi.toronto.edu
Abstract
Despite their success... | 5861 |@word middle:1 version:3 thereby:1 series:1 score:2 selecting:1 punishes:1 foveal:1 document:1 past:1 freitas:1 err:1 must:4 realistic:1 subsequent:1 informative:2 update:9 generative:13 intelligence:4 es:3 core:1 blei:1 coarse:1 contribute:1 toronto:8 location:15 wierstra:2 combine:1 inside:1 introduce:1 acquire... |
5,371 | 5,862 | Backpropagation for
Energy-Efficient Neuromorphic Computing
Steve K. Esser
IBM Research?Almaden
650 Harry Road, San Jose, CA 95120
sesser@us.ibm.com
Rathinakumar Appuswamy
IBM Research?Almaden
650 Harry Road, San Jose, CA 95120
rappusw@us.ibm.com
Paul A. Merolla
IBM Research?Almaden
650 Harry Road, San Jose, CA 9512... | 5862 |@word schmuker:1 approved:1 cm2:1 simulation:2 covariance:4 contrastive:1 thereby:1 versatile:1 solid:1 wellapproximated:1 reduction:1 configuration:3 liu:2 document:1 trinary:3 existing:1 com:5 comparing:1 discretization:2 activation:1 assigning:1 must:2 john:1 subsequent:1 shape:1 cqr:3 designed:3 progressively... |
5,372 | 5,863 | A Tractable Approximation to Optimal Point Process
Filtering: Application to Neural Encoding
Yuval Harel, Ron Meir
Department of Electrical Engineering
Technion ? Israel Institute of Technology
Technion City, Haifa, Israel
{yharel@tx,rmeir@ee}.technion.ac.il
Manfred Opper
Department of Artificial Intelligence
Technic... | 5863 |@word trial:5 open:2 simulation:2 seek:1 covariance:2 eng:2 fifteen:1 solid:1 reduction:3 moment:7 celebrated:1 series:3 initial:1 interestingly:1 outperforms:1 current:3 discretization:2 nt:12 surprising:1 marquardt:1 dx:1 readily:1 numerical:6 informative:8 analytic:7 enables:1 plot:2 drop:2 update:3 v:1 implyi... |
5,373 | 5,864 | Color Constancy by Learning to Predict
Chromaticity from Luminance
Ayan Chakrabarti
Toyota Technological Institute at Chicago
6045 S. Kenwood Ave., Chicago, IL 60637
ayanc@ttic.edu
Abstract
Color constancy is the recovery of true surface color from observed color, and
requires estimating the chromaticity of scene ill... | 5864 |@word cnn:1 middle:1 version:8 hu:1 km:3 seek:1 decomposition:1 brightness:3 harder:1 shading:4 carry:1 reduction:1 contains:4 disparity:1 interestingly:1 franklin:1 outperforms:1 kmk:1 current:4 recovered:1 com:1 chicago:2 partition:1 informative:2 visible:1 hypothesize:1 update:2 cue:1 core:1 quantized:2 revisi... |
5,374 | 5,865 | Efficient Exact Gradient Update for training Deep
Networks with Very Large Sparse Targets
Pascal Vincent? , Alexandre de Br?bisson, Xavier Bouthillier
D?partement d?Informatique et de Recherche Op?rationnelle
Universit? de Montr?al, Montr?al, Qu?bec, CANADA
?
and CIFAR
Abstract
An important class of problems involves ... | 5865 |@word version:5 briefly:1 manageable:1 seems:1 norm:1 open:1 d2:31 heuristically:3 tried:1 contrastive:4 incurs:4 initial:1 contains:2 score:4 o2:1 current:1 com:2 activation:4 tackling:1 yet:1 written:2 must:1 gpu:20 realize:1 subsequent:1 numerical:3 cheap:2 plot:1 update:48 intelligence:3 prohibitive:12 fewer:... |
5,375 | 5,866 | Pointer Networks
Oriol Vinyals?
Google Brain
Meire Fortunato?
Department of Mathematics, UC Berkeley
Navdeep Jaitly
Google Brain
Abstract
We introduce a new neural architecture to learn the conditional probability of an
output sequence with elements that are discrete tokens corresponding to positions
in an input seq... | 5866 |@word middle:1 stronger:1 seems:1 termination:1 propagate:1 excited:1 tr:8 reduction:1 daniel:1 document:1 interestingly:1 outperforms:1 guadarrama:1 contextual:1 com:3 anne:1 activation:1 must:1 parsing:1 mesh:1 ronald:2 update:1 n0:2 alone:1 generative:2 half:1 imitate:1 ivo:1 plane:2 beginning:3 core:1 short:2... |
5,376 | 5,867 | Precision-Recall-Gain Curves:
PR Analysis Done Right
Meelis Kull
Intelligent Systems Laboratory
University of Bristol, United Kingdom
Meelis.Kull@bristol.ac.uk
Peter A. Flach
Intelligent Systems Laboratory
University of Bristol, United Kingdom
Peter.Flach@bristol.ac.uk
Abstract
Precision-Recall analysis abounds in a... | 5867 |@word middle:1 version:4 achievable:2 proportion:5 flach:7 suitably:1 nd:1 methodologically:2 thereby:1 mention:1 solid:4 score:49 united:2 current:1 assigning:1 fn:8 plot:13 v:3 half:1 selected:2 inspection:3 fpr:6 reciprocal:1 location:1 constructed:2 c2:4 become:1 combine:1 pairwise:1 aupr:20 indeed:1 expected... |
5,377 | 5,868 | NEXT: A System for Real-World Development,
Evaluation, and Application of Active Learning
Kevin Jamieson
UC Berkeley
Lalit Jain, Chris Fernandez, Nick Glattard, Robert Nowak
University of Wisconsin - Madison
kjamieson@berkeley.edu
{ljain,crfernandez,glattard,rdnowak}@wisc.edu
Abstract
Active learning methods automa... | 5868 |@word trial:1 repository:1 judgement:2 polynomial:1 norm:2 replicate:3 nd:1 open:4 instruction:1 vldb:1 atul:1 accounting:1 fabrice:1 pick:1 yorker:5 shading:1 versatile:1 reduction:1 wrapper:1 liu:1 initial:1 score:6 selecting:5 series:1 daniel:2 configuration:1 past:3 current:1 comparing:2 contextual:3 com:4 ye... |
5,378 | 5,869 | Structured Transforms for
Small-Footprint Deep Learning
Vikas Sindhwani
Tara N. Sainath
Sanjiv Kumar
Google, New York
{sindhwani, tsainath, sanjivk}@google.com
Abstract
We consider the task of building compact deep learning pipelines suitable for deployment on storage and power constrained mobile devices. We propose... | 5869 |@word kohli:1 version:1 inversion:2 polynomial:2 compression:1 replicate:1 carolina:1 decomposition:2 sgd:1 dramatic:1 configuration:2 lightweight:1 series:1 contains:2 tuned:1 outperforms:1 existing:3 freitas:2 com:1 yet:1 written:2 devin:1 sanjiv:1 numerical:2 drop:1 plot:1 moczulski:1 hash:1 prohibitive:1 devi... |
5,379 | 587 | Using Prior Knowledge in a NNPDA to Learn
Context-Free Languages
Sreerupa Das
Dept. of Compo Sc. &
Inst. of Cognitive Sc.
University of Colorado
Boulder, CO 80309
c.
Lee Giles?
NEC Research Inst.
4 Independence Way
Princeton, NJ 08540
Guo-Zheng SUD
"'lnst. for Adv. Compo Studies
University of Maryland
College Park,... | 587 |@word version:1 simulation:2 propagate:1 accommodate:1 initial:5 contains:1 prefix:2 current:3 activation:9 assigning:1 must:2 predetermined:1 plot:1 intelligence:2 selected:1 short:1 compo:2 provides:2 node:1 simpler:1 incorrect:2 consists:2 embody:1 elman:2 sud:1 increasing:1 becomes:1 provided:3 matched:1 what:... |
5,380 | 5,870 | Equilibrated adaptive learning rates for non-convex
optimization
Harm de Vries1
Universit?e de Montr?eal
devries@iro.umontreal.ca
Yann N. Dauphin1
Universit?e de Montr?eal
dauphiya@iro.umontreal.ca
Yoshua Bengio
Universit?e de Montr?eal
yoshua.bengio@umontreal.ca
Abstract
Parameter-specific adaptive learning rate m... | 5870 |@word norm:6 seems:1 nd:1 open:1 seek:2 simplifying:1 sgd:17 arous:1 reduction:3 daniel:1 interestingly:2 outperforms:3 bradley:5 recovered:1 comparing:1 gpu:1 john:1 realize:1 numerical:4 confirming:1 drop:1 update:7 implying:1 prohibitive:2 beginning:1 provides:2 pascanu:4 contribute:1 sigmoidal:1 org:2 along:2... |
5,381 | 5,871 | Bayesian Active Model Selection
with an Application to Automated Audiometry
Jacob R. Gardner
CS, Cornell University
Ithaca, NY 14850
jrg365@cornell.edu
Kilian Q. Weinberger
CS, Cornell University
Ithaca, NY 14850
kqw4@cornell.edu
Gustavo Malkomes
CSE, WUSTL
St. Louis, MO 63130
luizgustavo@wustl.edu
Dennis Barbour
BME... | 5871 |@word trial:4 retraining:4 laryngology:1 seek:1 simulation:1 jacob:1 covariance:6 accounting:2 prominence:1 paid:1 incurs:2 initial:1 series:2 occupational:2 united:1 selecting:4 existing:1 current:1 comparing:1 must:2 john:1 concatenate:1 informative:1 shape:1 enables:1 lengthen:1 noninformative:1 analytic:2 plo... |
5,382 | 5,872 | Efficient and Robust Automated Machine Learning
Matthias Feurer
Aaron Klein
Katharina Eggensperger
Jost Tobias Springenberg
Manuel Blum
Frank Hutter
Department of Computer Science
University of Freiburg, Germany
{feurerm,kleinaa,eggenspk,springj,mblum,fh}@cs.uni-freiburg.de
Abstract
The success of machine learning in... | 5872 |@word h:2 madelon:1 exploitation:1 repository:4 eliminating:1 polynomial:3 version:3 proportion:1 open:1 grey:1 hu:2 crucially:1 decomposition:1 xtest:1 sgd:2 automl:42 configuration:14 selecting:2 tuned:1 interestingly:1 dubourg:1 past:1 existing:1 outperforms:2 current:1 com:6 comparing:1 manuel:1 freitas:1 yet... |
5,383 | 5,873 | A Framework for Individualizing Predictions of Disease
Trajectories by Exploiting Multi-Resolution Structure
Suchi Saria
Dept. of Computer Science
Johns Hopkins University
Baltimore, MD 21218
ssaria@cs.jhu.edu
Peter Schulam
Dept. of Computer Science
Johns Hopkins University
Baltimore, MD 21218
pschulam@jhu.edu
Abstr... | 5873 |@word adomavicius:1 version:3 polynomial:2 seems:1 yi0:2 covariance:7 accounting:1 reduction:1 initial:1 liu:1 series:3 contains:3 genetic:1 ours:1 longitudinal:5 current:1 recovered:1 comparing:1 must:1 written:2 john:3 partition:1 tailoring:1 drop:3 treating:1 update:3 plot:7 designed:1 alone:3 generative:1 few... |
5,384 | 5,874 | Gaussian Process Random Fields
David A. Moore and Stuart J. Russell
Computer Science Division
University of California, Berkeley
Berkeley, CA 94709
{dmoore, russell}@cs.berkeley.edu
Abstract
Gaussian processes have been successful in both supervised and unsupervised
machine learning tasks, but their computational comp... | 5874 |@word determinant:1 eliminating:1 km:6 vanhatalo:1 covariance:14 decomposition:3 contraction:2 accounting:1 tr:2 igp:1 reduction:2 initial:3 liu:1 contains:1 united:1 existing:1 current:2 recovered:1 com:1 assigning:1 must:1 hou:1 partition:13 pseudomarginals:1 concert:1 v:2 alone:1 intelligence:10 prohibitive:1 ... |
5,385 | 5,875 | MCMC for Variationally Sparse Gaussian Processes
James Hensman
CHICAS, Lancaster University
james.hensman@lancaster.ac.uk
Maurizio Filippone
EURECOM
maurizio.filippone@eurecom.fr
Alexander G. de G. Matthews
University of Cambridge
am554@cam.ac.uk
Zoubin Ghahramani
University of Cambridge
zoubin@cam.ac.uk
Abstract
Ga... | 5875 |@word cox:3 middle:1 inversion:3 seems:2 replicate:1 logit:2 suitably:1 vanhatalo:1 covariance:32 decomposition:1 reduction:1 initial:1 contains:2 series:1 kuf:3 tuned:7 ours:1 existing:2 freitas:1 current:1 com:1 elliptical:2 recovered:1 arkk:2 fn:5 informative:1 shape:1 dupont:1 plot:2 aside:1 parameterization:... |
5,386 | 5,876 | Streaming, Distributed Variational Inference for
Bayesian Nonparametrics
Trevor Campbell1
Julian Straub2 John W. Fisher III2 Jonathan P. How1
1
LIDS, 2 CSAIL, MIT
{tdjc@ , jstraub@csail. , fisher@csail. , jhow@}mit.edu
Abstract
This paper presents a methodology for creating streaming, distributed inference algorithm... | 5876 |@word trial:2 polynomial:1 advantageous:1 km:26 crucially:1 decomposition:10 accounting:1 thereby:1 tr:1 series:1 score:3 united:1 denoting:1 document:2 past:1 current:3 com:2 yet:1 must:1 written:1 john:4 numerical:2 partition:3 concert:1 update:15 intelligence:4 fewer:1 yr:1 discovering:1 plane:2 core:1 filtere... |
5,387 | 5,877 | Fixed-Length Poisson MRF:
Adding Dependencies to the Multinomial
David I. Inouye
Pradeep Ravikumar
Inderjit S. Dhillon
Department of Computer Science
University of Texas at Austin
{dinouye,pradeepr,inderjit}@cs.utexas.edu
Abstract
We propose a novel distribution that generalizes the Multinomial distribution to
enabl... | 5877 |@word trial:1 middle:1 briefly:1 seems:2 plsa:1 open:2 seek:3 tried:1 pressure:1 moment:1 liu:2 contains:2 series:2 zij:6 united:1 document:26 suppressing:1 reynolds:1 outperforms:6 reaction:3 yet:1 assigning:1 must:1 partition:23 hofmann:1 remove:1 update:1 zik:1 intelligence:1 leaf:1 mccallum:1 core:1 blei:4 pr... |
5,388 | 5,878 | Human Memory Search as Initial-Visit Emitting
Random Walk
?
Kwang-Sung Jun? , Xiaojin Zhu? , Timothy Rogers?
Wisconsin Institute for Discovery, ? Department of Computer Sciences, ? Department of Psychology
University of Wisconsin-Madison
kjun@discovery.wisc.edu, jerryzhu@cs.wisc.edu, ttrogers@wisc.edu
Ming Yuan
Depar... | 5878 |@word version:1 inversion:1 polynomial:2 norm:3 nd:1 mehta:1 lobe:2 decomposition:1 p0:3 paulsen:1 pick:1 mammal:1 thereby:1 initial:9 series:3 contains:3 prefix:14 longitudinal:3 outperforms:4 existing:1 past:1 current:2 optim:1 loglik:1 yet:1 must:6 written:1 happen:1 confirming:1 kdd:1 remove:1 plot:2 unintell... |
5,389 | 5,879 | Spectral Learning of Large Structured HMMs for
Comparative Epigenomics
Chicheng Zhang
UC San Diego
chz038@eng.ucsd.edu
Jimin Song
Rutgers University
song@dls.rutgers.edu
Kevin C Chen
Rutgers University
kcchen@dls.rutgers.edu
Kamalika Chaudhuri
UC San Diego
kamalika@eng.ucsd.edu
Abstract
We develop a latent variable... | 5879 |@word mild:2 version:6 nd:11 hu:52 eng:2 decomposition:7 contrastive:2 simplifying:1 jacob:1 eld:1 reduction:1 moment:4 initial:2 score:3 daniel:2 existing:1 current:2 recovered:4 k562:2 partition:1 j1:4 cant:2 remove:1 designed:2 hypothesize:1 v:1 leaf:3 discovering:1 histone:1 cult:2 yuanfeng:1 parkes:1 provide... |
5,390 | 588 | An Analog VLSI Chip for Radial Basis Functions
.lohn C. Platt
Synaptics, Inc.
2698 Orchard Parkway
San Jose, CA 95134
J aneen Anderson
David B. Kirk'"
Abstract
We have designed, fabricated, and tested an analog VLSI chip
which computes radial basis functions in parallel. We have developed a synapse circuit that app... | 588 |@word cox:1 version:4 agf:1 middle:2 simulation:3 solid:1 tuned:1 current:7 yet:1 follower:6 must:2 john:1 partition:5 girosi:1 designed:1 device:1 core:2 height:1 mathematical:1 ik:1 introduce:1 roughly:1 dist:1 aliasing:1 multi:1 linearity:6 matched:1 circuit:6 aliased:1 vref:1 fuzzy:2 developed:1 differing:1 fa... |
5,391 | 5,880 | A Structural Smoothing Framework For Robust
Graph-Comparison
S.V.N. Vishwanathan
Department of Computer Science
University of California
Santa Cruz, CA, 95064, USA
vishy@ucsc.edu
Pinar Yanardag
Department of Computer Science
Purdue University
West Lafayette, IN, 47906, USA
ypinar@purdue.edu
Abstract
In this paper, w... | 5880 |@word kgk:1 version:3 briefly:1 kondor:2 proportion:2 flach:1 open:1 p0:1 pg:6 thereby:1 recursively:3 contains:2 tuned:1 ours:2 existing:2 current:1 com:1 written:1 cruz:2 mutagenic:2 subsequent:1 kdd:1 moreno:1 designed:1 graphlets:10 v:1 leaf:1 selected:1 item:1 tertiary:1 colored:1 iterates:1 multiset:5 node:... |
5,392 | 5,881 | Optimization Monte Carlo: Efficient and
Embarrassingly Parallel Likelihood-Free Inference
Max Welling?
Informatics Institute
University of Amsterdam
welling.max@gmail.com
Edward Meeds
Informatics Institute
University of Amsterdam
tmeeds@gmail.com
Abstract
We describe an embarrassingly parallel, anytime Monte Carlo m... | 5881 |@word briefly:1 version:2 prangle:1 grey:1 simulation:14 crucially:1 accounting:2 pick:1 solid:1 outlook:1 moment:1 initial:2 inefficiency:1 liu:1 series:3 selecting:1 sobol:2 com:3 gmail:2 dx:3 must:1 ust:1 kdd:1 cheap:1 treating:1 drop:4 plot:2 update:2 half:2 fewer:1 es:12 hamiltonian:2 core:1 indefinitely:1 d... |
5,393 | 5,882 | Inverse Reinforcement Learning with Locally
Consistent Reward Functions
Quoc Phong Nguyen? , Kian Hsiang Low? , and Patrick Jaillet?
Dept. of Computer Science, National University of Singapore, Republic of Singapore?
Dept. of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, USA?
{qpho... | 5882 |@word briefly:1 open:1 covariance:1 pick:1 incurs:1 tr:1 solid:1 reduction:1 initial:4 contains:3 denoting:1 interestingly:3 outperforms:4 existing:4 current:1 refines:1 realistic:2 partition:8 remove:1 update:1 v:4 selected:5 amir:1 authority:1 traverse:1 marivate:1 deactivating:1 five:1 along:3 driver:8 koltun:... |
5,394 | 5,883 | Consistent Multilabel Classification
Oluwasanmi Koyejo?
Department of Psychology,
Stanford University
sanmi@stanford.edu
Nagarajan Natarajan?
Department of Computer Science,
University of Texas at Austin
naga86@cs.utexas.edu
Pradeep Ravikumar
Department of Computer Science,
University of Texas at Austin
pradeepr@cs.u... | 5883 |@word version:1 briefly:1 nd:1 c0:3 open:2 d2:5 thres:11 raajay:1 exclusively:1 tuned:1 ours:1 existing:4 comparing:1 surprising:1 b01:3 written:3 dx:1 conforming:1 fn:17 drop:1 plot:3 designed:1 intelligence:1 selected:2 characterization:1 five:1 mathematical:1 constructed:5 c2:4 initiative:1 prove:1 consists:1 ... |
5,395 | 5,884 | Is Approval Voting Optimal Given Approval Votes?
Nisarg Shah
Computer Science Department
Carnegie Mellon University
nkshah@cs.cmu.edu
Ariel D. Procaccia
Computer Science Department
Carnegie Mellon University
arielpro@cs.cmu.edu
Abstract
Some crowdsourcing platforms ask workers to express their opinions by approving ... | 5884 |@word mild:2 eliminating:1 polynomial:1 seems:3 approved:15 stronger:1 nd:2 open:1 d2:2 confirms:1 simulation:3 simplifying:1 q1:1 mention:1 moment:1 contains:1 score:4 selecting:1 com:1 must:2 chicago:1 partition:1 nisarg:1 realistic:1 intelligence:1 selected:2 farther:1 parkes:3 provides:1 characterization:1 bi... |
5,396 | 5,885 | A Normative Theory of Adaptive Dimensionality
Reduction in Neural Networks
Cengiz Pehlevan
Simons Center for Data Analysis
Simons Foundation
New York, NY 10010
cpehlevan@simonsfoundation.org
Dmitri B. Chklovskii
Simons Center for Data Analysis
Simons Foundation
New York, NY 10010
dchklovskii@simonsfoundation.org
Abst... | 5885 |@word h:1 version:1 inversion:1 norm:2 kriegeskorte:1 heuristically:1 hu:3 simulation:3 covariance:20 decomposition:3 decorrelate:1 twolayer:1 tr:3 solid:1 recursively:2 reduction:12 contains:1 precluding:1 interestingly:3 past:1 existing:2 current:1 comparing:1 must:3 john:1 multineuron:3 numerical:3 informative... |
5,397 | 5,886 | Efficient Non-greedy Optimization of Decision Trees
Mohammad Norouzi1?
Maxwell D. Collins2 ?
Matthew Johnson3
4
5
David J. Fleet
Pushmeet Kohli
1,4
Department of Computer Science, University of Toronto
2
Department of Computer Science, University of Wisconsin-Madison
3,5
Microsoft Research
Abstract
Decision trees and... | 5886 |@word kohli:3 determinant:1 norm:8 d2:6 gradual:1 jacob:1 pick:2 sgd:15 thereby:1 initial:2 configuration:2 score:4 selecting:1 tuned:1 si:7 must:1 john:1 subsequent:1 numerical:1 hofmann:1 enables:1 update:8 hash:1 greedy:33 leaf:40 selected:3 fewer:1 cook:1 accordingly:2 beginning:1 ith:1 oblique:6 core:1 num:1... |
5,398 | 5,887 | Statistical Topological Data Analysis ?
A Kernel Perspective
Stefan Huber
IST Austria
stefan.huber@ist.ac.at
Roland Kwitt
Department of Computer Science
University of Salzburg
rkwitt@gmx.at
Marc Niethammer
Department of Computer Science and BRIC
UNC Chapel Hill
mn@cs.unc.edu
Weili Lin
Department of Radiology and BRI... | 5887 |@word mild:3 version:1 briefly:2 nchen:1 stronger:1 norm:3 mri:1 open:1 q1:2 concise:2 boundedness:2 configuration:1 contains:2 series:3 score:1 denoting:1 rkhs:8 bootstrapped:1 longitudinal:3 past:1 existing:1 com:1 universality:3 written:1 readily:1 mesh:5 numerical:2 fn:3 informative:1 shape:6 enables:3 analyt... |
5,399 | 5,888 | Variational Consensus Monte Carlo
Maxim Rabinovich, Elaine Angelino, and Michael I. Jordan
Computer Science Division
University of California, Berkeley
{rabinovich, elaine, jordan}@eecs.berkeley.edu
Abstract
Practitioners of Bayesian statistics have long depended on Markov chain Monte
Carlo (MCMC) to obtain samples fr... | 5888 |@word mild:1 polynomial:1 replicate:1 nd:1 open:2 willing:1 hu:2 crucially:2 covariance:6 sgd:2 thereby:1 accommodate:1 moment:12 reduction:3 initial:1 contains:1 series:1 denoting:2 outperforms:1 elliptical:1 must:2 belmont:1 partition:13 remove:1 designed:1 update:1 spec:1 greedy:1 intelligence:3 hamiltonian:1 ... |
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