Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2502.06327	Prompt-Driven Continual Graph Learning	Continual Graph Learning (CGL), which aims to accommodate new tasks over evolving graph data without forgetting prior knowledge, is garnering significant research interest. Mainstream solutions adopt the memory replay-based idea, ie, caching representative data from earlier tasks for retraining the graph model. However...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	532,035
2404.10193	Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering	The goal of selective prediction is to allow an a model to abstain when it may not be able to deliver a reliable prediction, which is important in safety-critical contexts. Existing approaches to selective prediction typically require access to the internals of a model, require retraining a model or study only unimodal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	446,987
2303.16333	Flow supervision for Deformable NeRF	In this paper we present a new method for deformable NeRF that can directly use optical flow as supervision. We overcome the major challenge with respect to the computationally inefficiency of enforcing the flow constraints to the backward deformation field, used by deformable NeRFs. Specifically, we show that invertin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	354,821
2207.00670	DRESS: Dynamic REal-time Sparse Subnets	The limited and dynamically varied resources on edge devices motivate us to deploy an optimized deep neural network that can adapt its sub-networks to fit in different resource constraints. However, existing works often build sub-networks through searching different network architectures in a hand-crafted sampling spac...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	305,837
2107.11990	Augmentation Pathways Network for Visual Recognition	Data augmentation is practically helpful for visual recognition, especially at the time of data scarcity. However, such success is only limited to quite a few light augmentations (e.g., random crop, flip). Heavy augmentations are either unstable or show adverse effects during training, owing to the big gap between the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	247,764
2012.05414	Rewriter-Evaluator Architecture for Neural Machine Translation	Encoder-decoder has been widely used in neural machine translation (NMT). A few methods have been proposed to improve it with multiple passes of decoding. However, their full potential is limited by a lack of appropriate termination policies. To address this issue, we present a novel architecture, Rewriter-Evaluator. I...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	210,770
2502.10581	Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective	As large language models have evolved, it has become crucial to distinguish between process supervision and outcome supervision -- two key reinforcement learning approaches to complex reasoning tasks. While process supervision offers intuitive advantages for long-term credit assignment, the precise relationship between...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	533,950
1808.09023	Real-time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy	Vehicle-to-Pedestrian (V2P) communication can significantly improve pedestrian safety at a signalized intersection. It is unlikely that pedestrians will carry a low latency communication enabled device and activate a pedestrian safety application in their hand-held device all the time. Because of this limitation, multi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	106,088
1710.01347	Simple Cortex: A Model of Cells in the Sensory Nervous System	Neuroscience research has produced many theories and computational neural models of sensory nervous systems. Notwithstanding many different perspectives towards developing intelligent machines, artificial intelligence has ultimately been influenced by neuroscience. Therefore, this paper provides an introduction to biol...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	82,003
2106.00261	Exploring Dynamic Selection of Branch Expansion Orders for Code Generation	Due to the great potential in facilitating software development, code generation has attracted increasing attention recently. Generally, dominant models are Seq2Tree models, which convert the input natural language description into a sequence of tree-construction actions corresponding to the pre-order traversal of an A...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	238,056
1707.07835	Towards Semantic Query Segmentation	Query Segmentation is one of the critical components for understanding users' search intent in Information Retrieval tasks. It involves grouping tokens in the search query into meaningful phrases which help downstream tasks like search relevance and query understanding. In this paper, we propose a novel approach to seg...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	77,708
2212.03375	General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation	Estimating the probability of failure for complex real-world systems using high-fidelity computational models is often prohibitively expensive, especially when the probability is small. Exploiting low-fidelity models can make this process more feasible, but merging information from multiple low-fidelity and high-fideli...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	335,094
2101.06931	Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach	Deep learning models are the state-of-the-art methods for semantic point cloud segmentation, the success of which relies on the availability of large-scale annotated datasets. However, it can be extremely time-consuming and prohibitively expensive to compile such datasets. In this work, we propose an active learning ap...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	215,890
2207.06888	Distance Learner: Incorporating Manifold Prior to Model Training	The manifold hypothesis (real world data concentrates near low-dimensional manifolds) is suggested as the principle behind the effectiveness of machine learning algorithms in very high dimensional problems that are common in domains such as vision and speech. Multiple methods have been proposed to explicitly incorporat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	308,028
2208.10583	Improving Sample Efficiency in Evolutionary RL Using Off-Policy Ranking	Evolution Strategy (ES) is a powerful black-box optimization technique based on the idea of natural evolution. In each of its iterations, a key step entails ranking candidate solutions based on some fitness score. For an ES method in Reinforcement Learning (RL), this ranking step requires evaluating multiple policies. ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	314,113
2108.09918	Fluent: An AI Augmented Writing Tool for People who Stutter	Stuttering is a speech disorder which impacts the personal and professional lives of millions of people worldwide. To save themselves from stigma and discrimination, people who stutter (PWS) may adopt different strategies to conceal their stuttering. One of the common strategies is word substitution where an individual...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	251,747
2006.02683	Uncertainty quantification in medical image segmentation with normalizing flows	Medical image segmentation is inherently an ambiguous task due to factors such as partial volumes and variations in anatomical definitions. While in most cases the segmentation uncertainty is around the border of structures of interest, there can also be considerable inter-rater differences. The class of conditional va...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	180,113
1812.11031	Distributed Multi-Stream Beamforming in MIMO Multi-Relay Interference Networks	In this paper, multi-stream transmission in interference networks aided by multiple amplify-and-forward (AF) relays in the presence of direct links is considered. The objective is to minimize the sum power of transmitters and relays by beamforming optimization under the stream signal-to-interference-plus-noise-ratio (S...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	117,487
2311.16956	Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent	This paper proposes a novel approach to adaptive step sizes in stochastic gradient descent (SGD) by utilizing quantities that we have identified as numerically traceable -- the Lipschitz constant for gradients and a concept of the local variance in search directions. Our findings yield a nearly hyperparameter-free algo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	411,102
1910.06054	An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays	We propose a new algorithm for adversarial multi-armed bandits with unrestricted delays. The algorithm is based on a novel hybrid regularizer applied in the Follow the Regularized Leader (FTRL) framework. It achieves $\mathcal{O}(\sqrt{kn}+\sqrt{D\log(k)})$ regret guarantee, where $k$ is the number of arms, $n$ is the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	149,248
0901.3990	Du corpus au dictionnaire	In this article, we propose an automatic process to build multi-lingual lexico-semantic resources. The goal of these resources is to browse semantically textual information contained in texts of different languages. This method uses a mathematical model called Atlas s\'emantiques in order to represent the different sen...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	3,053
2110.03895	ALL-IN-ONE: Multi-Task Learning BERT models for Evaluating Peer Assessments	Peer assessment has been widely applied across diverse academic fields over the last few decades and has demonstrated its effectiveness. However, the advantages of peer assessment can only be achieved with high-quality peer reviews. Previous studies have found that high-quality review comments usually comprise several ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	259,671
1206.4633	Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online Learning	Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded number of support vectors, making it non-scalable and unsuitable for applications with large-scale datasets. In this work, we study the problem ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	16,684
2206.10559	Low Resource Pipeline for Spoken Language Understanding via Weak Supervision	In Weak Supervised Learning (WSL), a model is trained over noisy labels obtained from semantic rules and task-specific pre-trained models. Rules offer limited generalization over tasks and require significant manual efforts while pre-trained models are available only for limited tasks. In this work, we propose to utili...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	303,951
2305.00663	Activation Functions Not To Active: A Plausible Theory on Interpreting Neural Networks	Researchers commonly believe that neural networks model a high-dimensional space but cannot give a clear definition of this space. What is this space? What is its dimension? And does it has finite dimensions? In this paper, we develop a plausible theory on interpreting neural networks in terms of the role of activation...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	361,415
2402.05947	Separable Multi-Concept Erasure from Diffusion Models	Large-scale diffusion models, known for their impressive image generation capabilities, have raised concerns among researchers regarding social impacts, such as the imitation of copyrighted artistic styles. In response, existing approaches turn to machine unlearning techniques to eliminate unsafe concepts from pre-trai...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	428,077
2203.16152	Spline-Based Space-Time Finite Element Approach for Fluid-Structure Interaction Problems With a Focus on Fully Enclosed Domains	Non-Uniform Rational B-Spline (NURBS) surfaces are commonly used within Computer-Aided Design (CAD) tools to represent geometric objects. When using isogeometric analysis (IGA), it is possible to use such NURBS geometries for numerical analysis directly. Analyzing fluid flows, however, requires complex three-dimensiona...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	288,673
2106.12923	Understanding Modern Techniques in Optimization: Frank-Wolfe, Nesterov's Momentum, and Polyak's Momentum	In the first part of this dissertation research, we develop a modular framework that can serve as a recipe for constructing and analyzing iterative algorithms for convex optimization. Specifically, our work casts optimization as iteratively playing a two-player zero-sum game. Many existing optimization algorithms inclu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	242,889
1906.02924	PseudoEdgeNet: Nuclei Segmentation only with Point Annotations	Nuclei segmentation is one of the important tasks for whole slide image analysis in digital pathology. With the drastic advance of deep learning, recent deep networks have demonstrated successful performance of the nuclei segmentation task. However, a major bottleneck to achieving good performance is the cost for annot...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	134,228
2204.07932	Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning	While deep neural networks (DNNs) have strengthened the performance of cooperative multi-agent reinforcement learning (c-MARL), the agent policy can be easily perturbed by adversarial examples. Considering the safety critical applications of c-MARL, such as traffic management, power management and unmanned aerial vehic...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	291,904
2312.08975	On Mask-based Image Set Desensitization with Recognition Support	In recent years, Deep Neural Networks (DNN) have emerged as a practical method for image recognition. The raw data, which contain sensitive information, are generally exploited within the training process. However, when the training process is outsourced to a third-party organization, the raw data should be desensitize...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	415,540
2307.12058	Discovering Spatio-Temporal Rationales for Video Question Answering	This paper strives to solve complex video question answering (VideoQA) which features long video containing multiple objects and events at different time. To tackle the challenge, we highlight the importance of identifying question-critical temporal moments and spatial objects from the vast amount of video content. Tow...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	381,123
1904.05146	DeepSphere: towards an equivariant graph-based spherical CNN	Spherical data is found in many applications. By modeling the discretized sphere as a graph, we can accommodate non-uniformly distributed, partial, and changing samplings. Moreover, graph convolutions are computationally more efficient than spherical convolutions. As equivariance is desired to exploit rotational symmet...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	127,221
1909.09153	Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks	The deployment of machine learning algorithms on resource-constrained edge devices is an important challenge from both theoretical and applied points of view. In this article, we focus on resource-efficient randomly connected neural networks known as Random Vector Functional Link (RVFL) networks since their simple desi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,171
2409.17270	Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning	Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning, particularly in novel domains and complex logical sequences. This research introduces Proof of Thought, a framework that enhances the reliability and transparency of LLM outputs. Our approach bri...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	true	false	true	491,717
2408.05459	A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation	Attributed networks containing entity-specific information in node attributes are ubiquitous in modeling social networks, e-commerce, bioinformatics, etc. Their inherent network topology ranges from simple graphs to hypergraphs with high-order interactions and multiplex graphs with separate layers. An important graph m...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	479,799
2305.18402	Neural Sculpting: Uncovering hierarchically modular task structure in neural networks through pruning and network analysis	Natural target functions and tasks typically exhibit hierarchical modularity -- they can be broken down into simpler sub-functions that are organized in a hierarchy. Such sub-functions have two important features: they have a distinct set of inputs (input-separability) and they are reused as inputs higher in the hierar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	369,020
1811.01149	Predictive Deployment of UAV Base Stations in Wireless Networks: Machine Learning Meets Contract Theory	In this paper, a novel framework is proposed to enable a predictive deployment of unmanned aerial vehicles (UAVs) as temporary base stations (BSs) to complement ground cellular systems in face of downlink traffic overload. First, a novel learning approach, based on the weighted expectation maximization (WEM) algorithm,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	112,284
2010.03081	Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies	The coronavirus disease 2019 (COVID-19) pandemic has quickly become a global public health crisis unseen in recent years. It is known that the structure of the human contact network plays an important role in the spread of transmissible diseases. In this work, we study a structure aware model of COVID-19 CGEM. This mod...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	199,263
2208.03514	Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning	With the rapid development of artificial intelligence and autonomous driving technology, the demand for semiconductors is projected to rise substantially. However, the massive expansion of semiconductor manufacturing and the development of new technology will bring many defect wafers. If these defect wafers have not be...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	311,805
2206.05473	Comparative Snippet Generation	We model product reviews to generate comparative responses consisting of positive and negative experiences regarding the product. Specifically, we generate a single-sentence, comparative response from a given positive and a negative opinion. We contribute the first dataset for this task of Comparative Snippet Generatio...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	302,015
2004.01876	Modeling and Analysis of Networked Discrete Event Systems with Multiple Control Channels	In this paper, we propose a novel framework for modeling and analysis of networked discrete-event systems (DES). We assume that the plant is controlled by a feedback supervisor whose control decisions are subject to communication delays and losses. Furthermore, we consider a general setting where the supervisor sends c...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	171,041
2008.08624	Estimating the time-lapse between medical insurance reimbursement with non-parametric regression models	Non-parametric supervised learning algorithms represent a succinct class of supervised learning algorithms where the learning parameters are highly flexible and whose values are directly dependent on the size of the training data. In this paper, we comparatively study the properties of four nonparametric algorithms, K-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	192,464
2411.04535	Meta-Reasoning Improves Tool Use in Large Language Models	External tools help large language models succeed at tasks where they would otherwise typically fail. In existing frameworks, choosing tools at test time relies on naive greedy decoding, regardless of whether the model has been fine-tuned on tool-annotated data or prompted with in-context examples. In contrast, we find...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	506,307
2303.11897	TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering	Despite thousands of researchers, engineers, and artists actively working on improving text-to-image generation models, systems often fail to produce images that accurately align with the text inputs. We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,053
2208.12413	Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning	Parotid gland tumor is a common type of head and neck tumor. Segmentation of the parotid glands and tumors by MR images is important for the treatment of parotid gland tumors. However, segmentation of the parotid glands is particularly challenging due to their variable shape and low contrast with surrounding structures...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	314,717
2309.12815	Improving Generalization in Game Agents with Data Augmentation in Imitation Learning	Imitation learning is an effective approach for training game-playing agents and, consequently, for efficient game production. However, generalization - the ability to perform well in related but unseen scenarios - is an essential requirement that remains an unsolved challenge for game AI. Generalization is difficult f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	393,947
2407.12135	Trustworthy AI in practice: an analysis of practitioners' needs and challenges	Recently, there has been growing attention on behalf of both academic and practice communities towards the ability of Artificial Intelligence (AI) systems to operate responsibly and ethically. As a result, a plethora of frameworks and guidelines have appeared to support practitioners in implementing Trustworthy AI appl...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	true	473,776
2209.08554	Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions	Pruning is one of the predominant approaches for compressing deep neural networks (DNNs). Lately, coresets (provable data summarizations) were leveraged for pruning DNNs, adding the advantage of theoretical guarantees on the trade-off between the compression rate and the approximation error. However, coresets in this d...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	318,168
2303.16203	Your Diffusion Model is Secretly a Zero-Shot Classifier	The recent wave of large-scale text-to-image diffusion models has dramatically increased our text-based image generation abilities. These models can generate realistic images for a staggering variety of prompts and exhibit impressive compositional generalization abilities. Almost all use cases thus far have solely focu...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	true	false	false	354,781
2501.17555	An Exceptional Dataset For Rare Pancreatic Tumor Segmentation	Pancreatic NEuroendocrine Tumors (pNETs) are very rare endocrine neoplasms that account for less than 5% of all pancreatic malignancies, with an incidence of only 1-1.5 cases per 100,000. Early detection of pNETs is critical for improving patient survival, but the rarity of pNETs makes segmenting them from CT a very ch...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	528,371
2301.01838	PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time Series	Recent rapid development of sensor technology has allowed massive fine-grained time series (TS) data to be collected and set the foundation for the development of data-driven services and applications. During the process, data sharing is often involved to allow the third-party modelers to perform specific time series d...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	339,341
2304.12474	Design optimization for high-performance computing using FPGA	Reconfigurable architectures like Field Programmable Gate Arrays (FPGAs) have been used for accelerating computations in several domains because of their unique combination of flexibility, performance, and power efficiency. However, FPGAs have not been widely used for high-performance computing, primarily because of th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	360,223
1512.05417	Influence Prediction for Continuous-Time Information Propagation on Networks	We consider the problem of predicting the time evolution of influence, the expected number of activated nodes, given a set of initially active nodes on a propagation network. To address the significant computational challenges of this problem on large-scale heterogeneous networks, we establish a system of differential ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	50,222
1909.03947	Scheduling optimization of parallel linear algebra algorithms using Supervised Learning	Linear algebra algorithms are used widely in a variety of domains, e.g machine learning, numerical physics and video games graphics. For all these applications, loop-level parallelism is required to achieve high performance. However, finding the optimal way to schedule the workload between threads is a non-trivial prob...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	144,650
1903.10956	On the Influence of Bias-Correction on Distributed Stochastic Optimization	Various bias-correction methods such as EXTRA, gradient tracking methods, and exact diffusion have been proposed recently to solve distributed {\em deterministic} optimization problems. These methods employ constant step-sizes and converge linearly to the {\em exact} solution under proper conditions. However, their per...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	125,402
2308.13604	Network science Ising states of matter	Network science provides very powerful tools for extracting information from interacting data. Although recently the unsupervised detection of phases of matter using machine learning has raised significant interest, the full prediction power of network science has not yet been systematically explored in this context. H...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	387,985
2103.04235	Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation	Coronavirus Disease 2019 (COVID-19) has rapidly spread in 2020, emerging a mass of studies for lung infection segmentation from CT images. Though many methods have been proposed for this issue, it is a challenging task because of infections of various size appearing in different lobe zones. To tackle these issues, we p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	223,565
2002.05671	AI safety: state of the field through quantitative lens	Last decade has seen major improvements in the performance of artificial intelligence which has driven wide-spread applications. Unforeseen effects of such mass-adoption has put the notion of AI safety into the public eye. AI safety is a relatively new field of research focused on techniques for building AI beneficial ...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	163,971
1410.7852	A Markov Decision Process Analysis of the Cold Start Problem in Bayesian Information Filtering	We consider the information filtering problem, in which we face a stream of items, and must decide which ones to forward to a user to maximize the number of relevant items shown, minus a penalty for each irrelevant item shown. Forwarding decisions are made separately in a personalized way for each user. We focus on the...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	37,107
2304.10909	Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study	Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatments. Automated medical coding can considerably alleviate this administrative burden. In this paper, we reproduce, compare, and analyze stat...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	359,604
1809.09350	Fully Implicit Online Learning	Regularized online learning is widely used in machine learning applications. In online learning, performing exact minimization ($i.e.,$ implicit update) is known to be beneficial to the numerical stability and structure of solution. In this paper we study a class of regularized online algorithms without linearizing the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	108,691
2303.17490	Sound to Visual Scene Generation by Audio-to-Visual Latent Alignment	How does audio describe the world around us? In this paper, we propose a method for generating an image of a scene from sound. Our method addresses the challenges of dealing with the large gaps that often exist between sight and sound. We design a model that works by scheduling the learning procedure of each model comp...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	355,222
2407.05782	Sequential Contrastive Audio-Visual Learning	Contrastive learning has emerged as a powerful technique in audio-visual representation learning, leveraging the natural co-occurrence of audio and visual modalities in extensive web-scale video datasets to achieve significant advancements. However, conventional contrastive audio-visual learning methodologies often rel...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	471,132
2112.09824	Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better	Federated learning (FL) enables distribution of machine learning workloads from the cloud to resource-limited edge devices. Unfortunately, current deep networks remain not only too compute-heavy for inference and training on edge devices, but also too large for communicating updates over bandwidth-constrained networks....	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	272,262
2201.07106	Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structures	Lesions or organ boundaries visible through medical imaging data are often ambiguous, thus resulting in significant variations in multi-reader delineations, i.e., the source of aleatoric uncertainty. In particular, quantifying the inter-observer variability of manual annotations with Magnetic Resonance (MR) Imaging dat...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	275,932
2209.09526	Deep Neural Network-Based Detector for Single-Carrier Index Modulation NOMA	In this paper, a deep neural network (DNN)-based detector for an uplink single-carrier index modulation nonorthogonal multiple access (SC-IM-NOMA) system is proposed, where SC-IM-NOMA allows users to use the same set of subcarriers for transmitting their data modulated by the sub-carrier index modulation technique. Mor...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	318,545
1604.08600	Caching and Delivery via Interference Elimination	We propose a new caching scheme where linear combinations of the file segments are cached at the users, for the cases where the number of files is no greater than the number of users. When a user requests a certain file in the delivery phase, the other file segments in the cached linear combinations can be viewed as in...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	55,224
2302.13328	Reinforcement Learning Based Pushing and Grasping Objects from Ungraspable Poses	Grasping an object when it is in an ungraspable pose is a challenging task, such as books or other large flat objects placed horizontally on a table. Inspired by human manipulation, we address this problem by pushing the object to the edge of the table and then grasping it from the hanging part. In this paper, we devel...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	347,904
2308.13252	Kissing to Find a Match: Efficient Low-Rank Permutation Representation	Permutation matrices play a key role in matching and assignment problems across the fields, especially in computer vision and robotics. However, memory for explicitly representing permutation matrices grows quadratically with the size of the problem, prohibiting large problem instances. In this work, we propose to tack...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	387,845
2310.06178	Look-Up mAI GeMM: Increasing AI GeMMs Performance by Nearly 2.5x via msGeMM	AI models are increasing in size and recent advancement in the community has shown that unlike HPC applications where double precision datatype are required, lower-precision datatypes such as fp8 or int4 are sufficient to bring the same model quality both for training and inference. Following these trends, GPU vendors ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	398,457
2110.03175	Fingerprinting Multi-exit Deep Neural Network Models via Inference Time	Transforming large deep neural network (DNN) models into the multi-exit architectures can overcome the overthinking issue and distribute a large DNN model on resource-constrained scenarios (e.g. IoT frontend devices and backend servers) for inference and transmission efficiency. Nevertheless, intellectual property (IP)...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	259,397
2112.12927	Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification	Learning a common latent embedding by aligning the latent spaces of cross-modal autoencoders is an effective strategy for Generalized Zero-Shot Classification (GZSC). However, due to the lack of fine-grained instance-wise annotations, it still easily suffer from the domain shift problem for the discrepancy between the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	273,086
1807.00412	Learning to Drive in a Day	We demonstrate the first application of deep reinforcement learning to autonomous driving. From randomly initialised parameters, our model is able to learn a policy for lane following in a handful of training episodes using a single monocular image as input. We provide a general and easy to obtain reward: the distance ...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	101,828
2310.17576	1D-Touch: NLP-Assisted Coarse Text Selection via a Semi-Direct Gesture	Existing text selection techniques on touchscreen focus on improving the control for moving the carets. Coarse-grained text selection on word and phrase levels has not received much support beyond word-snapping and entity recognition. We introduce 1D-Touch, a novel text selection method that complements the carets-base...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	403,195
2209.13598	Computing Melodic Templates in Oral Music Traditions	The term melodic template or skeleton refers to a basic melody which is subject to variation during a music performance. In many oral music tradition, these templates are implicitly passed throughout generations without ever being formalized in a score. In this work, we introduce a new geometric optimization problem, t...	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	319,963
1102.3340	Multi-skill Collaborative Teams based on Densest Subgraphs	We consider the problem of identifying a team of skilled individuals for collaboration, in the presence of a social network. Each node in the social network may be an expert in one or more skills. Edge weights specify affinity or collaborative compatibility between respective nodes. Given a project that requires a set ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	9,239
2501.13986	An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks	Rotation equivariant graph neural networks, i.e., networks designed to guarantee certain geometric relations between their inputs and outputs, yield state-of-the-art performance on spatial deep learning tasks. They exhibit high data efficiency during training and significantly reduced inference time for interatomic pot...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	526,929
2108.07318	Peak Sidelobe Level and Peak Crosscorrelation of Golay-Rudin-Shapiro Sequences	Sequences with low aperiodic autocorrelation and crosscorrelation are used in communications and remote sensing. Golay and Shapiro independently devised a recursive construction that produces families of complementary pairs of binary sequences. In the simplest case, the construction produces the Rudin-Shapiro sequences...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	250,879
2105.02027	Non-Autoregressive vs Autoregressive Neural Networks for System Identification	The application of neural networks to non-linear dynamic system identification tasks has a long history, which consists mostly of autoregressive approaches. Autoregression, the usage of the model outputs of previous time steps, is a method of transferring a system state between time steps, which is not necessary for mo...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	233,707
2308.12261	Prompt2Model: Generating Deployable Models from Natural Language Instructions	Large language models (LLMs) enable system builders today to create competent NLP systems through prompting, where they only need to describe the task in natural language and provide a few examples. However, in other ways, LLMs are a step backward from traditional special-purpose NLP models; they require extensive comp...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	387,475
2406.09330	Learning from Natural Language Explanations for Generalizable Entity Matching	Entity matching is the task of linking records from different sources that refer to the same real-world entity. Past work has primarily treated entity linking as a standard supervised learning problem. However, supervised entity matching models often do not generalize well to new data, and collecting exhaustive labeled...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	463,877
2106.00417	Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners	Unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) are two typical strategies to reduce expensive manual annotations in machine learning. In order to learn effective models for a target task, UDA utilizes the available labeled source data, which may have different distributions from unlabeled sampl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	238,114
1604.08397	GNU Radio Signal Processing Models for Dynamic Multi-User Burst Modems	This paper presents a modern method for implementing burst modems in GNU Radio. Since burst modems are widely used for multi-user channel access and sharing in non-broadcast radio systems, this capability is critical to the development of numerous waveforms in GNU Radio. We focus on making such systems easy to develop ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	55,207
2502.02549	Anytime Incremental $\rho$POMDP Planning in Continuous Spaces	Partially Observable Markov Decision Processes (POMDPs) provide a robust framework for decision-making under uncertainty in applications such as autonomous driving and robotic exploration. Their extension, $\rho$POMDPs, introduces belief-dependent rewards, enabling explicit reasoning about uncertainty. Existing online ...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	530,370
1912.03646	Universal Limitations on Quantum Key Distribution over a Network	We consider the distribution of secret keys, both in a bipartite and a multipartite (conference) setting, via a quantum network and establish a framework to obtain bounds on the achievable rates. We show that any multipartite private state--the output of a protocol distilling secret key among the trusted parties--has t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	156,653
1712.01990	A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting	One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings --- e.g., a big shopping mall and a university campus --- is a scalable indoor localization technique. In this paper, we report the current status of our investigation on the use of deep neural network...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	86,215
2005.11093	DJEnsemble: On the Selection of a Disjoint Ensemble of Deep Learning Black-Box Spatio-Temporal Models	In this paper, we present a cost-based approach for the automatic selection and allocation of a disjoint ensemble of black-box predictors to answer predictive spatio-temporal queries. Our approach is divided into two parts -- offline and online. During the offline part, we preprocess the predictive domain data -- trans...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	178,382
2410.22837	SFDFusion: An Efficient Spatial-Frequency Domain Fusion Network for Infrared and Visible Image Fusion	Infrared and visible image fusion aims to utilize the complementary information from two modalities to generate fused images with prominent targets and rich texture details. Most existing algorithms only perform pixel-level or feature-level fusion from different modalities in the spatial domain. They usually overlook t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	503,791
1204.0191	OCR Post-Processing Error Correction Algorithm using Google Online Spelling Suggestion	With the advent of digital optical scanners, a lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into an electronic version that can be manipulated by a computer. For this purpose, OCR, short for Optical Character Recognition was developed to translate scanned graphical text ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	15,231
2003.08763	Shape retrieval of non-rigid 3d human models	3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided ...	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	168,850
1904.11886	Recommending research articles to consumers of online vaccination information	Online health communications often provide biased interpretations of evidence and have unreliable links to the source research. We tested the feasibility of a tool for matching webpages to their source evidence. From 207,538 eligible vaccination-related PubMed articles, we evaluated several approaches using 3,573 uniqu...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	128,972
2207.05666	Zero-shot Cross-lingual Transfer is Under-specified Optimization	Pretrained multilingual encoders enable zero-shot cross-lingual transfer, but often produce unreliable models that exhibit high performance variance on the target language. We postulate that this high variance results from zero-shot cross-lingual transfer solving an under-specified optimization problem. We show that an...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	307,616
2401.09216	Algebraic solution of project scheduling problems with temporal constraints	New solutions for problems in optimal scheduling of activities in a project under temporal constraints are developed in the framework of tropical algebra which deals with the theory and application of algebraic systems with idempotent operations. We start with a constrained tropical optimization problem that has an obj...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	422,186
2209.15450	Explainable Censored Learning: Finding Critical Features with Long Term Prognostic Values for Survival Prediction	Interpreting critical variables involved in complex biological processes related to survival time can help understand prediction from survival models, evaluate treatment efficacy, and develop new therapies for patients. Currently, the predictive results of deep learning (DL)-based models are better than or as good as s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	320,614
2010.05459	D2D Assisted Multi-antenna Coded Caching	A device-to-device (D2D) aided multi-antenna coded caching scheme is proposed to improve the average delivery rate and reduce the downlink (DL) beamforming complexity.} Novel beamforming and resource allocation schemes are proposed where local data exchange among nearby users is exploited. The transmission is split int...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	200,141
1904.09722	FishNet: A Camera Localizer using Deep Recurrent Networks	This paper proposes a robust localization system that employs deep learning for better scene representation, and enhances the accuracy of 6-DOF camera pose estimation. Inspired by the fact that global scene structure can be revealed by wide field-of-view, we leverage the large overlap of a fisheye camera between adjace...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	128,461
2209.08351	Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control	Flocking control is a significant problem in multi-agent systems such as multi-agent unmanned aerial vehicles and multi-agent autonomous underwater vehicles, which enhances the cooperativity and safety of agents. In contrast to traditional methods, multi-agent reinforcement learning (MARL) solves the problem of flockin...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	true	false	false	false	318,093
2205.13933	Standalone Neural ODEs with Sensitivity Analysis	This paper presents the Standalone Neural ODE (sNODE), a continuous-depth neural ODE model capable of describing a full deep neural network. This uses a novel nonlinear conjugate gradient (NCG) descent optimization scheme for training, where the Sobolev gradient can be incorporated to improve smoothness of model weight...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	299,148
2107.10955	Learning Linear Polytree Structural Equation Models	We are interested in the problem of learning the directed acyclic graph (DAG) when data are generated from a linear structural equation model (SEM) and the causal structure can be characterized by a polytree. Under the Gaussian polytree models, we study sufficient conditions on the sample sizes for the well-known Chow-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,441

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