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
1306.0733	Fast Gradient-Based Inference with Continuous Latent Variable Models in Auxiliary Form	We propose a technique for increasing the efficiency of gradient-based inference and learning in Bayesian networks with multiple layers of continuous latent vari- ables. We show that, in many cases, it is possible to express such models in an auxiliary form, where continuous latent variables are conditionally determini...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	24,985
2012.04483	A Novel Transformation Approach of Shared-link Coded Caching Schemes for Multiaccess Networks	This paper considers the multiaccess coded caching systems formulated by Hachem et al., including a central server containing $N$ files connected to $K$ cache-less users through an error-free shared link, and $K$ cache-nodes, each equipped with a cache memory size of $M$ files. Each user has access to $L$ neighbouring ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	210,473
1710.06608	Cell Segmentation in 3D Confocal Images using Supervoxel Merge-Forests with CNN-based Hypothesis Selection	Automated segmentation approaches are crucial to quantitatively analyze large-scale 3D microscopy images. Particularly in deep tissue regions, automatic methods still fail to provide error-free segmentations. To improve the segmentation quality throughout imaged samples, we present a new supervoxel-based 3D segmentatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	82,804
2111.09013	Image Super-Resolution Using T-Tetromino Pixels	For modern high-resolution imaging sensors, pixel binning is performed in low-lighting conditions and in case high frame rates are required. To recover the original spatial resolution, single-image super-resolution techniques can be applied for upscaling. To achieve a higher image quality after upscaling, we propose a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	266,875
2002.10670	Exploring BERT Parameter Efficiency on the Stanford Question Answering Dataset v2.0	In this paper we explore the parameter efficiency of BERT arXiv:1810.04805 on version 2.0 of the Stanford Question Answering dataset (SQuAD2.0). We evaluate the parameter efficiency of BERT while freezing a varying number of final transformer layers as well as including the adapter layers proposed in arXiv:1902.00751. ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	165,476
2201.09910	Learning Neural Contextual Bandits Through Perturbed Rewards	Thanks to the power of representation learning, neural contextual bandit algorithms demonstrate remarkable performance improvement against their classical counterparts. But because their exploration has to be performed in the entire neural network parameter space to obtain nearly optimal regret, the resulting computati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	276,812
2012.10758	Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality	Increasing popularity of high-dynamic-range (HDR) image and video content brings the need for metrics that could predict the severity of image impairments as seen on displays of different brightness levels and dynamic range. Such metrics should be trained and validated on a sufficiently large subjective image quality d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,431
2402.07192	Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations	Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved sur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	428,603
2004.00503	Deep Learning Approach for Enhanced Cyber Threat Indicators in Twitter Stream	In recent days, the amount of Cyber Security text data shared via social media resources mainly Twitter has increased. An accurate analysis of this data can help to develop cyber threat situational awareness framework for a cyber threat. This work proposes a deep learning based approach for tweet data analysis. To conv...	false	false	false	true	false	false	true	false	true	false	false	false	true	false	false	true	false	false	170,660
2007.05566	Contrastive Training for Improved Out-of-Distribution Detection	Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper proposes and investigates the use of contrastive training to boost OOD detection performance. Unlike leading methods for OOD detection, our approach does not requ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,714
1912.01454	Representation Learning on Unit Ball with 3D Roto-Translational Equivariance	Convolution is an integral operation that defines how the shape of one function is modified by another function. This powerful concept forms the basis of hierarchical feature learning in deep neural networks. Although performing convolution in Euclidean geometries is fairly straightforward, its extension to other topol...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	156,094
2110.05041	Provenance in Temporal Interaction Networks	In temporal interaction networks, vertices correspond to entities, which exchange data quantities (e.g., money, bytes, messages) over time. Tracking the origin of data that have reached a given vertex at any time can help data analysts to understand the reasons behind the accumulated quantity at the vertex or behind th...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	260,144
1512.03549	Words are not Equal: Graded Weighting Model for building Composite Document Vectors	Despite the success of distributional semantics, composing phrases from word vectors remains an important challenge. Several methods have been tried for benchmark tasks such as sentiment classification, including word vector averaging, matrix-vector approaches based on parsing, and on-the-fly learning of paragraph vect...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	50,053
2401.13462	Growing from Exploration: A self-exploring framework for robots based on foundation models	Intelligent robot is the ultimate goal in the robotics field. Existing works leverage learning-based or optimization-based methods to accomplish human-defined tasks. However, the challenge of enabling robots to explore various environments autonomously remains unresolved. In this work, we propose a framework named GExp...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	423,736
2312.07174	Investigation into the Training Dynamics of Learned Optimizers	Optimization is an integral part of modern deep learning. Recently, the concept of learned optimizers has emerged as a way to accelerate this optimization process by replacing traditional, hand-crafted algorithms with meta-learned functions. Despite the initial promising results of these methods, issues with stability ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	414,824
2203.05132	Compilable Neural Code Generation with Compiler Feedback	Automatically generating compilable programs with (or without) natural language descriptions has always been a touchstone problem for computational linguistics and automated software engineering. Existing deep-learning approaches model code generation as text generation, either constrained by grammar structures in deco...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	284,717
1607.00436	Terminal-Set-Enhanced Community Detection in Social Networks	Community detection aims to reveal the community structure in a social network, which is one of the fundamental problems. In this paper we investigate the community detection problem based on the concept of terminal set. A terminal set is a group of users within which any two users belong to different communities. Alth...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	58,073
2410.03791	People are poorly equipped to detect AI-powered voice clones	As generative artificial intelligence (AI) continues its ballistic trajectory, everything from text to audio, image, and video generation continues to improve at mimicking human-generated content. Through a series of perceptual studies, we report on the realism of AI-generated voices in terms of identity matching and n...	true	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	494,972
2103.05950	FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding	Emerging interests have been brought to recognize previously unseen objects given very few training examples, known as few-shot object detection (FSOD). Recent researches demonstrate that good feature embedding is the key to reach favorable few-shot learning performance. We observe object proposals with different Inter...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	224,143
2412.07182	An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications	This study focuses on enhancing rice leaf disease image classification algorithms, which have traditionally relied on Convolutional Neural Network (CNN) models. We employed transfer learning with MobileViTV2_050 using ImageNet-1k weights, a lightweight model that integrates CNN's local feature extraction with Vision Tr...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	515,546
2112.03860	Differentiable Gaussianization Layers for Inverse Problems Regularized by Deep Generative Models	Deep generative models such as GANs, normalizing flows, and diffusion models are powerful regularizers for inverse problems. They exhibit great potential for helping reduce ill-posedness and attain high-quality results. However, the latent tensors of such deep generative models can fall out of the desired high-dimensio...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	270,360
2301.04243	Robust Human Identity Anonymization using Pose Estimation	Many outdoor autonomous mobile platforms require more human identity anonymized data to power their data-driven algorithms. The human identity anonymization should be robust so that less manual intervention is needed, which remains a challenge for current face detection and anonymization systems. In this paper, we prop...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	340,000
2210.10360	Adaptive Neural Network Ensemble Using Frequency Distribution	Neural network (NN) ensembles can reduce large prediction variance of NN and improve prediction accuracy. For highly nonlinear problems with insufficient data set, the prediction accuracy of NN models becomes unstable, resulting in a decrease in the accuracy of ensembles. Therefore, this study proposes a frequency dist...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	324,891
1708.02271	Implementation of Torque Controller for Brushless Motors on the Omni-directional Wheeled Mobile Robot	The major issue for the wheeled mobile robot is the low level controller gains tuning up especially in the robot competition. The floor surface can be damaged by the robot wheels during the competition, therefore the surface coefficient can be changed over time. PI gains have to be tuned before every match along the co...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	78,552
2406.19825	Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems	The ongoing energy transition drives the development of decentralised renewable energy sources, which are heterogeneous and weather-dependent, complicating their integration into energy systems. This study tackles this issue by introducing a novel reinforcement learning (RL) framework tailored for the co-optimisation o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	468,581
2303.08812	Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networks	Convolutional neural networks (CNNs) have seen extensive applications in scientific data analysis, including in neutrino telescopes. However, the data from these experiments present numerous challenges to CNNs, such as non-regular geometry, sparsity, and high dimensionality. Consequently, CNNs are highly inefficient on...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	351,784
2406.08206	Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation	Estimating conditional average dose responses (CADR) is an important but challenging problem. Estimators must correctly model the potentially complex relationships between covariates, interventions, doses, and outcomes. In recent years, the machine learning community has shown great interest in developing tailored CADR...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	463,388
1902.01975	Age of Information in Multiple Sensing	Having timely and fresh knowledge about the current state of information sources is critical in a variety of applications. In particular, a status update may arrive at the destination much later than its generation time due to processing and communication delays. The freshness of the status update at the destination is...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	120,779
2303.09678	Neural Lyapunov Control for Nonlinear Systems with Unstructured Uncertainties	Stabilizing controller design and region of attraction (RoA) estimation are essential in nonlinear control. Moreover, it is challenging to implement a control Lyapunov function (CLF) in practice when only partial knowledge of the system is available. We propose a learning framework that can synthesize state-feedback co...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	352,140
2412.10457	Explaining Model Overfitting in CNNs via GMM Clustering	Convolutional Neural Networks (CNNs) have demonstrated remarkable prowess in the field of computer vision. However, their opaque decision-making processes pose significant challenges for practical applications. In this study, we provide quantitative metrics for assessing CNN filters by clustering the feature maps corre...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	516,945
1707.09441	A compressive channel estimation technique robust to synchronization impairments	Initial access at millimeter wave frequencies is a challenging problem due to hardware non-idealities and low SNR measurements prior to beamforming. Prior work has exploited the observation that mmWave MIMO channels are sparse in the spatial angle domain and has used compressed sensing based algorithms for channel esti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,004
2411.15109	Effective Littlestone Dimension	Delle Rose et al.~(COLT'23) introduced an effective version of the Vapnik-Chervonenkis dimension, and showed that it characterizes improper PAC learning with total computable learners. In this paper, we introduce and study a similar effectivization of the notion of Littlestone dimension. Finite effective Littlestone di...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	510,441
2111.15181	Zero-Shot Semantic Segmentation via Spatial and Multi-Scale Aware Visual Class Embedding	Fully supervised semantic segmentation technologies bring a paradigm shift in scene understanding. However, the burden of expensive labeling cost remains as a challenge. To solve the cost problem, recent studies proposed language model based zero-shot semantic segmentation (L-ZSSS) approaches. In this paper, we address...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,848
2205.03454	Structure Learning in Graphical Models from Indirect Observations	This paper considers learning of the graphical structure of a $p$-dimensional random vector $X \in R^p$ using both parametric and non-parametric methods. Unlike the previous works which observe $x$ directly, we consider the indirect observation scenario in which samples $y$ are collected via a sensing matrix $A \in R^{...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	295,283
2204.13536	Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets	We propose a simple model of an idealized online cultural market in which $N$ items, endowed with a hidden quality metric, are recommended to users by a ranking algorithm possibly biased by the current items' popularity. Our goal is to better understand the underlying mechanisms of the well-known fact that popularity b...	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	false	293,845
2309.07248	Geometric Gait Optimization for Inertia-Dominated Systems With Nonzero Net Momentum	Inertia-dominated mechanical systems can achieve net displacement by 1) periodically changing their shape (known as kinematic gait) and 2) adjusting their inertia distribution to utilize the existing nonzero net momentum (known as momentum gait). Therefore, finding the gait that most effectively utilizes the two types ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	391,711
1507.03049	Forecasting the cost of processing multi-join queries via hashing for main-memory databases (Extended version)	Database management systems (DBMSs) carefully optimize complex multi-join queries to avoid expensive disk I/O. As servers today feature tens or hundreds of gigabytes of RAM, a significant fraction of many analytic databases becomes memory-resident. Even after careful tuning for an in-memory environment, a linear disk I...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	45,044
1906.04940	CogCompTime: A Tool for Understanding Time in Natural Language Text	Automatic extraction of temporal information in text is an important component of natural language understanding. It involves two basic tasks: (1) Understanding time expressions that are mentioned explicitly in text (e.g., February 27, 1998 or tomorrow), and (2) Understanding temporal information that is conveyed impli...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	134,885
2402.10086	Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review	Artificial Intelligence (AI) shows promising applications for the perception and planning tasks in autonomous driving (AD) due to its superior performance compared to conventional methods. However, inscrutable AI systems exacerbate the existing challenge of safety assurance of AD. One way to mitigate this challenge is ...	true	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	429,809
2409.19315	Analog In-Memory Computing Attention Mechanism for Fast and Energy-Efficient Large Language Models	Transformer networks, driven by self-attention, are central to Large Language Models. In generative Transformers, self-attention uses cache memory to store token projections, avoiding recomputation at each time step. However, GPU-stored projections must be loaded into SRAM for each new generation step, causing latency ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	true	492,626
1006.5686	Geometric Approximations of Some Aloha-like Stability Regions	Most bounds on the stability region of Aloha give necessary and sufficient conditions for the stability of an arrival rate vector under a specific contention probability (control) vector. But such results do not yield easy-to-check bounds on the overall Aloha stability region because they potentially require checking m...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,918
2311.00797	Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling	We study the tipping point collective dynamics of an adaptive susceptible-infected-susceptible (SIS) epidemiological network in a data-driven, machine learning-assisted manner. We identify a parameter-dependent effective stochastic differential equation (eSDE) in terms of physically meaningful coarse mean-field variabl...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	404,776
cs/0212038	The Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning	A large body of research in machine learning is concerned with supervised learning from examples. The examples are typically represented as vectors in a multi-dimensional feature space (also known as attribute-value descriptions). A teacher partitions a set of training examples into a finite number of classes. The task...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	537,767
2407.17277	From Data to Predictive Control: A Framework for Stochastic Linear Systems with Output Measurements	We introduce data to predictive control, D2PC, a framework to facilitate the design of robust and predictive controllers from data. The proposed framework is designed for discrete-time stochastic linear systems with output measurements and provides a principled design of a predictive controller based on data. The frame...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	475,908
2005.12592	GECToR -- Grammatical Error Correction: Tag, Not Rewrite	In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder. Our system is pre-trained on synthetic data and then fine-tuned in two stages: first on errorful corpora, and second on a combination of errorful and error-free parallel corpora. We design custom token-level transformation...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	178,782
2410.11199	Isambard-AI: a leadership class supercomputer optimised specifically for Artificial Intelligence	Isambard-AI is a new, leadership-class supercomputer, designed to support AI-related research. Based on the HPE Cray EX4000 system, and housed in a new, energy efficient Modular Data Centre in Bristol, UK, Isambard-AI employs 5,448 NVIDIA Grace-Hopper GPUs to deliver over 21 ExaFLOP/s of 8-bit floating point performanc...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	498,432
2402.04211	Variational Shapley Network: A Probabilistic Approach to Self-Explaining Shapley values with Uncertainty Quantification	Shapley values have emerged as a foundational tool in machine learning (ML) for elucidating model decision-making processes. Despite their widespread adoption and unique ability to satisfy essential explainability axioms, computational challenges persist in their estimation when ($i$) evaluating a model over all possib...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	427,363
2502.06149	Reward-Based Collision-Free Algorithm for Trajectory Planning of Autonomous Robots	This paper introduces a new mission planning algorithm for autonomous robots that enables the reward-based selection of an optimal waypoint sequence from a predefined set. The algorithm computes a feasible trajectory and corresponding control inputs for a robot to navigate between waypoints while avoiding obstacles, ma...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	531,951
2007.00924	IIE-NLP-NUT at SemEval-2020 Task 4: Guiding PLM with Prompt Template Reconstruction Strategy for ComVE	This paper introduces our systems for the first two subtasks of SemEval Task4: Commonsense Validation and Explanation. To clarify the intention for judgment and inject contrastive information for selection, we propose the input reconstruction strategy with prompt templates. Specifically, we formalize the subtasks into ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	185,268
2302.06239	Finite element hybridization of port-Hamiltonian systems	In this contribution, we extend the hybridization framework for the Hodge Laplacian [Awanou et al., Hybridization and postprocessing in finite element exterior calculus, 2023 ] to port-Hamiltonian systems describing linear wave propagation phenomena. To this aim, a dual field mixed Galerkin discretization is introduced...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	345,337
0807.1211	Flux: FunctionaL Updates for XML (extended report)	XML database query languages have been studied extensively, but XML database updates have received relatively little attention, and pose many challenges to language design. We are developing an XML update language called Flux, which stands for FunctionaL Updates for XML, drawing upon ideas from functional programming l...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	2,040
1912.11856	A Comparative Study on Machine Learning Algorithms for the Control of a Wall Following Robot	A comparison of the performance of various machine learning models to predict the direction of a wall following robot is presented in this paper. The models were trained using an open-source dataset that contains 24 ultrasound sensors readings and the corresponding direction for each sample. This dataset was captured u...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	158,685
2201.00199	The GatedTabTransformer. An enhanced deep learning architecture for tabular modeling	There is an increasing interest in the application of deep learning architectures to tabular data. One of the state-of-the-art solutions is TabTransformer which incorporates an attention mechanism to better track relationships between categorical features and then makes use of a standard MLP to output its final logits....	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	273,893
2301.11201	Relative-Interior Solution for the (Incomplete) Linear Assignment Problem with Applications to the Quadratic Assignment Problem	We study the set of optimal solutions of the dual linear programming formulation of the linear assignment problem (LAP) to propose a method for computing a solution from the relative interior of this set. Assuming that an arbitrary dual-optimal solution and an optimal assignment are available (for which many efficient ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	342,053
1606.02213	Energy-Aware Relay Selection and Power Allocation for Multiple-User Cooperative Networks	This paper investigates the relay assignment and power allocation problem for two different network power management policies: group lifetime maximization (GLM) and minimum weighted total power (MWTP), with the aim of lifetime maximization in symbol error rate (SER) constrained multipleuser cooperative network. With op...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	56,920
2006.07281	Algorithms and Learning for Fair Portfolio Design	We consider a variation on the classical finance problem of optimal portfolio design. In our setting, a large population of consumers is drawn from some distribution over risk tolerances, and each consumer must be assigned to a portfolio of lower risk than her tolerance. The consumers may also belong to underlying grou...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	181,746
1808.03143	A Hybrid Dynamic-regenerative Damping Scheme for Energy Regeneration in Variable Impedance Actuators	Increasing research efforts have been made to improve the energy efficiency of variable impedance actuators (VIAs) through reduction of energy consumption. However, the harvesting of dissipated energy in such systems remains underexplored. This study proposes a novel variable damping module design enabling energy regen...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	104,886
2312.15868	Video Frame Interpolation with Region-Distinguishable Priors from SAM	In existing Video Frame Interpolation (VFI) approaches, the motion estimation between neighboring frames plays a crucial role. However, the estimation accuracy in existing methods remains a challenge, primarily due to the inherent ambiguity in identifying corresponding areas in adjacent frames for interpolation. Theref...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	418,168
2210.03506	What Do End-Users Really Want? Investigation of Human-Centered XAI for Mobile Health Apps	In healthcare, AI systems support clinicians and patients in diagnosis, treatment, and monitoring, but many systems' poor explainability remains challenging for practical application. Overcoming this barrier is the goal of explainable AI (XAI). However, an explanation can be perceived differently and, thus, not solve t...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	322,063
1510.08382	Flexibly Mining Better Subgroups	In subgroup discovery, also known as supervised pattern mining, discovering high quality one-dimensional subgroups and refinements of these is a crucial task. For nominal attributes, this is relatively straightforward, as we can consider individual attribute values as binary features. For numerical attributes, the task...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	48,281
2011.05931	Transmission of a Bit over a Discrete Poisson Channel with Memory	A coding scheme for transmission of a bit maps a given bit to a sequence of channel inputs (called the codeword associated to the transmitted bit). In this paper, we study the problem of designing the best code for a discrete Poisson channel with memory (under peak-power and total-power constraints). The outputs of a d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	206,086
2102.03302	Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discovery	Deep graph embedding is an important approach for community discovery. Deep graph neural network with self-supervised mechanism can obtain the low-dimensional embedding vectors of nodes from unlabeled and unstructured graph data. The high-order information of graph can provide more abundant structure information for th...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	218,699
2008.10236	Strawberry Detection using Mixed Training on Simulated and Real Data	This paper demonstrates how simulated images can be useful for object detection tasks in the agricultural sector, where labeled data can be scarce and costly to collect. We consider training on mixed datasets with real and simulated data for strawberry detection in real images. Our results show that using the real data...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,943
2003.05375	On the Effect of Correlation on the Capacity of Backscatter Communication Systems	We analyse the effect of correlation between the forward and backward links on the capacity of backscatter communication systems. To that aim, we obtain an analytical expression for the average capacity under a correlated Rayleigh product fading channel, as well as closed-form asymptotic expressions for the high and lo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	167,837
2011.01374	Synthetic Data Generation for Economists	As more tech companies engage in rigorous economic analyses, we are confronted with a data problem: in-house papers cannot be replicated due to use of sensitive, proprietary, or private data. Readers are left to assume that the obscured true data (e.g., internal Google information) indeed produced the results given, or...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	204,557
1907.04913	Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model	In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	138,230
2307.11166	Exploring reinforcement learning techniques for discrete and continuous control tasks in the MuJoCo environment	We leverage the fast physics simulator, MuJoCo to run tasks in a continuous control environment and reveal details like the observation space, action space, rewards, etc. for each task. We benchmark value-based methods for continuous control by comparing Q-learning and SARSA through a discretization approach, and using...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	380,818
1108.5212	Deinterleaving Finite Memory Processes via Penalized Maximum Likelihood	We study the problem of deinterleaving a set of finite-memory (Markov) processes over disjoint finite alphabets, which have been randomly interleaved by a finite-memory switch. The deinterleaver has access to a sample of the resulting interleaved process, but no knowledge of the number or structure of the component Mar...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	11,820
2204.01966	Time Efficient Joint UAV-BS Deployment and User Association based on Machine Learning	This paper proposes a time-efficient mechanism to decrease the on-line computing time of solving the joint unmanned aerial vehicle base station (UAV-BS) deployment and user/sensor association (UDUA) problem aiming at maximizing the downlink sum transmission throughput. The joint UDUA problem is decoupled into two sub-p...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	true	289,781
2502.02967	Demonstrating a Control Framework for Physical Human-Robot Interaction Toward Industrial Applications	Human-Robot Interaction (pHRI) is critical for implementing Industry 5.0 which focuses on human-centric approaches. However, few studies explore the practical alignment of pHRI to industrial grade performance. This paper introduces a versatile control framework designed to bridge this gap by incorporating the torque-ba...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	530,548
2306.03354	Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour	Being able to reason about how one's behaviour can affect the behaviour of others is a core skill required of intelligent driving agents. Despite this, the state of the art struggles to meet the need of agents to discover causal links between themselves and others. Observational approaches struggle because of the non-s...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	371,283
2207.08380	Visual Representations of Physiological Signals for Fake Video Detection	Realistic fake videos are a potential tool for spreading harmful misinformation given our increasing online presence and information intake. This paper presents a multimodal learning-based method for detection of real and fake videos. The method combines information from three modalities - audio, video, and physiology....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,572
2404.11537	SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening	Pansharpening is a significant image fusion technique that merges the spatial content and spectral characteristics of remote sensing images to generate high-resolution multispectral images. Recently, denoising diffusion probabilistic models have been gradually applied to visual tasks, enhancing controllable image gener...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	447,534
2201.12569	Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes	We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized. This problem exists ubiquitously in social media, finance and health informatics but is rarely inve...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	277,693
1811.03734	Close and ordinary social contacts: how important are they in promoting large-scale contagion?	An outstanding problem of interdisciplinary interest is to understand quantitatively the role of social contacts in contagion dynamics. In general, there are two types of contacts: close ones among friends, colleagues and family members, etc., and ordinary contacts from encounters with strangers. Typically, social rein...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	112,912
1909.00889	Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data	We propose to harness the potential of simulation for the semantic segmentation of real-world self-driving scenes in a domain generalization fashion. The segmentation network is trained without any data of target domains and tested on the unseen target domains. To this end, we propose a new approach of domain randomiza...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	143,743
1401.3520	Adaptive Mode Selection for Bidirectional Relay Networks -- Fixed Rate Transmission	In this paper, we consider the problem of sum throughput maximization for bidirectional relay networks with block fading. Thereby, user 1 and user 2 exchange information only via a relay node, i.e., a direct link between both users is not present. We assume that channel state information at the transmitter (CSIT) is no...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	29,901
1903.11502	Numerical modeling of neutron transport in SP3 approximation by finite element method	The SP3 approximation of the neutron transport equation allows improving the accuracy for both static and transient simulations for reactor core analysis compared with the neutron diffusion theory. Besides, the SP3 calculation costs are much less than higher order transport methods (SN or PN). Another advantage of the ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	125,528
2001.11602	Safe Trajectory Tracking in Uncertain Environments	In Model Predictive Control (MPC) formulations of trajectory tracking problems, infeasible reference trajectories and a-priori unknown constraints can lead to cumbersome designs, aggressive tracking, and loss of recursive feasibility. This is the case, for example, in trajectory tracking applications for mobile systems...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	162,114
2410.08146	Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning	A promising approach for improving reasoning in large language models is to use process reward models (PRMs). PRMs provide feedback at each step of a multi-step reasoning trace, potentially improving credit assignment over outcome reward models (ORMs) that only provide feedback at the final step. However, collecting de...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	496,974
2002.07354	Leveraging Linear Quadratic Regulator Cost and Energy Consumption for Ultra-Reliable and Low-Latency IoT Control Systems	To efficiently support the real-time control applications, networked control systems operating with ultra-reliable and low-latency communications (URLLCs) become fundamental technology for future Internet of things (IoT). However, the design of control, sensing and communications is generally isolated at present. In th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	164,442
1210.5117	Distributed and Autonomous Resource and Power Allocation for Wireless Networks	In this paper, a distributed and autonomous technique for resource and power allocation in orthogonal frequency division multiple access (OFDMA) femto-cellular networks is presented. Here, resource blocks (RBs) and their corresponding transmit powers are assigned to the user(s) in each cell individually without explici...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	19,249
1404.1356	Optimal learning with Bernstein Online Aggregation	We introduce a new recursive aggregation procedure called Bernstein Online Aggregation (BOA). The exponential weights include an accuracy term and a second order term that is a proxy of the quadratic variation as in Hazan and Kale (2010). This second term stabilizes the procedure that is optimal in different senses. We...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	32,102
2312.09688	Shaping and Being Shaped by Drones: Supporting Perception-Action Loops	We report on a three-day challenge during which five teams each programmed a nanodrone to be piloted through an obstacle course using bodily movement, in a 3D transposition of the '80s video-game Pacman. Using a bricolage approach to analyse interviews, field notes, video recordings, and inspection of each team's code ...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	415,843
1906.01299	Grid-based Localization Stack for Inspection Drones towards Automation of Large Scale Warehouse Systems	SLAM based techniques are often adopted for solving the navigation problem for the drones in GPS denied environment. Despite the widespread success of these approaches, they have not yet been fully exploited for automation in a warehouse system due to expensive sensors and setup requirements. This paper focuses on the ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	133,672
1811.03559	A Feature Complete SPIKE Banded Algorithm and Solver	New features and enhancements for the SPIKE banded solver are presented. Among all the SPIKE algorithm versions, we focus our attention on the recursive SPIKE technique which provides the best trade-off between generality and parallel efficiency, but was known for its lack of flexibility. Its application was essentiall...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	112,869
2004.00234	Botnet Detection Using Recurrent Variational Autoencoder	Botnets are increasingly used by malicious actors, creating increasing threat to a large number of internet users. To address this growing danger, we propose to study methods to detect botnets, especially those that are hard to capture with the commonly used methods, such as the signature based ones and the existing an...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	170,564
2107.02121	ParDen: Surrogate Assisted Hyper-Parameter Optimisation for Portfolio Selection	Portfolio optimisation is a multi-objective optimisation problem (MOP), where an investor aims to optimise the conflicting criteria of maximising a portfolio's expected return whilst minimising its risk and other costs. However, selecting a portfolio is a computationally expensive problem because of the cost associated...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	244,705
1907.00824	Designing Deep Reinforcement Learning for Human Parameter Exploration	Software tools for generating digital sound often present users with high-dimensional, parametric interfaces, that may not facilitate exploration of diverse sound designs. In this paper, we propose to investigate artificial agents using deep reinforcement learning to explore parameter spaces in partnership with users f...	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	137,152
2305.04847	CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation	Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint. This work ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	362,926
1803.00401	Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks	Deep neural network (DNN) architecture based models have high expressive power and learning capacity. However, they are essentially a black box method since it is not easy to mathematically formulate the functions that are learned within its many layers of representation. Realizing this, many researchers have started t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	91,671
2406.11906	NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics	Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the high-throughput analysis of protein composition in biological tissues. Many deep learning methods have been developed for \emph{de novo} peptide sequencing task, i.e., predicting the peptide sequence for the observed mass spectrum....	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	465,135
1806.04957	Expression Empowered ResiDen Network for Facial Action Unit Detection	The paper explores the topic of Facial Action Unit (FAU) detection in the wild. In particular, we are interested in answering the following questions: (1) how useful are residual connections across dense blocks for face analysis? (2) how useful is the information from a network trained for categorical Facial Expression...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	100,359
1903.07072	STNReID : Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification	Partial person re-identification (ReID) is a challenging task because only partial information of person images is available for matching target persons. Few studies, especially on deep learning, have focused on matching partial person images with holistic person images. This study presents a novel deep partial ReID fr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	124,531
2207.12405	Versatile Weight Attack via Flipping Limited Bits	To explore the vulnerability of deep neural networks (DNNs), many attack paradigms have been well studied, such as the poisoning-based backdoor attack in the training stage and the adversarial attack in the inference stage. In this paper, we study a novel attack paradigm, which modifies model parameters in the deployme...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	310,005
2304.05591	Semantic Feature Verification in FLAN-T5	This study evaluates the potential of a large language model for aiding in generation of semantic feature norms - a critical tool for evaluating conceptual structure in cognitive science. Building from an existing human-generated dataset, we show that machine-verified norms capture aspects of conceptual structure beyon...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	357,679
1308.3509	Stochastic Optimization for Machine Learning	It has been found that stochastic algorithms often find good solutions much more rapidly than inherently-batch approaches. Indeed, a very useful rule of thumb is that often, when solving a machine learning problem, an iterative technique which relies on performing a very large number of relatively-inexpensive updates w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	26,473
2401.10890	Event detection from novel data sources: Leveraging satellite imagery alongside GPS traces	Rapid identification and response to breaking events, particularly those that pose a threat to human life such as natural disasters or conflicts, is of paramount importance. The prevalence of mobile devices and the ubiquity of network connectivity has generated a massive amount of temporally- and spatially-stamped data...	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	422,806
2402.18998	COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection	Existing approaches towards anomaly detection~(AD) often rely on a substantial amount of anomaly-free data to train representation and density models. However, large anomaly-free datasets may not always be available before the inference stage; in which case an anomaly detection model must be trained with only a handful...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,645
2012.11685	Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval	Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documen...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	212,698

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