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
2109.05792	Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Making	Automated decision systems (ADS) have become ubiquitous in many high-stakes domains. Those systems typically involve sophisticated yet opaque artificial intelligence (AI) techniques that seldom allow for full comprehension of their inner workings, particularly for affected individuals. As a result, ADS are prone to def...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	254,949
0908.2397	Interference Assisted Secret Communication	Wireless communication is susceptible to eavesdropping attacks because of its broadcast nature. This paper illustrates how interference can be used to counter eavesdropping and assist secrecy. In particular, a wire-tap channel with a helping interferer (WT-HI) is considered. Here, a transmitter sends a confidential mes...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	4,289
2212.07237	Geometric Algebra for Optimal Control with Applications in Manipulation Tasks	Many problems in robotics are fundamentally problems of geometry, which lead to an increased research effort in geometric methods for robotics in recent years. The results were algorithms using the various frameworks of screw theory, Lie algebra and dual quaternions. A unification and generalization of these popular fo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	336,349
1606.07707	Collective Semi-Supervised Learning for User Profiling in Social Media	The abundance of user-generated data in social media has incentivized the development of methods to infer the latent attributes of users, which are crucially useful for personalization, advertising and recommendation. However, the current user profiling approaches have limited success, due to the lack of a principled w...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	57,767
1804.05253	"With 1 follower I must be AWESOME :P". Exploring the role of irony markers in irony recognition	Conversations in social media often contain the use of irony or sarcasm, when the users say the opposite of what they really mean. Irony markers are the meta-communicative clues that inform the reader that an utterance is ironic. We propose a thorough analysis of theoretically grounded irony markers in two social media...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	95,026
2010.11321	MRI Image Recovery using Damped Denoising Vector AMP	Motivated by image recovery in magnetic resonance imaging (MRI), we propose a new approach to solving linear inverse problems based on iteratively calling a deep neural-network, sometimes referred to as plug-and-play recovery. Our approach is based on the vector approximate message passing (VAMP) algorithm, which is kn...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	202,195
1410.4984	Gaussian Process Models with Parallelization and GPU acceleration	In this work, we present an extension of Gaussian process (GP) models with sophisticated parallelization and GPU acceleration. The parallelization scheme arises naturally from the modular computational structure w.r.t. datapoints in the sparse Gaussian process formulation. Additionally, the computational bottleneck is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	36,854
2103.09708	What's in My LiDAR Odometry Toolbox?	With the democratization of 3D LiDAR sensors, precise LiDAR odometries and SLAM are in high demand. New methods regularly appear, proposing solutions ranging from small variations in classical algorithms to radically new paradigms based on deep learning. Yet it is often difficult to compare these methods, notably due t...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	225,238
2005.02769	SwarmLab: a Matlab Drone Swarm Simulator	Among the available solutions for drone swarm simulations, we identified a gap in simulation frameworks that allow easy algorithms prototyping, tuning, debugging and performance analysis, and do not require the user to interface with multiple programming languages. We present SwarmLab, a software entirely written in Ma...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	175,967
1507.02084	Shedding Light on the Asymmetric Learning Capability of AdaBoost	In this paper, we propose a different insight to analyze AdaBoost. This analysis reveals that, beyond some preconceptions, AdaBoost can be directly used as an asymmetric learning algorithm, preserving all its theoretical properties. A novel class-conditional description of AdaBoost, which models the actual asymmetric b...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	44,941
2004.06100	Pretrained Transformers Improve Out-of-Distribution Robustness	Although pretrained Transformers such as BERT achieve high accuracy on in-distribution examples, do they generalize to new distributions? We systematically measure out-of-distribution (OOD) generalization for seven NLP datasets by constructing a new robustness benchmark with realistic distribution shifts. We measure th...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	172,416
2403.06206	Limit of the Maximum Random Permutation Set Entropy	The Random Permutation Set (RPS) is a new type of set proposed recently, which can be regarded as the generalization of evidence theory. To measure the uncertainty of RPS, the entropy of RPS and its corresponding maximum entropy have been proposed. Exploring the maximum entropy provides a possible way of understanding ...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	436,349
2107.00805	A Novel Low Complexity Faster-than-Nyquist (FTN) Signaling Detector for Ultra High-Order QAM	Faster-than-Nyquist (FTN) signaling is a promising non-orthogonal pulse modulation technique that can improve the spectral efficiency (SE) of next generation communication systems at the expense of higher detection complexity to remove the introduced inter-symbol interference (ISI). In this paper, we investigate the de...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	244,275
2206.08753	Information Geometry of Risks and Returns	We reveal a geometric structure underlying both hedging and investment products. The structure follows from a simple formula expressing investment risks in terms of returns. This informs optimal product designs. Optimal pure hedging (including cost-optimal products) and hybrid hedging (where a partial hedge is built in...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	303,283
2004.05746	Enabling Incremental Knowledge Transfer for Object Detection at the Edge	Object detection using deep neural networks (DNNs) involves a huge amount of computation which impedes its implementation on resource/energy-limited user-end devices. The reason for the success of DNNs is due to having knowledge over all different domains of observed environments. However, we need a limited knowledge o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	172,295
2408.01365	Data Debugging is NP-hard for Classifiers Trained with SGD	Data debugging is to find a subset of the training data such that the model obtained by retraining on the subset has a better accuracy. A bunch of heuristic approaches are proposed, however, none of them are guaranteed to solve this problem effectively. This leaves an open issue whether there exists an efficient algori...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	478,203
1908.06319	Locally Linear Embedding and fMRI feature selection in psychiatric classification	Background: Functional magnetic resonance imaging (fMRI) provides non-invasive measures of neuronal activity using an endogenous Blood Oxygenation-Level Dependent (BOLD) contrast. This article introduces a nonlinear dimensionality reduction (Locally Linear Embedding) to extract informative measures of the underlying ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	141,979
2403.11697	Urban Scene Diffusion through Semantic Occupancy Map	Generating unbounded 3D scenes is crucial for large-scale scene understanding and simulation. Urban scenes, unlike natural landscapes, consist of various complex man-made objects and structures such as roads, traffic signs, vehicles, and buildings. To create a realistic and detailed urban scene, it is crucial to accura...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	438,820
2411.01532	SPARC: Spectral Architectures Tackling the Cold-Start Problem in Graph Learning	Graphs play a central role in modeling complex relationships in data, yet most graph learning methods falter when faced with cold-start nodes--new nodes lacking initial connections--due to their reliance on adjacency information. To tackle this, we propose SPARC, a groundbreaking framework that introduces a novel appro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	505,113
2306.03476	Putting Humans in the Image Captioning Loop	Image Captioning (IC) models can highly benefit from human feedback in the training process, especially in cases where data is limited. We present work-in-progress on adapting an IC system to integrate human feedback, with the goal to make it easily adaptable to user-specific data. Our approach builds on a base IC mode...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	371,346
cs/0605092	The Multiple Access Channel with Feedback and Correlated Sources	In this paper, we investigate communication strategies for the multiple access channel with feedback and correlated sources (MACFCS). The MACFCS models a wireless sensor network scenario in which sensors distributed throughout an arbitrary random field collect correlated measurements and transmit them to a common sink....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,465
2106.05057	Multiple Kernel Representation Learning on Networks	Learning representations of nodes in a low dimensional space is a crucial task with numerous interesting applications in network analysis, including link prediction, node classification, and visualization. Two popular approaches for this problem are matrix factorization and random walk-based models. In this paper, we a...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	239,952
2404.04511	Cluster-based Video Summarization with Temporal Context Awareness	In this paper, we present TAC-SUM, a novel and efficient training-free approach for video summarization that addresses the limitations of existing cluster-based models by incorporating temporal context. Our method partitions the input video into temporally consecutive segments with clustering information, enabling the ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	444,674
2401.01195	Deep Learning Driven Buffer-Aided Cooperative Networks for B5G/6G: Challenges, Solutions, and Future Opportunities	Buffer-aided cooperative networks (BACNs) have garnered significant attention due to their potential applications in beyond fifth generation (B5G) or sixth generation (6G) critical scenarios. This article explores various typical application scenarios of buffer-aided relaying in B5G/6G networks to emphasize the importa...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	419,268
2012.03601	Efficient Kernel based Matched Filter Approach for Segmentation of Retinal Blood Vessels	Retinal blood vessels structure contains information about diseases like obesity, diabetes, hypertension and glaucoma. This information is very useful in identification and treatment of these fatal diseases. To obtain this information, there is need to segment these retinal vessels. Many kernel based methods have been ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	210,177
1710.09868	How far did we get in face spoofing detection?	The growing use of control access systems based on face recognition shed light over the need for even more accurate systems to detect face spoofing attacks. In this paper, an extensive analysis on face spoofing detection works published in the last decade is presented. The analyzed works are categorized by their fundam...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	83,270
2409.01780	State-of-the-art Advances of Deep-learning Linguistic Steganalysis Research	With the evolution of generative linguistic steganography techniques, conventional steganalysis falls short in robustly quantifying the alterations induced by steganography, thereby complicating detection. Consequently, the research paradigm has pivoted towards deep-learning-based linguistic steganalysis. This study of...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	485,463
2012.10016	The dual of an evaluation code	The aim of this work is to study the dual and the algebraic dual of an evaluation code using standard monomials and indicator functions. We show that the dual of an evaluation code is the evaluation code of the algebraic dual. We develop an algorithm for computing a basis for the algebraic dual. Let $C_1$ and $C_2$ be ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	212,229
2208.13628	Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment	Vision and Language Pretraining has become the prevalent approach for tackling multimodal downstream tasks. The current trend is to move towards ever larger models and pretraining datasets. This computational headlong rush does not seem reasonable in the long term to move toward sustainable solutions, and de facto excl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	315,100
1410.7881	A neural circuit for navigation inspired by C. elegans Chemotaxis	We develop an artificial neural circuit for contour tracking and navigation inspired by the chemotaxis of the nematode Caenorhabditis elegans. In order to harness the computational advantages spiking neural networks promise over their non-spiking counterparts, we develop a network comprising 7-spiking neurons with non-...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	37,112
2105.00826	WhatTheWikiFact: Fact-Checking Claims Against Wikipedia	The rise of Internet has made it a major source of information. Unfortunately, not all information online is true, and thus a number of fact-checking initiatives have been launched, both manual and automatic, to deal with the problem. Here, we present our contribution in this regard: \emph{WhatTheWikiFact}, a system fo...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	233,347
2012.09328	Simultaneous View and Feature Selection for Collaborative Multi-Robot Perception	Collaborative multi-robot perception provides multiple views of an environment, offering varying perspectives to collaboratively understand the environment even when individual robots have poor points of view or when occlusions are caused by obstacles. These multiple observations must be intelligently fused for accurat...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	212,028
1901.08306	Reentrant phase transitions in threshold driven contagion on multiplex networks	Models of threshold driven contagion explain the cascading spread of information, behavior, systemic risk, and epidemics on social, financial and biological networks. At odds with empirical observation, these models predict that single-layer unweighted networks become resistant to global cascades after reaching suffici...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	119,442
2012.08405	Model-Based Deep Learning	Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physics, prior information and additional domain knowledge. Simple classical models are useful but sensitive to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	211,755
1304.4693	Structured Lattice Codes for Some Two-User Gaussian Networks with Cognition, Coordination and Two Hops	We study a number of two-user interference networks with multiple-antenna transmitters/receivers, transmitter side information in the form of linear combinations (over finite-field) of the information messages, and two-hop relaying. We start with a Cognitive Interference Channel (CIC) where one of the transmitters (non...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,030
1812.11042	Image Processing in Quantum Computers	Quantum Image Processing (QIP)is an exciting new field showing a lot of promise as a powerful addition to the arsenal of Image Processing techniques. Representing image pixel by pixel using classical information requires an enormous amount of computational resources. Hence, exploring methods to represent images in a di...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	117,490
2108.13555	Adaptive Label Smoothing To Regularize Large-Scale Graph Training	Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many domains. To handle large-scale graphs, most of the existing methods partition the input graph into multiple sub-graphs (e.g., through node cl...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	252,826
1708.07058	Identifying Growth-Patterns in Children by Applying Cluster analysis to Electronic Medical Records	Obesity is one of the leading health concerns in the United States. Researchers and health care providers are interested in understanding factors affecting obesity and detecting the likelihood of obesity as early as possible. In this paper, we set out to recognize children who have higher risk of obesity by identifying...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	79,429
2411.05943	Quantifying artificial intelligence through algebraic generalization	The rapid development of modern artificial intelligence (AI) systems has created an urgent need for their scientific quantification. While their fluency across a variety of domains is impressive, modern AI systems fall short on tests requiring symbolic processing and abstraction - a glaring limitation given the necessi...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	506,899
2407.13013	FernUni LLM Experimental Infrastructure (FLEXI) -- Enabling Experimentation and Innovation in Higher Education Through Access to Open Large Language Models	Using the full potential of LLMs in higher education is hindered by challenges with access to LLMs. The two main access modes currently discussed are paying for a cloud-based LLM or providing a locally maintained open LLM. In this paper, we describe the current state of establishing an open LLM infrastructure at FernUn...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	474,197
cs/9512107	Rule-based Machine Learning Methods for Functional Prediction	We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	540,326
1211.5708	On Watts' Cascade Model with Random Link Weights	We study an extension of Duncan Watts' 2002 model of information cascades in social networks where edge weights are taken to be random, an innovation motivated by recent applications of cascade analysis to systemic risk in financial networks. The main result is a probabilistic analysis that characterizes the cascade in...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	19,911
1708.08739	Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs	Graphs (networks) are an important tool to model data in different domains. Real-world graphs are usually directed, where the edges have a direction and they are not symmetric. Betweenness centrality is an important index widely used to analyze networks. In this paper, first given a directed network $G$ and a vertex $r...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	79,683
1310.6338	Risk aversion as an evolutionary adaptation	Risk aversion is a common behavior universal to humans and animals alike. Economists have traditionally defined risk preferences by the curvature of the utility function. Psychologists and behavioral economists also make use of concepts such as loss aversion and probability weighting to model risk aversion. Neurophysio...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	27,958
2411.00465	Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions	Real-world offline datasets are often subject to data corruptions (such as noise or adversarial attacks) due to sensor failures or malicious attacks. Despite advances in robust offline reinforcement learning (RL), existing methods struggle to learn robust agents under high uncertainty caused by the diverse corrupted da...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	504,610
2206.11707	Cooperative Hybrid Networks with Active Relays and RISs for B5G: Applications, Challenges, and Research Directions	Among the recent advances and innovations in wireless technologies, reconfigurable intelligent surfaces (RISs) have received much attention and are envisioned to be one of the enabling technologies for beyond 5G (B5G) networks. On the other hand, active (or classical) cooperative relays have played a key role in provid...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	304,345
1911.03599	CenterFace: Joint Face Detection and Alignment Using Face as Point	Face detection and alignment in unconstrained environment is always deployed on edge devices which have limited memory storage and low computing power. This paper proposes a one-stage method named CenterFace to simultaneously predict facial box and landmark location with real-time speed and high accuracy. The proposed ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	152,690
2009.02174	Improving Self-Organizing Maps with Unsupervised Feature Extraction	The Self-Organizing Map (SOM) is a brain-inspired neural model that is very promising for unsupervised learning, especially in embedded applications. However, it is unable to learn efficient prototypes when dealing with complex datasets. We propose in this work to improve the SOM performance by using extracted features...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	194,482
2206.07912	Double Sampling Randomized Smoothing	Neural networks (NNs) are known to be vulnerable against adversarial perturbations, and thus there is a line of work aiming to provide robustness certification for NNs, such as randomized smoothing, which samples smoothing noises from a certain distribution to certify the robustness for a smoothed classifier. However, ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	302,934
2009.08824	Pedestrian Motion Tracking by Using Inertial Sensors on the Smartphone	Inertial Measurement Unit (IMU) has long been a dream for stable and reliable motion estimation, especially in indoor environments where GPS strength limits. In this paper, we propose a novel method for position and orientation estimation of a moving object only from a sequence of IMU signals collected from the phone. ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	196,358
2010.11844	Spatio-temporal Features for Generalized Detection of Deepfake Videos	For deepfake detection, video-level detectors have not been explored as extensively as image-level detectors, which do not exploit temporal data. In this paper, we empirically show that existing approaches on image and sequence classifiers generalize poorly to new manipulation techniques. To this end, we propose spatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	202,448
1807.05734	City of the People, for the People: Sensing Urban Dynamics via Social Media Interactions	Understanding the spatio-temporal dynamics of cities is in the heart of many applications including urban planning, zoning, and real-estate construction. So far, much of our understanding about urban dynamics came from traditional surveys conducted by persons or by leveraging mobile data in the form of Call Detailed Re...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	102,988
2003.00138	A Note on Latency Variability of Deep Neural Networks for Mobile Inference	Running deep neural network (DNN) inference on mobile devices, i.e., mobile inference, has become a growing trend, making inference less dependent on network connections and keeping private data locally. The prior studies on optimizing DNNs for mobile inference typically focus on the metric of average inference latency...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	166,201
1603.09732	Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions	Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	53,959
2009.03017	Non-exponentially weighted aggregation: regret bounds for unbounded loss functions	We tackle the problem of online optimization with a general, possibly unbounded, loss function. It is well known that when the loss is bounded, the exponentially weighted aggregation strategy (EWA) leads to a regret in $\sqrt{T}$ after $T$ steps. In this paper, we study a generalized aggregation strategy, where the wei...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	194,725
1304.3016	Autonomous Algorithms for Centralized and Distributed Interference Coordination: A Virtual Layer Based Approach	Interference mitigation techniques are essential for improving the performance of interference limited wireless networks. In this paper, we introduce novel interference mitigation schemes for wireless cellular networks with space division multiple access (SDMA). The schemes are based on a virtual layer that captures an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	23,787
1712.02037	Bayesian Policy Gradients via Alpha Divergence Dropout Inference	Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult. We propose an approach which instead estimates a distribution by fitting the value function with a Bayesian Neural Network. We optimize an $\alph...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	86,224
1301.1166	Quantum channels from association schemes	We propose in this note the study of quantum channels from association schemes. This is done by interpreting the $(0,1)$-matrices of a scheme as the Kraus operators of a channel. Working in the framework of one-shot zero-error information theory, we give bounds and closed formulas for various independence numbers of th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	20,834
2111.10659	Are Vision Transformers Robust to Patch Perturbations?	Recent advances in Vision Transformer (ViT) have demonstrated its impressive performance in image classification, which makes it a promising alternative to Convolutional Neural Network (CNN). Unlike CNNs, ViT represents an input image as a sequence of image patches. The patch-based input image representation makes the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	267,404
1912.09614	Features or Shape? Tackling the False Dichotomy of Time Series Classification	Time series classification is an important task in its own right, and it is often a precursor to further downstream analytics. To date, virtually all works in the literature have used either shape-based classification using a distance measure or feature-based classification after finding some suitable features for the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	158,115
2006.14285	Mobile smartphone tracing can detect almost all SARS-CoV-2 infections	Currently, many countries are considering the introduction of tracing software on mobile smartphones with the main purpose to inform and alarm the mobile app user. Here, we demonstrate that, in addition to alarming and informing, mobile tracing can detect nearly all users that are infected by SARS-CoV-2. Our algorithm ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	184,180
2408.06709	Review Learning: Advancing All-in-One Ultra-High-Definition Image Restoration Training Method	All-in-one image restoration tasks are becoming increasingly important, especially for ultra-high-definition (UHD) images. Existing all-in-one UHD image restoration methods usually boost the model's performance by introducing prompt or customized dynamized networks for different degradation types. For the inference sta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	480,310
0903.0843	Algorithms for Weighted Boolean Optimization	The Pseudo-Boolean Optimization (PBO) and Maximum Satisfiability (MaxSAT) problems are natural optimization extensions of Boolean Satisfiability (SAT). In the recent past, different algorithms have been proposed for PBO and for MaxSAT, despite the existence of straightforward mappings from PBO to MaxSAT and vice-vers...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	3,288
2103.03922	ES-Net: An Efficient Stereo Matching Network	Dense stereo matching with deep neural networks is of great interest to the research community. Existing stereo matching networks typically use slow and computationally expensive 3D convolutions to improve the performance, which is not friendly to real-world applications such as autonomous driving. In this paper, we pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	223,453
2012.00356	Better Fewer but Better: Community Search with Outliers	Given a set of vertices in a network, that we believe are of interest for the application under analysis, community search is the problem of producing a subgraph potentially explaining the relationships existing among the vertices of interest. In practice this means that the solution should add some vertices to the que...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	209,111
1205.2638	Temporal Action-Graph Games: A New Representation for Dynamic Games	In this paper we introduce temporal action graph games (TAGGs), a novel graphical representation of imperfect-information extensive form games. We show that when a game involves anonymity or context-specific utility independencies, its encoding as a TAGG can be much more compact than its direct encoding as a multiagent...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	15,944
2102.06099	Sufficiently Accurate Model Learning for Planning	Data driven models of dynamical systems help planners and controllers to provide more precise and accurate motions. Most model learning algorithms will try to minimize a loss function between the observed data and the model's predictions. This can be improved using prior knowledge about the task at hand, which can be e...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	219,639
1711.00748	Geometric k-nearest neighbor estimation of entropy and mutual information	Nonparametric estimation of mutual information is used in a wide range of scientific problems to quantify dependence between variables. The k-nearest neighbor (knn) methods are consistent, and therefore expected to work well for large sample size. These methods use geometrically regular local volume elements. This prac...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	83,773
1711.05227	Goal-Driven Query Answering for Existential Rules with Equality	Inspired by the magic sets for Datalog, we present a novel goal-driven approach for answering queries over terminating existential rules with equality (aka TGDs and EGDs). Our technique improves the performance of query answering by pruning the consequences that are not relevant for the query. This is challenging in ou...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	84,526
2003.11637	Bio-inspired Optimization: metaheuristic algorithms for optimization	In today's day and time solving real-world complex problems has become fundamentally vital and critical task. Many of these are combinatorial problems, where optimal solutions are sought rather than exact solutions. Traditional optimization methods are found to be effective for small scale problems. However, for real-w...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	false	false	169,669
cs/0607103	Ideas by Statistical Mechanics (ISM)	Ideas by Statistical Mechanics (ISM) is a generic program to model evolution and propagation of ideas/patterns throughout populations subjected to endogenous and exogenous interactions. The program is based on the author's work in Statistical Mechanics of Neocortical Interactions (SMNI), and uses the author's Adaptive ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	539,606
1508.07969	Recovery guarantees for multifrequency chirp waveforms in compressed radar sensing	Radar imaging systems transmit modulated wideband waveform to achieve high range resolution resulting in high sampling rates at the receiver proportional to the bandwidth of the transmit waveform. Analog processing techniques can be used on receive to reduce the number of measurements to N, the number of potential dela...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	46,450
2312.01431	D$^2$ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action Recognition	Adapting large pre-trained image models to few-shot action recognition has proven to be an effective and efficient strategy for learning robust feature extractors, which is essential for few-shot learning. Typical fine-tuning based adaptation paradigm is prone to overfitting in the few-shot learning scenarios and offer...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,436
2402.18927	Edge Computing Enabled Real-Time Video Analysis via Adaptive Spatial-Temporal Semantic Filtering	This paper proposes a novel edge computing enabled real-time video analysis system for intelligent visual devices. The proposed system consists of a tracking-assisted object detection module (TAODM) and a region of interesting module (ROIM). TAODM adaptively determines the offloading decision to process each video fram...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	433,622
2304.05128	Teaching Large Language Models to Self-Debug	Large language models (LLMs) have achieved impressive performance on code generation. However, for complex programming tasks, generating the correct solution in one go becomes challenging, thus some prior works have designed program repair approaches to improve code generation performance. In this work, we propose Self...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	357,499
2202.10725	Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domain	Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source domains to an unlabeled target domain. MDA is a challenging task due to the severe domain shift, which not only exists between target and source but also exists among diverse sources. Prior studies on MDA either estimate a mixed distri...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	281,641
2306.16507	Bending the Automation Bias Curve: A Study of Human and AI-based Decision Making in National Security Contexts	Uses of artificial intelligence (AI), especially those powered by machine learning approaches, are growing in sectors and societies around the world. How will AI adoption proceed, especially in the international security realm? Research on automation bias suggests that humans can often be overconfident in AI, whereas r...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	376,377
1909.07830	[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks	With substantial amount of time, resources and human (team) efforts invested to explore and develop successful deep neural networks (DNN), there emerges an urgent need to protect these inventions from being illegally copied, redistributed, or abused without respecting the intellectual properties of legitimate owners. F...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	145,790
2407.00097	Predictive accuracy of recommender algorithms	Recommender systems present a customized list of items based upon user or item characteristics with the objective of reducing a large number of possible choices to a smaller ranked set most likely to appeal to the user. A variety of algorithms for recommender systems have been developed and refined including applicatio...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	468,716
1908.03715	Differentially Private Aggregated Mobility Data Publication Using Moving Characteristics	With the rapid development of GPS enabled devices (smartphones) and location-based applications, location privacy is increasingly concerned. Intuitively, it is widely believed that location privacy can be preserved by publishing aggregated mobility data, such as the number of users in an area at some time. However, a r...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	141,301
2012.06984	Using Restricted Boltzmann Machines to Model Molecular Geometries	Precise physical descriptions of molecules can be obtained by solving the Schrodinger equation; however, these calculations are intractable and even approximations can be cumbersome. Force fields, which estimate interatomic potentials based on empirical data, are also time-consuming. This paper proposes a new methodolo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	211,302
1503.05296	Efficient Machine Learning for Big Data: A Review	With the emerging technologies and all associated devices, it is predicted that massive amount of data will be created in the next few years, in fact, as much as 90% of current data were created in the last couple of years,a trend that will continue for the foreseeable future. Sustainable computing studies the process ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	41,230
1412.6912	Coordinated Hybrid Automatic Repeat Request; Extended Version	We develop a coordinated hybrid automatic repeat request (HARQ) approach. With the proposed scheme, if a user message is correctly decoded in the first HARQ rounds, its spectrum is allocated to other users, to improve the network outage probability and the users' fairness. The results, which are obtained for single- an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	38,733
2410.03720	NeuralQP: A General Hypergraph-based Optimization Framework for Large-scale QCQPs	Machine Learning (ML) optimization frameworks have gained attention for their ability to accelerate the optimization of large-scale Quadratically Constrained Quadratic Programs (QCQPs) by learning shared problem structures. However, existing ML frameworks often rely heavily on strong problem assumptions and large-scale...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	494,912
2205.13034	EvoVGM: a Deep Variational Generative Model for Evolutionary Parameter Estimation	Most evolutionary-oriented deep generative models do not explicitly consider the underlying evolutionary dynamics of biological sequences as it is performed within the Bayesian phylogenetic inference framework. In this study, we propose a method for a deep variational Bayesian generative model (EvoVGM) that jointly app...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	298,777
2312.00232	Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks	Graph contrastive learning has shown great promise when labeled data is scarce, but large unlabeled datasets are available. However, it often does not take uncertainty estimation into account. We show that a variational Bayesian neural network approach can be used to improve not only the uncertainty estimates but also ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	411,967
2312.13211	DSFormer: Effective Compression of Text-Transformers by Dense-Sparse Weight Factorization	With the tremendous success of large transformer models in natural language understanding, down-sizing them for cost-effective deployments has become critical. Recent studies have explored the low-rank weight factorization techniques which are efficient to train, and apply out-of-the-box to any transformer architecture...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	417,237
2407.10180	Defending Against Repetitive Backdoor Attacks on Semi-supervised Learning through Lens of Rate-Distortion-Perception Trade-off	Semi-supervised learning (SSL) has achieved remarkable performance with a small fraction of labeled data by leveraging vast amounts of unlabeled data from the Internet. However, this large pool of untrusted data is extremely vulnerable to data poisoning, leading to potential backdoor attacks. Current backdoor defenses ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,873
1603.01292	Modular Decomposition and Analysis of Registration based Trackers	This paper presents a new way to study registration based trackers by decomposing them into three constituent sub modules: appearance model, state space model and search method. It is often the case that when a new tracker is introduced in literature, it only contributes to one or two of these sub modules while using e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	52,872
2303.11535	Adaptive Goal Management System of Robots	This paper considers the problem of managing single or multiple robots and proposes a cloud-based robot fleet manager, Adaptive Goal Management (AGM) System, for teams of unmanned mobile robots. The AGM system uses an adaptive goal execution approach and provides a restful API for communication between single or multip...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	352,887
2410.14894	Soft-Label Integration for Robust Toxicity Classification	Toxicity classification in textual content remains a significant problem. Data with labels from a single annotator fall short of capturing the diversity of human perspectives. Therefore, there is a growing need to incorporate crowdsourced annotations for training an effective toxicity classifier. Additionally, the stan...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	500,253
2405.20482	Sparsity regularization via tree-structured environments for disentangled representations	Many causal systems such as biological processes in cells can only be observed indirectly via measurements, such as gene expression. Causal representation learning -- the task of correctly mapping low-level observations to latent causal variables -- could advance scientific understanding by enabling inference of latent...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	459,367
1906.10991	Verifying Robustness of Gradient Boosted Models	Gradient boosted models are a fundamental machine learning technique. Robustness to small perturbations of the input is an important quality measure for machine learning models, but the literature lacks a method to prove the robustness of gradient boosted models. This work introduces VeriGB, a tool for quantifying the ...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	136,561
2002.11493	CookGAN: Meal Image Synthesis from Ingredients	In this work we propose a new computational framework, based on generative deep models, for synthesis of photo-realistic food meal images from textual list of its ingredients. Previous works on synthesis of images from text typically rely on pre-trained text models to extract text features, followed by generative neura...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	165,723
1909.08053	Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism	Recent work in language modeling demonstrates that training large transformer models advances the state of the art in Natural Language Processing applications. However, very large models can be quite difficult to train due to memory constraints. In this work, we present our techniques for training very large transforme...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	145,850
1607.05912	Simulating user learning in authoritative technology adoption: An agent based model for council-led smart meter deployment planning in the UK	How do technology users effectively transit from having zero knowledge about a technology to making the best use of it after an authoritative technology adoption? This post-adoption user learning has received little research attention in technology management literature. In this paper we investigate user learning in au...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	58,815
2402.13662	A Method For Bounding Tail Probabilities	We present a method for upper and lower bounding the right and the left tail probabilities of continuous random variables (RVs). For the right tail probability of RV $X$ with probability density function $f_X(x)$, this method requires first setting a continuous, positive, and strictly decreasing function $g_X(x)$ such ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	431,365
1709.08274	Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps	We introduce Graph-Structured Sum-Product Networks (GraphSPNs), a probabilistic approach to structured prediction for problems where dependencies between latent variables are expressed in terms of arbitrary, dynamic graphs. While many approaches to structured prediction place strict constraints on the interactions betw...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	81,442
2208.06711	Virtual Reality Platform to Develop and Test Applications on Human-Robot Social Interaction	Robotics simulation has been an integral part of research and development in the robotics area. The simulation eliminates the possibility of harm to sensors, motors, and the physical structure of a real robot by enabling robotics application testing to be carried out quickly and affordably without being subjected to me...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	312,805
1510.03380	Cyclic Communication and the Inseparability of MIMO Multi-way Relay Channels	The $K$-user MIMO multi-way relay channel (Y-channel) consisting of $K$ users with $M$ antennas each and a common relay node with $N$ antennas is studied in this paper. Each user wants to exchange messages with all the other users via the relay. A transmission strategy is proposed for this channel. The proposed strateg...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	47,833

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