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
1408.0765	Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network	A novel Bayesian modulation classification scheme is proposed for a single-antenna system over frequency-selective fading channels. The method is based on Gibbs sampling as applied to a latent Dirichlet Bayesian network (BN). The use of the proposed latent Dirichlet BN provides a systematic solution to the convergence ...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	35,109
2001.08805	Inexpensive and Portable System for Dexterous High-Density Myoelectric Control of Multiarticulate Prostheses	Multiarticulate bionic arms are now capable of mimicking the endogenous movements of the human hand. 3D-printing has reduced the cost of prosthetic hands themselves, but there is currently no low-cost alternative to dexterous electromyographic (EMG) control systems. To address this need, we developed an inexpensive (~$...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	161,386
1809.10012	Using Neural Networks to Generate Information Maps for Mobile Sensors	Target localization is a critical task for mobile sensors and has many applications. However, generating informative trajectories for these sensors is a challenging research problem. A common method uses information maps that estimate the value of taking measurements from any point in the sensor state space. These info...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	108,806
2104.02207	Dissecting User-Perceived Latency of On-Device E2E Speech Recognition	As speech-enabled devices such as smartphones and smart speakers become increasingly ubiquitous, there is growing interest in building automatic speech recognition (ASR) systems that can run directly on-device; end-to-end (E2E) speech recognition models such as recurrent neural network transducers and their variants ha...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	228,629
2306.05239	Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition	Sampled point and voxel methods are usually employed to downsample the dense events into sparse ones. After that, one popular way is to leverage a graph model which treats the sparse points/voxels as nodes and adopts graph neural networks (GNNs) to learn the representation of event data. Although good performance can b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	372,107
2501.13375	Bridging The Multi-Modality Gaps of Audio, Visual and Linguistic for Speech Enhancement	Speech Enhancement (SE) aims to improve the quality of noisy speech. It has been shown that additional visual cues can further improve performance. Given that speech communication involves audio, visual, and linguistic modalities, it is natural to expect another performance boost by incorporating linguistic information...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	526,657
2007.11261	Contact-Implicit Trajectory Optimization using an Analytically Solvable Contact Model for Locomotion on Variable Ground	This paper presents a novel contact-implicit trajectory optimization method using an analytically solvable contact model to enable planning of interactions with hard, soft, and slippery environments. Specifically, we propose a novel contact model that can be computed in closed-form, satisfies friction cone constraints ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	188,511
2107.01238	Solving Machine Learning Problems	Can a machine learn Machine Learning? This work trains a machine learning model to solve machine learning problems from a University undergraduate level course. We generate a new training set of questions and answers consisting of course exercises, homework, and quiz questions from MIT's 6.036 Introduction to Machine L...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,413
2412.04180	SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization	Large Language Models (LLMs) exhibit impressive performance across various tasks, but deploying them for inference poses challenges. Their high resource demands often necessitate complex, costly multi-GPU pipelines, or the use of smaller, less capable models. While quantization offers a promising solution utilizing low...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	514,295
2003.00872	AlignSeg: Feature-Aligned Segmentation Networks	Aggregating features in terms of different convolutional blocks or contextual embeddings has been proven to be an effective way to strengthen feature representations for semantic segmentation. However, most of the current popular network architectures tend to ignore the misalignment issues during the feature aggregatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	166,471
2301.00427	Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation	Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,906
2401.16791	Accelerated Cloud for Artificial Intelligence (ACAI)	Training an effective Machine learning (ML) model is an iterative process that requires effort in multiple dimensions. Vertically, a single pipeline typically includes an initial ETL (Extract, Transform, Load) of raw datasets, a model training stage, and an evaluation stage where the practitioners obtain statistics of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	424,986
2404.19605	Data-Driven Invertible Neural Surrogates of Atmospheric Transmission	We present a framework for inferring an atmospheric transmission profile from a spectral scene. This framework leverages a lightweight, physics-based simulator that is automatically tuned - by virtue of autodifferentiation and differentiable programming - to construct a surrogate atmospheric profile to model the observ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	450,708
1703.08314	Interacting Conceptual Spaces I : Grammatical Composition of Concepts	The categorical compositional approach to meaning has been successfully applied in natural language processing, outperforming other models in mainstream empirical language processing tasks. We show how this approach can be generalized to conceptual space models of cognition. In order to do this, first we introduce the ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	70,560
2405.11437	The First Swahili Language Scene Text Detection and Recognition Dataset	Scene text recognition is essential in many applications, including automated translation, information retrieval, driving assistance, and enhancing accessibility for individuals with visual impairments. Much research has been done to improve the accuracy and performance of scene text detection and recognition models. H...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	455,139
2406.09200	Orthogonality and isotropy of speaker and phonetic information in self-supervised speech representations	Self-supervised speech representations can hugely benefit downstream speech technologies, yet the properties that make them useful are still poorly understood. Two candidate properties related to the geometry of the representation space have been hypothesized to correlate well with downstream tasks: (1) the degree of o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	463,819
2205.03234	Real Time On Sensor Gait Phase Detection with 0.5KB Deep Learning Model	Gait phase detection with convolution neural network provides accurate classification but demands high computational cost, which inhibits real time low power on-sensor processing. This paper presents a segmentation based gait phase detection with a width and depth downscaled U-Net like model that only needs 0.5KB model...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	295,215
1511.07902	Performance Limits of Stochastic Sub-Gradient Learning, Part I: Single Agent Case	In this work and the supporting Part II, we examine the performance of stochastic sub-gradient learning strategies under weaker conditions than usually considered in the literature. The new conditions are shown to be automatically satisfied by several important cases of interest including SVM, LASSO, and Total-Variatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	49,477
2001.10615	Indexical Cities: Articulating Personal Models of Urban Preference with Geotagged Data	How to assess the potential of liking a city or a neighborhood before ever having been there. The concept of urban quality has until now pertained to global city ranking, where cities are evaluated under a grid of given parameters, or either to empirical and sociological approaches, often constrained by the amount of a...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	161,860
1906.07748	Joint Learning of Geometric and Probabilistic Constellation Shaping	The choice of constellations largely affects the performance of communication systems. When designing constellations, both the locations and probability of occurrence of the points can be optimized. These approaches are referred to as geometric and probabilistic shaping, respectively. Usually, the geometry of the const...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	135,669
1103.0769	Sparse Volterra and Polynomial Regression Models: Recoverability and Estimation	Volterra and polynomial regression models play a major role in nonlinear system identification and inference tasks. Exciting applications ranging from neuroscience to genome-wide association analysis build on these models with the additional requirement of parsimony. This requirement has high interpretative value, but ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	9,470
1607.03665	Energy- and Spectral-Efficiency Tradeoff in Full-Duplex Communications	This paper investigates the tradeoff between energyefficiency (EE) and spectral-efficiency (SE) for full-duplex (FD) enabled cellular networks.We assume that small cell base stations are working in the FD mode while user devices still work in the conventional half-duplex (HD) mode. First, a necessary condition for a FD...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	58,541
2007.05720	ECML: An Ensemble Cascade Metric Learning Mechanism towards Face Verification	Face verification can be regarded as a 2-class fine-grained visual recognition problem. Enhancing the feature's discriminative power is one of the key problems to improve its performance. Metric learning technology is often applied to address this need, while achieving a good tradeoff between underfitting and overfitti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	186,764
2110.04261	Extragradient Method: $O(1/K)$ Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity	Extragradient method (EG) (Korpelevich, 1976) is one of the most popular methods for solving saddle point and variational inequalities problems (VIP). Despite its long history and significant attention in the optimization community, there remain important open questions about convergence of EG. In this paper, we resolv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	259,820
2101.11222	Automatic image annotation base on Naive Bayes and Decision Tree classifiers using MPEG-7	Recently it has become essential to search for and retrieve high-resolution and efficient images easily due to swift development of digital images, many present annotation algorithms facing a big challenge which is the variance for represent the image where high level represent image semantic and low level illustrate t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	217,201
2202.09777	An Analysis of Complex-Valued CNNs for RF Data-Driven Wireless Device Classification	Recent deep neural network-based device classification studies show that complex-valued neural networks (CVNNs) yield higher classification accuracy than real-valued neural networks (RVNNs). Although this improvement is (intuitively) attributed to the complex nature of the input RF data (i.e., IQ symbols), no prior wor...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	281,315
1802.00093	Cross-domain CNN for Hyperspectral Image Classification	In this paper, we address the dataset scarcity issue with the hyperspectral image classification. As only a few thousands of pixels are available for training, it is difficult to effectively learn high-capacity Convolutional Neural Networks (CNNs). To cope with this problem, we propose a novel cross-domain CNN containi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	89,342
2107.03909	Weight Reparametrization for Budget-Aware Network Pruning	Pruning seeks to design lightweight architectures by removing redundant weights in overparameterized networks. Most of the existing techniques first remove structured sub-networks (filters, channels,...) and then fine-tune the resulting networks to maintain a high accuracy. However, removing a whole structure is a stro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,289
2207.11889	Salient Object Detection for Point Clouds	This paper researches the unexplored task-point cloud salient object detection (SOD). Differing from SOD for images, we find the attention shift of point clouds may provoke saliency conflict, i.e., an object paradoxically belongs to salient and non-salient categories. To eschew this issue, we present a novel view-depen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,815
2405.17921	Towards Clinical AI Fairness: Filling Gaps in the Puzzle	The ethical integration of Artificial Intelligence (AI) in healthcare necessitates addressing fairness-a concept that is highly context-specific across medical fields. Extensive studies have been conducted to expand the technical components of AI fairness, while tremendous calls for AI fairness have been raised from he...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	458,189
2501.17688	ContourFormer:Real-Time Contour-Based End-to-End Instance Segmentation Transformer	This paper presents Contourformer, a real-time contour-based instance segmentation algorithm. The method is fully based on the DETR paradigm and achieves end-to-end inference through iterative and progressive mechanisms to optimize contours. To improve efficiency and accuracy, we develop two novel techniques: sub-conto...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	528,419
2404.04452	Vision transformers in domain adaptation and domain generalization: a study of robustness	Deep learning models are often evaluated in scenarios where the data distribution is different from those used in the training and validation phases. The discrepancy presents a challenge for accurately predicting the performance of models once deployed on the target distribution. Domain adaptation and generalization ar...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	444,648
2210.16643	XNOR-FORMER: Learning Accurate Approximations in Long Speech Transformers	Transformers are among the state of the art for many tasks in speech, vision, and natural language processing, among others. Self-attentions, which are crucial contributors to this performance have quadratic computational complexity, which makes training on longer input sequences challenging. Prior work has produced st...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	327,407
2005.12439	Personalized Fashion Recommendation from Personal Social Media Data: An Item-to-Set Metric Learning Approach	With the growth of online shopping for fashion products, accurate fashion recommendation has become a critical problem. Meanwhile, social networks provide an open and new data source for personalized fashion analysis. In this work, we study the problem of personalized fashion recommendation from social media data, i.e....	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	true	178,725
0903.4545	Computer- and robot-assisted Medical Intervention	Medical robotics includes assistive devices used by the physician in order to make his/her diagnostic or therapeutic practices easier and more efficient. This chapter focuses on such systems. It introduces the general field of Computer-Assisted Medical Interventions, its aims, its different components and describes the...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	3,420
2308.16539	On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions	Game theory offers an interpretable mathematical framework for modeling multi-agent interactions. However, its applicability in real-world robotics applications is hindered by several challenges, such as unknown agents' preferences and goals. To address these challenges, we show a connection between differential games,...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	true	false	false	true	389,027
1910.05563	On the expected behaviour of noise regularised deep neural networks as Gaussian processes	Recent work has established the equivalence between deep neural networks and Gaussian processes (GPs), resulting in so-called neural network Gaussian processes (NNGPs). The behaviour of these models depends on the initialisation of the corresponding network. In this work, we consider the impact of noise regularisation ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	149,102
1910.08108	Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation	Recent studies on the adversarial vulnerability of neural networks have shown that models trained with the objective of minimizing an upper bound on the worst-case loss over all possible adversarial perturbations improve robustness against adversarial attacks. Beside exploiting adversarial training framework, we show t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	149,776
0807.0042	A Simple Converse Proof and a Unified Capacity Formula for Channels with Input Constraints	Given the single-letter capacity formula and the converse proof of a channel without constraints, we provide a simple approach to extend the results for the same channel but with constraints. The resulting capacity formula is the minimum of a Lagrange dual function. It gives an unified formula in the sense that it work...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,019
2103.09942	Machine Vision based Sample-Tube Localization for Mars Sample Return	A potential Mars Sample Return (MSR) architecture is being jointly studied by NASA and ESA. As currently envisioned, the MSR campaign consists of a series of 3 missions: sample cache, fetch and return to Earth. In this paper, we focus on the fetch part of the MSR, and more specifically the problem of autonomously detec...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	225,295
2406.07848	Multi-agent Reinforcement Learning with Deep Networks for Diverse Q-Vectors	Multi-agent reinforcement learning (MARL) has become a significant research topic due to its ability to facilitate learning in complex environments. In multi-agent tasks, the state-action value, commonly referred to as the Q-value, can vary among agents because of their individual rewards, resulting in a Q-vector. Dete...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	463,238
2410.08553	Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications	This research addresses privacy protection in Natural Language Processing (NLP) by introducing a novel algorithm based on differential privacy, aimed at safeguarding user data in common applications such as chatbots, sentiment analysis, and machine translation. With the widespread application of NLP technology, the sec...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	497,165
2011.06752	Critic PI2: Master Continuous Planning via Policy Improvement with Path Integrals and Deep Actor-Critic Reinforcement Learning	Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods from AlphaGo to Muzero have enjoyed huge success in discrete domains, such as chess and Go. Unfortunately, in real-world applications like robot control and inve...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	206,329
1802.00393	Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior	In recent years, offensive, abusive and hateful language, sexism, racism and other types of aggressive and cyberbullying behavior have been manifesting with increased frequency, and in many online social media platforms. In fact, past scientific work focused on studying these forms in popular media, such as Facebook an...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	89,407
2002.06345	Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images	Instance segmentation is an important task for biomedical and biological image analysis. Due to the complicated background components, the high variability of object appearances, numerous overlapping objects, and ambiguous object boundaries, this task still remains challenging. Recently, deep learning based methods hav...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	164,164
2404.18228	TextGram: Towards a better domain-adaptive pretraining	For green AI, it is crucial to measure and reduce the carbon footprint emitted during the training of large language models. In NLP, performing pre-training on Transformer models requires significant computational resources. This pre-training involves using a large amount of text data to gain prior knowledge for perfor...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	450,180
2104.11554	Sketch-based Normal Map Generation with Geometric Sampling	Normal map is an important and efficient way to represent complex 3D models. A designer may benefit from the auto-generation of high quality and accurate normal maps from freehand sketches in 3D content creation. This paper proposes a deep generative model for generating normal maps from users sketch with geometric sam...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	231,946
2209.03615	IMAP: Individual huMAn mobility Patterns visualizing platform	Understanding human mobility is essential for the development of smart cities and social behavior research. Human mobility models may be used in numerous applications, including pandemic control, urban planning, and traffic management. The existing models' accuracy in predicting users' mobility patterns is less than 25...	true	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true	316,543
2306.09129	Deep Learning for Energy Time-Series Analysis and Forecasting	Energy time-series analysis describes the process of analyzing past energy observations and possibly external factors so as to predict the future. Different tasks are involved in the general field of energy time-series analysis and forecasting, with electric load demand forecasting, personalized energy consumption fore...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	373,690
2311.00055	Rethinking Pre-Training in Tabular Data: A Neighborhood Embedding Perspective	Pre-training is prevalent in deep learning for vision and text data, leveraging knowledge from other datasets to enhance downstream tasks. However, for tabular data, the inherent heterogeneity in attribute and label spaces across datasets complicates the learning of shareable knowledge. We propose Tabular data Pre-Trai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	404,494
2006.07862	Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits	We study the problem of zero-order optimization of a strongly convex function. The goal is to find the minimizer of the function by a sequential exploration of its values, under measurement noise. We study the impact of higher order smoothness properties of the function on the optimization error and on the cumulative r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,980
2204.03354	Predictive coding and stochastic resonance as fundamental principles of auditory perception	How is information processed in the brain during perception? Mechanistic insight is achieved only when experiments are employed to test formal or computational models. In analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying auditory percepti...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	290,277
cs/0609097	Traveing Salesperson Problems for a double integrator	In this paper we propose some novel path planning strategies for a double integrator with bounded velocity and bounded control inputs. First, we study the following version of the Traveling Salesperson Problem (TSP): given a set of points in $\real^d$, find the fastest tour over the point set for a double integrator. W...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	539,710
1707.06002	Argotario: Computational Argumentation Meets Serious Games	An important skill in critical thinking and argumentation is the ability to spot and recognize fallacies. Fallacious arguments, omnipresent in argumentative discourse, can be deceptive, manipulative, or simply leading to `wrong moves' in a discussion. Despite their importance, argumentation scholars and NLP researchers...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	77,338
2408.12814	From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels	Scribble-based weakly supervised segmentation techniques offer comparable performance to fully supervised methods while significantly reducing annotation costs, making them an appealing alternative. Existing methods often rely on auxiliary tasks to enforce semantic consistency and use hard pseudo labels for supervision...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	482,898
1205.2601	Most Relevant Explanation: Properties, Algorithms, and Evaluations	Most Relevant Explanation (MRE) is a method for finding multivariate explanations for given evidence in Bayesian networks [12]. This paper studies the theoretical properties of MRE and develops an algorithm for finding multiple top MRE solutions. Our study shows that MRE relies on an implicit soft relevance measure in ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	15,909
1409.4237	On Analysis And Generation Of Biologically Important Boolean Functions	Boolean networks are used to model biological networks such as gene regulatory networks. Often Boolean networks show very chaotic behavior which is sensitive to any small perturbations.In order to reduce the chaotic behavior and to attain stability in the gene regulatory network,nested canalizing functions(NCF)are best...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	36,051
2212.02931	Leveraging Different Learning Styles for Improved Knowledge Distillation in Biomedical Imaging	Learning style refers to a type of training mechanism adopted by an individual to gain new knowledge. As suggested by the VARK model, humans have different learning preferences, like Visual (V), Auditory (A), Read/Write (R), and Kinesthetic (K), for acquiring and effectively processing information. Our work endeavors t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	334,937
2208.05577	Reducing Retraining by Recycling Parameter-Efficient Prompts	Parameter-efficient methods are able to use a single frozen pre-trained large language model (LLM) to perform many tasks by learning task-specific soft prompts that modulate model behavior when concatenated to the input text. However, these learned prompts are tightly coupled to a given frozen model -- if the model is ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	312,434
2112.05559	Collaborative Learning over Wireless Networks: An Introductory Overview	In this chapter, we will mainly focus on collaborative training across wireless devices. Training a ML model is equivalent to solving an optimization problem, and many distributed optimization algorithms have been developed over the last decades. These distributed ML algorithms provide data locality; that is, a joint m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	270,877
2111.14821	End-to-End Referring Video Object Segmentation with Multimodal Transformers	The referring video object segmentation task (RVOS) involves segmentation of a text-referred object instance in the frames of a given video. Due to the complex nature of this multimodal task, which combines text reasoning, video understanding, instance segmentation and tracking, existing approaches typically rely on so...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	268,718
0810.3451	The many faces of optimism - Extended version	The exploration-exploitation dilemma has been an intriguing and unsolved problem within the framework of reinforcement learning. "Optimism in the face of uncertainty" and model building play central roles in advanced exploration methods. Here, we integrate several concepts and obtain a fast and simple algorithm. We sho...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	2,526
2401.07606	RedEx: Beyond Fixed Representation Methods via Convex Optimization	Optimizing Neural networks is a difficult task which is still not well understood. On the other hand, fixed representation methods such as kernels and random features have provable optimization guarantees but inferior performance due to their inherent inability to learn the representations. In this paper, we aim at bri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	421,607
2308.04259	Generalized Forgetting Recursive Least Squares: Stability and Robustness Guarantees	This work presents generalized forgetting recursive least squares (GF-RLS), a generalization of recursive least squares (RLS) that encompasses many extensions of RLS as special cases. First, sufficient conditions are presented for the 1) Lyapunov stability, 2) uniform Lyapunov stability, 3) global asymptotic stability,...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	384,346
2002.01924	Explicit Wiretap Channel Codes via Source Coding, Universal Hashing, and Distribution Approximation, When the Channels' Statistics are Uncertain	We consider wiretap channels with uncertainty on the eavesdropper channel under (i) noisy blockwise type II, (ii) compound, or (iii) arbitrarily varying models. We present explicit wiretap codes that can handle these models in a unified manner and only rely on three primitives, namely source coding with side informatio...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	162,776
2008.03720	Disentangled Multidimensional Metric Learning for Music Similarity	Music similarity search is useful for a variety of creative tasks such as replacing one music recording with another recording with a similar "feel", a common task in video editing. For this task, it is typically necessary to define a similarity metric to compare one recording to another. Music similarity, however, is ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	191,012
2206.00489	Attack-Agnostic Adversarial Detection	The growing number of adversarial attacks in recent years gives attackers an advantage over defenders, as defenders must train detectors after knowing the types of attacks, and many models need to be maintained to ensure good performance in detecting any upcoming attacks. We propose a way to end the tug-of-war between ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	300,141
2309.07550	Naturalistic Robot Arm Trajectory Generation via Representation Learning	The integration of manipulator robots in household environments suggests a need for more predictable and human-like robot motion. This holds especially true for wheelchair-mounted assistive robots that can support the independence of people with paralysis. One method of generating naturalistic motion trajectories is vi...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	391,825
2308.12264	Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement	With the increasing usage, scale, and complexity of Deep Learning (DL) models, their rapidly growing energy consumption has become a critical concern. Promoting green development and energy awareness at different granularities is the need of the hour to limit carbon emissions of DL systems. However, the lack of standar...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	387,476
2212.09429	On the Complexity of Representation Learning in Contextual Linear Bandits	In contextual linear bandits, the reward function is assumed to be a linear combination of an unknown reward vector and a given embedding of context-arm pairs. In practice, the embedding is often learned at the same time as the reward vector, thus leading to an online representation learning problem. Existing approache...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	337,109
2206.04405	Conformal Off-Policy Prediction in Contextual Bandits	Most off-policy evaluation methods for contextual bandits have focused on the expected outcome of a policy, which is estimated via methods that at best provide only asymptotic guarantees. However, in many applications, the expectation may not be the best measure of performance as it does not capture the variability of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,614
1708.08142	Study of Set-Membership Kernel Adaptive Algorithms and Applications	Adaptive algorithms based on kernel structures have been a topic of significant research over the past few years. The main advantage is that they form a family of universal approximators, offering an elegant solution to problems with nonlinearities. Nevertheless these methods deal with kernel expansions, creating a gro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	79,599
1805.10105	Effects of Social Bots in the Iran-Debate on Twitter	2018 started with massive protests in Iran, bringing back the impressions of the so called "Arab Spring" and it's revolutionary impact for the Maghreb states, Syria and Egypt. Many reports and scientific examinations considered online social networks (OSN's) such as Twitter or Facebook to play a critical role in the op...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	98,580
1612.09534	Channel Measurements and Models for High-Speed Train Wireless Communication Systems in Tunnel Scenarios: A Survey	The rapid developments of high-speed trains (HSTs) introduce new challenges to HST wireless communication systems. Realistic HST channel models play a critical role in designing and evaluating HST communication systems. Due to the length limitation, bounding of tunnel itself, and waveguide effect, channel characteristi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	66,202
1712.06365	'Indifference' methods for managing agent rewards	`Indifference' refers to a class of methods used to control reward based agents. Indifference techniques aim to achieve one or more of three distinct goals: rewards dependent on certain events (without the agent being motivated to manipulate the probability of those events), effective disbelief (where agents behave as ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	86,879
2303.10311	On the rise of fear speech in online social media	Recently, social media platforms are heavily moderated to prevent the spread of online hate speech, which is usually fertile in toxic words and is directed toward an individual or a community. Owing to such heavy moderation, newer and more subtle techniques are being deployed. One of the most striking among these is fe...	false	false	false	true	false	false	false	false	true	false	false	false	false	true	false	false	false	false	352,389
1412.3697	Hybrid recommendation methods in complex networks	We propose here two new recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three relevant data sets, and we comp...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	38,311
2408.08892	Leveraging Large Language Models for Enhanced Process Model Comprehension	In Business Process Management (BPM), effectively comprehending process models is crucial yet poses significant challenges, particularly as organizations scale and processes become more complex. This paper introduces a novel framework utilizing the advanced capabilities of Large Language Models (LLMs) to enhance the in...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	481,193
1411.4044	Benchmarking DataStax Enterprise/Cassandra with HiBench	This report evaluates the new analytical capabilities of DataStax Enterprise (DSE) [1] through the use of standard Hadoop workloads. In particular, we run experiments with CPU and I/O bound micro-benchmarks as well as OLAP-style analytical query workloads. The performed tests should show that DSE is capable of successf...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	37,570
1808.06148	Generalized Bregman and Jensen divergences which include some f-divergences	In this paper, we introduce new classes of divergences by extending the definitions of the Bregman divergence and the skew Jensen divergence. These new divergence classes (g-Bregman divergence and skew g-Jensen divergence) satisfy some properties similar to the Bregman or skew Jensen divergence. We show these g-diverge...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	105,474
2408.09908	$p$SVM: Soft-margin SVMs with $p$-norm Hinge Loss	Support Vector Machines (SVMs) based on hinge loss have been extensively discussed and applied to various binary classification tasks. These SVMs achieve a balance between margin maximization and the minimization of slack due to outliers. Although many efforts have been dedicated to enhancing the performance of SVMs wi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	481,641
0805.4112	On the entropy and log-concavity of compound Poisson measures	Motivated, in part, by the desire to develop an information-theoretic foundation for compound Poisson approximation limit theorems (analogous to the corresponding developments for the central limit theorem and for simple Poisson approximation), this work examines sufficient conditions under which the compound Poisson d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,832
2211.05985	Using Persuasive Writing Strategies to Explain and Detect Health Misinformation	Nowadays, the spread of misinformation is a prominent problem in society. Our research focuses on aiding the automatic identification of misinformation by analyzing the persuasive strategies employed in textual documents. We introduce a novel annotation scheme encompassing common persuasive writing tactics to achieve o...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	329,738
1806.08279	Don't only Feel Read: Using Scene text to understand advertisements	We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,131
2412.10009	Class flipping for uplift modeling and Heterogeneous Treatment Effect estimation on imbalanced RCT data	Uplift modeling and Heterogeneous Treatment Effect (HTE) estimation aim at predicting the causal effect of an action, such as a medical treatment or a marketing campaign on a specific individual. In this paper, we focus on data from Randomized Controlled Experiments which guarantee causal interpretation of the outcomes...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	516,754
2003.04630	Lagrangian Neural Networks	Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet even though neural network models see increasing use in the physical sciences, they struggle to learn these symmetries. In this paper, we p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	167,605
1712.06139	TensorFlow-Serving: Flexible, High-Performance ML Serving	We describe TensorFlow-Serving, a system to serve machine learning models inside Google which is also available in the cloud and via open-source. It is extremely flexible in terms of the types of ML platforms it supports, and ways to integrate with systems that convey new models and updated versions from training to se...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	86,839
1604.06648	Automatic verbal aggression detection for Russian and American imageboards	The problem of aggression for Internet communities is rampant. Anonymous forums usually called imageboards are notorious for their aggressive and deviant behaviour even in comparison with other Internet communities. This study is aimed at studying ways of automatic detection of verbal expression of aggression for the m...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	54,973
1606.07729	On Lossless Feedback Delay Networks	Lossless Feedback Delay Networks (FDNs) are commonly used as a design prototype for artificial reverberation algorithms. The lossless property is dependent on the feedback matrix, which connects the output of a set of delays to their inputs, and the lengths of the delays. Both, unitary and triangular feedback matrices ...	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	57,771
2108.06009	SAR image matching algorithm based on multi-class features	Synthetic aperture radar has the ability to work 24/7 and 24/7, and has high application value. Propose a new SAR image matching algorithm based on multi class features, mainly using two different types of features: straight lines and regions to enhance the robustness of the matching algorithm; On the basis of using pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,476
2309.02534	Experience and Prediction: A Metric of Hardness for a Novel Litmus Test	In the last decade, the Winograd Schema Challenge (WSC) has become a central aspect of the research community as a novel litmus test. Consequently, the WSC has spurred research interest because it can be seen as the means to understand human behavior. In this regard, the development of new techniques has made possible ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	390,069
2411.14347	DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding	In this paper, we introduce DINO-X, which is a unified object-centric vision model developed by IDEA Research with the best open-world object detection performance to date. DINO-X employs the same Transformer-based encoder-decoder architecture as Grounding DINO 1.5 to pursue an object-level representation for open-worl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	510,119
1401.3872	Second-Order Consistencies	In this paper, we propose a comprehensive study of second-order consistencies (i.e., consistencies identifying inconsistent pairs of values) for constraint satisfaction. We build a full picture of the relationships existing between four basic second-order consistencies, namely path consistency (PC), 3-consistency (3C),...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	29,986
1110.6650	Summarization and Matching of Density-Based Clusters in Streaming Environments	Density-based cluster mining is known to serve a broad range of applications ranging from stock trade analysis to moving object monitoring. Although methods for efficient extraction of density-based clusters have been studied in the literature, the problem of summarizing and matching of such clusters with arbitrary sha...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	12,823
1912.11894	Analysis of Reference and Citation Copying in Evolving Bibliographic Networks	Extensive literature demonstrates how the copying of references (links) can lead to the emergence of various structural properties (e.g., power-law degree distribution and bipartite cores) in bibliographic and other similar directed networks. However, it is also well known that the copying process is incapable of mimic...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	158,693
2206.12839	Repository-Level Prompt Generation for Large Language Models of Code	With the success of large language models (LLMs) of code and their use as code assistants (e.g. Codex used in GitHub Copilot), techniques for introducing domain-specific knowledge in the prompt design process become important. In this work, we propose a framework called Repo-Level Prompt Generator that learns to genera...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	304,751
2302.06834	Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization	Learning Markov decision processes (MDP) in an adversarial environment has been a challenging problem. The problem becomes even more challenging with function approximation, since the underlying structure of the loss function and transition kernel are especially hard to estimate in a varying environment. In fact, the s...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	345,546
1803.04375	A Feature-Rich Vietnamese Named-Entity Recognition Model	In this paper, we present a feature-based named-entity recognition (NER) model that achieves the start-of-the-art accuracy for Vietnamese language. We combine word, word-shape features, PoS, chunk, Brown-cluster-based features, and word-embedding-based features in the Conditional Random Fields (CRF) model. We also expl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	92,447
1904.07964	3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders	We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs. The approach learns an encoding of the samples in the training corpus using an unsupervised variational autoencoder-decoder architecture, withou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	127,922
1811.12569	Are All Training Examples Created Equal? An Empirical Study	Modern computer vision algorithms often rely on very large training datasets. However, it is conceivable that a carefully selected subsample of the dataset is sufficient for training. In this paper, we propose a gradient-based importance measure that we use to empirically analyze relative importance of training images ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	115,042

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