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
2011.04521	Automated Discovery of Mathematical Definitions in Text with Deep Neural Networks	Automatic definition extraction from texts is an important task that has numerous applications in several natural language processing fields such as summarization, analysis of scientific texts, automatic taxonomy generation, ontology generation, concept identification, and question answering. For definitions that are c...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	205,613
1911.05071	Experience-Embedded Visual Foresight	Visual foresight gives an agent a window into the future, which it can use to anticipate events before they happen and plan strategic behavior. Although impressive results have been achieved on video prediction in constrained settings, these models fail to generalize when confronted with unfamiliar real-world objects. ...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	153,158
2405.05445	Large Language Model Enhanced Machine Learning Estimators for Classification	Pre-trained large language models (LLM) have emerged as a powerful tool for simulating various scenarios and generating output given specific instructions and multimodal input. In this work, we analyze the specific use of LLM to enhance a classical supervised machine learning method for classification problems. We prop...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	452,917
1602.04396	Optimal Sample Complexity for Stable Matrix Recovery	Tremendous efforts have been made to study the theoretical and algorithmic aspects of sparse recovery and low-rank matrix recovery. This paper fills a theoretical gap in matrix recovery: the optimal sample complexity for stable recovery without constants or log factors. We treat sparsity, low-rankness, and potentially ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	52,122
1901.10576	Boolean Functions with Biased Inputs: Approximation and Noise Sensitivity	This paper considers the problem of approximating a Boolean function $f$ using another Boolean function from a specified class. Two classes of approximating functions are considered: $k$-juntas, and linear Boolean functions. The $n$ input bits of the function are assumed to be independently drawn from a distribution th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	120,051
2306.12737	Ladder Fine-tuning approach for SAM integrating complementary network	Recently, foundation models have been introduced demonstrating various tasks in the field of computer vision. These models such as Segment Anything Model (SAM) are generalized models trained using huge datasets. Currently, ongoing research focuses on exploring the effective utilization of these generalized models for s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	375,050
1610.05419	A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques	In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both me...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	62,505
cs/0007010	Boosting Applied to Word Sense Disambiguation	In this paper Schapire and Singer's AdaBoost.MH boosting algorithm is applied to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar-based approaches, which represent state-of-the-art accuracy on sup...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	537,153
2412.03825	Residual Hyperbolic Graph Convolution Networks	Hyperbolic graph convolutional networks (HGCNs) have demonstrated representational capabilities of modeling hierarchical-structured graphs. However, as in general GCNs, over-smoothing may occur as the number of model layers increases, limiting the representation capabilities of most current HGCN models. In this paper, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	514,123
2301.02126	CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization	Unsupervised anomaly detection in medical imaging aims to detect and localize arbitrary anomalies without requiring annotated anomalous data during training. Often, this is achieved by learning a data distribution of normal samples and detecting anomalies as regions in the image which deviate from this distribution. Mo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	339,420
1811.11375	Instance-level Sketch-based Retrieval by Deep Triplet Classification Siamese Network	Sketch has been employed as an effective communicative tool to express the abstract and intuitive meanings of object. Recognizing the free-hand sketch drawing is extremely useful in many real-world applications. While content-based sketch recognition has been studied for several decades, the instance-level Sketch-Based...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	114,763
1008.3608	Crystallized Rates Region of the Interference Channel via Correlated Equilibrium with Interference as Noise	Treating the interference as noise in the n-user interference channel, the paper describes a novel approach to the rates region, composed by the time-sharing convex hull of 2^n-1 corner points achieved through On/Off binary power control. The resulting rates region is denoted crystallized rates region. By treating the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,323
1906.08717	A New Statistical Approach for Comparing Algorithms for Lexicon Based Sentiment Analysis	Lexicon based sentiment analysis usually relies on the identification of various words to which a numerical value corresponding to sentiment can be assigned. In principle, classifiers can be obtained from these algorithms by comparison with human annotation, which is considered the gold standard. In practise this is di...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	135,949
2501.06781	Eliza: A Web3 friendly AI Agent Operating System	AI Agent, powered by large language models (LLMs) as its cognitive core, is an intelligent agentic system capable of autonomously controlling and determining the execution paths under user's instructions. With the burst of capabilities of LLMs and various plugins, such as RAG, text-to-image/video/3D, etc., the potentia...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	524,124
1701.01326	Higher Order Context Transformations	The context transformation and generalized context transformation methods, we introduced recently, were able to reduce zero order entropy by exchanging digrams, and as a consequence, they were removing mutual information between consecutive symbols of the input message. These transformations were intended to be used as...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	66,391
0910.5537	An Analysis of Phase Synchronization Mismatch Sensitivity for Coherent MIMO Radar Systems	In this study, the hybrid Cramer-Rao bound (CRB) is developed for target localization, to establish the sensitivity of the estimation mean-square error (MSE) to the level of phase synchronization mismatch in coherent Multiple-Input Multiple-Output (MIMO) radar systems with widely distributed antennas. The lower bound o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,820
1706.06525	A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification	In Acoustic Scene Classification (ASC) two major approaches have been followed . While one utilizes engineered features such as mel-frequency-cepstral-coefficients (MFCCs), the other uses learned features that are the outcome of an optimization algorithm. I-vectors are the result of a modeling technique that usually ta...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	75,692
2212.03182	Overlapping oriented imbalanced ensemble learning method based on projective clustering and stagewise hybrid sampling	The challenge of imbalanced learning lies not only in class imbalance problem, but also in the class overlapping problem which is complex. However, most of the existing algorithms mainly focus on the former. The limitation prevents the existing methods from breaking through. To address this limitation, this paper propo...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	335,019
2106.02584	Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning	We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input. To this end, we introduce a general-purpose deep learning architecture that takes as input the entire dataset instead of processing one datapoin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	238,930
2112.15458	Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network	Efficiently and accurately detecting people from 3D point cloud data is of great importance in many robotic and autonomous driving applications. This fundamental perception task is still very challenging due to (i) significant deformations of human body pose and gesture over time and (ii) point cloud sparsity and scarc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	273,792
2204.12165	When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?	Word alignment has proven to benefit many-to-many neural machine translation (NMT). However, high-quality ground-truth bilingual dictionaries were used for pre-editing in previous methods, which are unavailable for most language pairs. Meanwhile, the contrastive objective can implicitly utilize automatically learned wo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	293,396
1506.04089	Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences	We propose a neural sequence-to-sequence model for direction following, a task that is essential to realizing effective autonomous agents. Our alignment-based encoder-decoder model with long short-term memory recurrent neural networks (LSTM-RNN) translates natural language instructions to action sequences based upon a ...	false	false	false	false	true	false	true	true	true	false	false	false	false	false	false	true	false	false	44,124
1804.10435	Regularized Nonparametric Volterra Kernel Estimation	In this paper, the regularization approach introduced recently for nonparametric estimation of linear systems is extended to the estimation of nonlinear systems modelled as Volterra series. The kernels of order higher than one, representing higher dimensional impulse responses in the series, are considered to be realiz...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	96,161
2104.12335	Diverse Image Inpainting with Bidirectional and Autoregressive Transformers	Image inpainting is an underdetermined inverse problem, which naturally allows diverse contents to fill up the missing or corrupted regions realistically. Prevalent approaches using convolutional neural networks (CNNs) can synthesize visually pleasant contents, but CNNs suffer from limited perception fields for capturi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	232,180
2205.02662	Greedy Clustering-Based Algorithm for Improving Multi-point Robotic Manipulation Sequencing	The problem of optimizing a sequence of tasks for a robot, also known as multi-point manufacturing, is a well-studied problem. Many of these solutions use a variant of the Traveling Salesman Problem (TSP) and seek to find the minimum distance or time solution. Optimal solution methods struggle to run in real-time and s...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	295,012
1910.03552	TorchBeast: A PyTorch Platform for Distributed RL	TorchBeast is a platform for reinforcement learning (RL) research in PyTorch. It implements a version of the popular IMPALA algorithm for fast, asynchronous, parallel training of RL agents. Additionally, TorchBeast has simplicity as an explicit design goal: We provide both a pure-Python implementation ("MonoBeast") as ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	148,525
1204.1231	How Many Vote Operations Are Needed to Manipulate A Voting System?	In this paper, we propose a framework to study a general class of strategic behavior in voting, which we call vote operations. We prove the following theorem: if we fix the number of alternatives, generate $n$ votes i.i.d. according to a distribution $\pi$, and let $n$ go to infinity, then for any $\epsilon >0$, with p...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	15,299
1603.06496	Instance Influence Estimation for Hyperspectral Target Signature Characterization using Extended Functions of Multiple Instances	The Extended Functions of Multiple Instances (eFUMI) algorithm is a generalization of Multiple Instance Learning (MIL). In eFUMI, only bag level (i.e. set level) labels are needed to estimate target signatures from mixed data. The training bags in eFUMI are labeled positive if any data point in a bag contains or repres...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	53,507
2103.11622	Progressive and Aligned Pose Attention Transfer for Person Image Generation	This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. We design a progressive generator which comprises a sequence of transfer blocks. Each block performs an intermediate transfer step by modeling the relationship between the conditio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	225,876
2206.10588	Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning	The combination of Monte Carlo methods and deep learning has recently led to efficient algorithms for solving partial differential equations (PDEs) in high dimensions. Related learning problems are often stated as variational formulations based on associated stochastic differential equations (SDEs), which allow the min...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	303,963
1902.06691	Openbots	Social bots have recently gained attention in the context of public opinion manipulation on social media platforms. While a lot of research effort has been put into the classification and detection of such (semi-)automated programs, it is still unclear how sophisticated those bots actually are, which platforms they tar...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	121,817
1603.05359	Cascading Bandits for Large-Scale Recommendation Problems	Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	53,354
1905.03831	An Efficient and high accuracy finite-difference scheme for the acoustic wave equation in 3D heterogeneous media	Efficient and accurate numerical simulation of 3D acoustic wave propagation in heterogeneous media plays an important role in the success of seismic full waveform inversion (FWI) problem. In this work, we employed the combined scheme and developed a new explicit compact high-order finite difference scheme to solve the ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	130,302
1901.10084	A Parallel Projection Method for Metric Constrained Optimization	Many clustering applications in machine learning and data mining rely on solving metric-constrained optimization problems. These problems are characterized by $O(n^3)$ constraints that enforce triangle inequalities on distance variables associated with $n$ objects in a large dataset. Despite its usefulness, metric-cons...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	119,923
1908.04767	Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides	Purpose: Exercise-induced pulmonary hemorrhage (EIPH) is a common syndrome in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	141,569
2006.14091	Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences	Reward functions are a common way to specify the objective of a robot. As designing reward functions can be extremely challenging, a more promising approach is to directly learn reward functions from human teachers. Importantly, data from human teachers can be collected either passively or actively in a variety of form...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	184,121
2407.05145	On high-dimensional modifications of the nearest neighbor classifier	Nearest neighbor classifier is arguably the most simple and popular nonparametric classifier available in the literature. However, due to the concentration of pairwise distances and the violation of the neighborhood structure, this classifier often suffers in high-dimension, low-sample size (HDLSS) situations, especial...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	470,842
2405.12832	Wav-KAN: Wavelet Kolmogorov-Arnold Networks	In this paper, we introduce Wav-KAN, an innovative neural network architecture that leverages the Wavelet Kolmogorov-Arnold Networks (Wav-KAN) framework to enhance interpretability and performance. Traditional multilayer perceptrons (MLPs) and even recent advancements like Spl-KAN face challenges related to interpretab...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	455,668
1612.00593	PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation	Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	64,915
2110.08966	Computing Semilinear Sparse Models for Approximately Eventually Periodic Signals	Some elements of the theory and algorithmics corresponding to the computation of semilinear sparse models for discrete-time signals are presented. In this study, we will focus on approximately eventually periodic discrete-time signals, that is, signals that can exhibit an aperiodic behavior for an initial amount of tim...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	261,629
2303.00340	A Practical Upper Bound for the Worst-Case Attribution Deviations	Model attribution is a critical component of deep neural networks (DNNs) for its interpretability to complex models. Recent studies bring up attention to the security of attribution methods as they are vulnerable to attribution attacks that generate similar images with dramatically different attributions. Existing work...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	348,567
1507.04374	Uniform-Price Mechanism Design for a Large Population of Dynamic Agents	This paper focuses on the coordination of a large population of dynamic agents with private information over multiple periods. Each agent maximizes the individual utility, while the coordinator determines the market rule to achieve group objectives. The coordination problem is formulated as a dynamic mechanism design p...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	45,165
2012.12121	Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages	There are several domains that own corresponding widely used feature extractors, such as ResNet, BERT, and GPT-x. These models are usually pre-trained on large amounts of unlabeled data by self-supervision and can be effectively applied to downstream tasks. In the speech domain, wav2vec2.0 starts to show its powerful r...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	212,835
2408.09369	Flemme: A Flexible and Modular Learning Platform for Medical Images	As the rapid development of computer vision and the emergence of powerful network backbones and architectures, the application of deep learning in medical imaging has become increasingly significant. Unlike natural images, medical images lack huge volumes of data but feature more modalities, making it difficult to trai...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	481,403
2206.10334	Defending Adversarial Examples by Negative Correlation Ensemble	The security issues in DNNs, such as adversarial examples, have attracted much attention. Adversarial examples refer to the examples which are capable to induce the DNNs return completely predictions by introducing carefully designed perturbations. Obviously, adversarial examples bring great security risks to the devel...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	303,878
2202.10608	It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum Generation	We are interested in training general-purpose reinforcement learning agents that can solve a wide variety of goals. Training such agents efficiently requires automatic generation of a goal curriculum. This is challenging as it requires (a) exploring goals of increasing difficulty, while ensuring that the agent (b) is e...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	281,590
2009.12084	Sensor Fault Detection and Isolation via Networked Estimation: Full-Rank Dynamical Systems	This paper considers the problem of simultaneous sensor fault detection, isolation, and networked estimation of linear full-rank dynamical systems. The proposed networked estimation is a variant of single time-scale protocol and is based on (i) consensus on \textit{a-priori} estimates and (ii) measurement innovation. T...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	197,333
2403.13808	On Pretraining Data Diversity for Self-Supervised Learning	We explore the impact of training with more diverse datasets, characterized by the number of unique samples, on the performance of self-supervised learning (SSL) under a fixed computational budget. Our findings consistently demonstrate that increasing pretraining data diversity enhances SSL performance, albeit only whe...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	439,791
2110.07831	RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models	Backdoor attacks, which maliciously control a well-trained model's outputs of the instances with specific triggers, are recently shown to be serious threats to the safety of reusing deep neural networks (DNNs). In this work, we propose an efficient online defense mechanism based on robustness-aware perturbations. Speci...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	261,149
2110.05203	Tracking of stabilizing, optimal control in fixed-time based on time-varying objective function	The controller of an input-affine system is determined through minimizing a time-varying objective function, where stabilization is ensured via a Lyapunov function decay condition as constraint. This constraint is incorporated into the objective function via a barrier function. The time-varying minimum of the resulting...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	260,201
2009.12496	Modeling Dyadic Conversations for Personality Inference	Nowadays, automatical personality inference is drawing extensive attention from both academia and industry. Conventional methods are mainly based on user generated contents, e.g., profiles, likes, and texts of an individual, on social media, which are actually not very reliable. In contrast, dyadic conversations betwee...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	197,441
2211.15823	Personalized Reward Learning with Interaction-Grounded Learning (IGL)	In an era of countless content offerings, recommender systems alleviate information overload by providing users with personalized content suggestions. Due to the scarcity of explicit user feedback, modern recommender systems typically optimize for the same fixed combination of implicit feedback signals across all users...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	333,399
1906.00572	Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning	In an effort to better understand the different ways in which the discount factor affects the optimization process in reinforcement learning, we designed a set of experiments to study each effect in isolation. Our analysis reveals that the common perception that poor performance of low discount factors is caused by (to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,442
1903.07549	Reduced and Aggregated Distribution Grid Representations Approximated by Polyhedral Sets	In this paper we present a novel tractable method to compute reduced and aggregated distribution grid representations that provide an interface in the form of active and reactive power (PQ) capability areas for improving transmission service operator - distribution service operator (TSO-DSO) interactions. Based on a lo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	124,647
2008.02992	Leveraging Localization for Multi-camera Association	We present McAssoc, a deep learning approach to the as-sociation of detection bounding boxes in different views ofa multi-camera system. The vast majority of the academiahas been developing single-camera computer vision algo-rithms, however, little research attention has been directedto incorporating them into a multi-...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,777
2406.06664	ASTRA: Aligning Speech and Text Representations for Asr without Sampling	This paper introduces ASTRA, a novel method for improving Automatic Speech Recognition (ASR) through text injection.Unlike prevailing techniques, ASTRA eliminates the need for sampling to match sequence lengths between speech and text modalities. Instead, it leverages the inherent alignments learned within CTC/RNNT mod...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	462,733
2108.08405	Integrating Dialog History into End-to-End Spoken Language Understanding Systems	End-to-end spoken language understanding (SLU) systems that process human-human or human-computer interactions are often context independent and process each turn of a conversation independently. Spoken conversations on the other hand, are very much context dependent, and dialog history contains useful information that...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	251,236
2309.16397	Uncertainty-Aware Decision Transformer for Stochastic Driving Environments	Offline Reinforcement Learning (RL) enables policy learning without active interactions, making it especially appealing for self-driving tasks. Recent successes of Transformers inspire casting offline RL as sequence modeling, which, however, fails in stochastic environments with incorrect assumptions that identical act...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	395,338
2011.11376	On the application of Physically-Guided Neural Networks with Internal Variables to Continuum Problems	Predictive Physics has been historically based upon the development of mathematical models that describe the evolution of a system under certain external stimuli and constraints. The structure of such mathematical models relies on a set of hysical hypotheses that are assumed to be fulfilled by the system within a certa...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	207,809
1808.02862	A Novel Tactile Force Probe for Tissue Stiffness Classification	In this study, we have proposed a new type of tactile sensor that is capable of detecting the stiffness of soft objects. The sensor consists of a brass cylinder with an axial bore. An iron core can easily move inside the bore. Three peripheral bobbins were machined in the cylinder around which three coils have been wou...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	104,841
2411.11667	Dissecting Representation Misalignment in Contrastive Learning via Influence Function	Contrastive learning, commonly applied in large-scale multimodal models, often relies on data from diverse and often unreliable sources, which can include misaligned or mislabeled text-image pairs. This frequently leads to robustness issues and hallucinations, ultimately causing performance degradation. Data valuation ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	509,130
1711.01577	Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning	Long Short-Term Memory (LSTM) is a popular approach to boosting the ability of Recurrent Neural Networks to store longer term temporal information. The capacity of an LSTM network can be increased by widening and adding layers. However, usually the former introduces additional parameters, while the latter increases the...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	83,917
0707.0652	How to use the Scuba Diving metaphor to solve problem with neutrality ?	We proposed a new search heuristic using the scuba diving metaphor. This approach is based on the concept of evolvability and tends to exploit neutrality which exists in many real-world problems. Despite the fact that natural evolution does not directly select for evolvability, the basic idea behind the scuba search he...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	382
1810.13170	Face Presentation Attack Detection in Learned Color-liked Space	Face presentation attack detection (PAD) has become a thorny problem for biometric systems and numerous countermeasures have been proposed to address it. However, majority of them directly extract feature descriptors and distinguish fake faces from the real ones in existing color spaces (e.g. RGB, HSV and YCbCr). Unfor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	111,928
1810.07842	A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation	We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation. Compared to the commonly used Dice loss, our loss function achieves a better trade off between precision and recall when training on small structures such as lesions. To evaluate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	110,706
2008.09192	PicoDomain: A Compact High-Fidelity Cybersecurity Dataset	Analysis of cyber relevant data has become an area of increasing focus. As larger percentages of businesses and governments begin to understand the implications of cyberattacks, the impetus for better cybersecurity solutions has increased. Unfortunately, current cybersecurity datasets either offer no ground truth or do...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	192,637
1401.3836	An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data	Crowdsourcing platforms offer a practical solution to the problem of affordably annotating large datasets for training supervised classifiers. Unfortunately, poor worker performance frequently threatens to compromise annotation reliability, and requesting multiple labels for every instance can lead to large cost increa...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	29,953
2204.10959	A benchmark dataset for deep learning-based airplane detection: HRPlanes	Airplane detection from satellite imagery is a challenging task due to the complex backgrounds in the images and differences in data acquisition conditions caused by the sensor geometry and atmospheric effects. Deep learning methods provide reliable and accurate solutions for automatic detection of airplanes; however, ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	292,975
2306.17169	Enterprise Disk Drive Scrubbing Based on Mondrian Conformal Predictors	Disk scrubbing is a process aimed at resolving read errors on disks by reading data from the disk. However, scrubbing the entire storage array at once can adversely impact system performance, particularly during periods of high input/output operations. Additionally, the continuous reading of data from disks when scrubb...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	376,613
1611.09084	Hierarchical Hyperlink Prediction for the WWW	The hyperlink prediction task, that of proposing new links between webpages, can be used to improve search engines, expand the visibility of web pages, and increase the connectivity and navigability of the web. Hyperlink prediction is typically performed on webgraphs composed by thousands or millions of vertices, where...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	true	64,616
1108.4063	Backpressure with Adaptive Redundancy (BWAR)	Backpressure scheduling and routing, in which packets are preferentially transmitted over links with high queue differentials, offers the promise of throughput-optimal operation for a wide range of communication networks. However, when the traffic load is low, due to the corresponding low queue occupancy, backpressure ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	11,741
0907.2090	Some bounds on the capacity of communicating the sum of sources	We consider directed acyclic networks with multiple sources and multiple terminals where each source generates one i.i.d. random process over an abelian group and all the terminals want to recover the sum of these random processes. The different source processes are assumed to be independent. The solvability of such ne...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,091
1908.10999	Spectral Regularization for Combating Mode Collapse in GANs	Despite excellent progress in recent years, mode collapse remains a major unsolved problem in generative adversarial networks (GANs).In this paper, we present spectral regularization for GANs (SR-GANs), a new and robust method for combating the mode collapse problem in GANs. Theoretical analysis shows that the optimal ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	143,263
2304.02941	Dr. KID: Direct Remeshing and K-set Isometric Decomposition for Scalable Physicalization of Organic Shapes	Dr. KID is an algorithm that uses isometric decomposition for the physicalization of potato-shaped organic models in a puzzle fashion. The algorithm begins with creating a simple, regular triangular surface mesh of organic shapes, followed by iterative k-means clustering and remeshing. For clustering, we need similarit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	356,617
2412.13477	Generating Unseen Nonlinear Evolution in Sea Surface Temperature Using a Deep Learning-Based Latent Space Data Assimilation Framework	Advances in data assimilation (DA) methods have greatly improved the accuracy of Earth system predictions. To fuse multi-source data and reconstruct the nonlinear evolution missing from observations, geoscientists are developing future-oriented DA methods. In this paper, we redesign a purely data-driven latent space DA...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	518,301
2205.09115	AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications	Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment. In this paper, we investigate a proof-of-concept approach using automated quantum machine learning (AutoQML) framework called AutoAnsatz to recognize human gesture. We address h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	297,159
2111.09450	See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation	Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects. This leads to performance degradation when 3D detectors trained for one lidar are tested on other types of lidars. Remarkable progress in lidar manufacturing has brought about advances i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	267,013
1605.03506	Characterizing Quantifier Fuzzification Mechanisms: a behavioral guide for practical applications	Important advances have been made in the fuzzy quantification field. Nevertheless, some problems remain when we face the decision of selecting the most convenient model for a specific application. In the literature, several desirable adequacy properties have been proposed, but theoretical limits impede quantification m...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	55,755
2412.01933	Recurrent Neural Network on PICTURE Model	Intensive Care Units (ICUs) provide critical care and life support for most severely ill and injured patients in the hospital. With the need for ICUs growing rapidly and unprecedentedly, especially during COVID-19, accurately identifying the most critical patients helps hospitals to allocate resources more efficiently ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	513,310
1109.3227	Multiple Beamforming with Perfect Coding	Perfect Space-Time Block Codes (PSTBCs) achieve full diversity, full rate, nonvanishing constant minimum determinant, uniform average transmitted energy per antenna, and good shaping. However, the high decoding complexity is a critical issue for practice. When the Channel State Information (CSI) is available at both th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	12,171
2002.03352	Streaming Submodular Maximization under a $k$-Set System Constraint	In this paper, we propose a novel framework that converts streaming algorithms for monotone submodular maximization into streaming algorithms for non-monotone submodular maximization. This reduction readily leads to the currently tightest deterministic approximation ratio for submodular maximization subject to a $k$-ma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	163,239
2105.01633	An Estimation of Online Video User Engagement from Features of Continuous Emotions	Portraying emotion and trustworthiness is known to increase the appeal of video content. However, the causal relationship between these signals and online user engagement is not well understood. This limited understanding is partly due to a scarcity in emotionally annotated data and the varied modalities which express ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	233,574
2206.01913	Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees	Learning for control of dynamical systems with formal guarantees remains a challenging task. This paper proposes a learning framework to simultaneously stabilize an unknown nonlinear system with a neural controller and learn a neural Lyapunov function to certify a region of attraction (ROA) for the closed-loop system. ...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	300,667
2402.14536	Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis	Domain adaption has been widely adapted for cross-domain sentiment analysis to transfer knowledge from the source domain to the target domain. Whereas, most methods are proposed under the assumption that the target (test) domain is known, making them fail to generalize well on unknown test data that is not always avail...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	431,730
1412.2231	Generalized Singular Value Thresholding	This work studies the Generalized Singular Value Thresholding (GSVT) operator ${\text{Prox}}_{g}^{{\sigma}}(\cdot)$, \begin{equation} {\text{Prox}}_{g}^{{\sigma}}(B)=\arg\min\limits_{X}\sum_{i=1}^{m}g(\sigma_{i}(X)) + \frac{1}{2}\|\|X-B\|\|_{F}^{2}, \end{equation} associated with a nonconvex function $g$ defined on the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	38,184
2309.03616	Filtration Surfaces for Dynamic Graph Classification	Existing approaches for classifying dynamic graphs either lift graph kernels to the temporal domain, or use graph neural networks (GNNs). However, current baselines have scalability issues, cannot handle a changing node set, or do not take edge weight information into account. We propose filtration surfaces, a novel me...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	390,443
1902.00151	A dual Newton based preconditioned proximal point algorithm for exclusive lasso models	The exclusive lasso (also known as elitist lasso) regularization has become popular recently due to its superior performance on group sparsity. Compared to the group lasso regularization which enforces the competition on variables among different groups, the exclusive lasso regularization also enforces the competition ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,333
0802.2360	On Maximizing Coverage in Gaussian Relay Networks	Results for Gaussian relay channels typically focus on maximizing transmission rates for given locations of the source, relay and destination. We introduce an alternative perspective, where the objective is maximizing coverage for a given rate. The new objective captures the problem of how to deploy relays to provide a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,299
2406.13839	RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design	We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone design. We build upon SE(3) flow matching for protein backbone generation and establish protocols for data preparation and evaluation to address unique challenges posed by RNA modeling. We formulate RNA structures as a set of rigid-body frames a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	466,014
2501.15248	Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models	Ultrasound imaging is widely used in medical diagnosis, especially for fetal health assessment. However, the availability of high-quality annotated ultrasound images is limited, which restricts the training of machine learning models. In this paper, we investigate the use of diffusion models to generate synthetic ultra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	527,464
2105.00994	Fleet management for ride-pooling with meeting points at scale: a case study in the five boroughs of New York City	Introducing meeting points to ride-pooling (RP) services has been shown to increase the satisfaction level of both riders and service providers. Passengers may choose to walk to a meeting point for a cost reduction. Drivers may also get matched with more riders without making additional stops. There are economic benefi...	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	233,399
1902.04880	Bone Texture Analysis for Prediction of Incident Radio-graphic Hip Osteoarthritis Using Machine Learning: Data from the Cohort Hip and Cohort Knee (CHECK) study	Our aim was to assess the ability of radiography-based bone texture parameters in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period. Pelvic radiographs from CHECK (Cohort Hip and Cohort Knee) at baseline (987 hips) were analyzed for bone texture using fracta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	121,434
2011.07866	Cluster-Specific Predictions with Multi-Task Gaussian Processes	A model involving Gaussian processes (GPs) is introduced to simultaneously handle multi-task learning, clustering, and prediction for multiple functional data. This procedure acts as a model-based clustering method for functional data as well as a learning step for subsequent predictions for new tasks. The model is ins...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	206,694
1812.05980	Probabilistic Class-Specific Discriminant Analysis	In this paper we formulate a probabilistic model for class-specific discriminant subspace learning. The proposed model can naturally incorporate the multi-modal structure of the negative class, which is neglected by existing class-specific methods. Moreover, it can be directly used to define a class-specific probabilis...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	116,516
2207.11213	Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay	Few-shot class-incremental learning (FSCIL) has been proposed aiming to enable a deep learning system to incrementally learn new classes with limited data. Recently, a pioneer claims that the commonly used replay-based method in class-incremental learning (CIL) is ineffective and thus not preferred for FSCIL. This has,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,548
1903.04110	Hybrid Reinforcement Learning with Expert State Sequences	Existing imitation learning approaches often require that the complete demonstration data, including sequences of actions and states, are available. In this paper, we consider a more realistic and difficult scenario where a reinforcement learning agent only has access to the state sequences of an expert, while the expe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	123,902
1811.04595	Holistic Multi-modal Memory Network for Movie Question Answering	Answering questions according to multi-modal context is a challenging problem as it requires a deep integration of different data sources. Existing approaches only employ partial interactions among data sources in one attention hop. In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework whic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,126
1909.03983	Lattice-Based Fuzzy Medical Expert System for Low Back Pain Management	Low Back Pain (LBP) is a common medical condition that deprives many individuals worldwide of their normal routine activities. In the absence of external biomarkers, diagnosis of LBP is quite challenging. It requires dealing with several clinical variables, which have no precisely quantified values. Aiming at the devel...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	144,662
1807.00517	Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract)	Most machine learning methods are known to capture and exploit biases of the training data. While some biases are beneficial for learning, others are harmful. Specifically, image captioning models tend to exaggerate biases present in training data. This can lead to incorrect captions in domains where unbiased captions ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,860
2312.10925	Delving Deeper Into Astromorphic Transformers	Preliminary attempts at incorporating the critical role of astrocytes - cells that constitute more than 50% of human brain cells - in brain-inspired neuromorphic computing remain in infancy. This paper seeks to delve deeper into various key aspects of neuron-synapse-astrocyte interactions to mimic self-attention mechan...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	416,369

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