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
2103.15949	Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors	Transformer networks have revolutionized NLP representation learning since they were introduced. Though a great effort has been made to explain the representation in transformers, it is widely recognized that our understanding is not sufficient. One important reason is that there lack enough visualization tools for det...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	227,387
1709.05298	Conversational Exploratory Search via Interactive Storytelling	Conversational interfaces are likely to become more efficient, intuitive and engaging way for human-computer interaction than today's text or touch-based interfaces. Current research efforts concerning conversational interfaces focus primarily on question answering functionality, thereby neglecting support for search a...	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	80,829
2009.05752	Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks	Chest X-ray (CXR) is a low-cost medical imaging technique. It is a common procedure for the identification of many respiratory diseases compared to MRI, CT, and PET scans. This paper presents the use of generative adversarial networks (GAN) to perform the task of lung segmentation on a given CXR. GANs are popular to ge...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	195,414
2204.04245	Online Emotions During the Storming of the U.S. Capitol: Evidence from the Social Media Network Parler	The storming of the U.S. Capitol on January 6, 2021 has led to the killing of 5 people and is widely regarded as an attack on democracy. The storming was largely coordinated through social media networks such as Parler. Yet little is known regarding how users interacted on Parler during the storming of the Capitol. In ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	290,586
2309.04268	Optimal Rate of Kernel Regression in Large Dimensions	We perform a study on kernel regression for large-dimensional data (where the sample size $n$ is polynomially depending on the dimension $d$ of the samples, i.e., $n\asymp d^{\gamma}$ for some $\gamma >0$ ). We first build a general tool to characterize the upper bound and the minimax lower bound of kernel regression f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	390,664
2301.03957	AI based approach to Trailer Generation for Online Educational Courses	In this paper, we propose an AI based approach to Trailer Generation in the form of short videos for online educational courses. Trailers give an overview of the course to the learners and help them make an informed choice about the courses they want to learn. It also helps to generate curiosity and interest among the ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	339,926
2410.05193	RevisEval: Improving LLM-as-a-Judge via Response-Adapted References	With significant efforts in recent studies, LLM-as-a-Judge has become a cost-effective alternative to human evaluation for assessing text generation quality in a wide range of tasks. However, there still remains a reliability gap between LLM-as-a-Judge and human evaluation. One important reason is the lack of guided or...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	495,612
2406.16437	Theory on Mixture-of-Experts in Continual Learning	Continual learning (CL) has garnered significant attention because of its ability to adapt to new tasks that arrive over time. Catastrophic forgetting (of old tasks) has been identified as a major issue in CL, as the model adapts to new tasks. The Mixture-of-Experts (MoE) model has recently been shown to effectively mi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	467,127
2303.03975	GATE: A Challenge Set for Gender-Ambiguous Translation Examples	Although recent years have brought significant progress in improving translation of unambiguously gendered sentences, translation of ambiguously gendered input remains relatively unexplored. When source gender is ambiguous, machine translation models typically default to stereotypical gender roles, perpetuating harmful...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	349,918
1308.5334	Approximated Symbolic Computations over Hybrid Automata	Hybrid automata are a natural framework for modeling and analyzing systems which exhibit a mixed discrete continuous behaviour. However, the standard operational semantics defined over such models implicitly assume perfect knowledge of the real systems and infinite precision measurements. Such assumptions are not only ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	26,629
2010.06876	Semantic Flow-guided Motion Removal Method for Robust Mapping	Moving objects in scenes are still a severe challenge for the SLAM system. Many efforts have tried to remove the motion regions in the images by detecting moving objects. In this way, the keypoints belonging to motion regions will be ignored in the later calculations. In this paper, we proposed a novel motion removal m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	200,632
2402.16041	Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy	Large language models (LLMs) such as ChatGPT have exhibited remarkable performance in generating human-like texts. However, machine-generated texts (MGTs) may carry critical risks, such as plagiarism issues, misleading information, or hallucination issues. Therefore, it is very urgent and important to detect MGTs in ma...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	432,401
2410.21200	BongLLaMA: LLaMA for Bangla Language	Bangla (or "Bengali") is a language spoken by approximately 240 million native speakers and around 300 million people worldwide. Despite being the 5th largest spoken language in the world, Bangla is still a "low-resource" language, and existing pretrained language models often struggle to perform well on Bangla Languag...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	503,127
2103.04303	Joint Coding and Scheduling Optimization for Distributed Learning over Wireless Edge Networks	Unlike theoretical distributed learning (DL), DL over wireless edge networks faces the inherent dynamics/uncertainty of wireless connections and edge nodes, making DL less efficient or even inapplicable under the highly dynamic wireless edge networks (e.g., using mmW interfaces). This article addresses these problems b...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	223,595
2309.13345	BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models	Large language models (LLMs) have achieved dramatic proficiency over NLP tasks with normal length. Recently, multiple studies have committed to extending the context length and enhancing the long text modeling capabilities of LLMs. To comprehensively evaluate the long context ability of LLMs, we propose BAMBOO, a multi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	394,167
1112.1117	Finding Heavy Paths in Graphs: A Rank Join Approach	Graphs have been commonly used to model many applications. A natural problem which abstracts applications such as itinerary planning, playlist recommendation, and flow analysis in information networks is that of finding the heaviest path(s) in a graph. More precisely, we can model these applications as a graph with non...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	13,323
1304.5880	Dealing with natural language interfaces in a geolocation context	In the geolocation field where high-level programs and low-level devices coexist, it is often difficult to find a friendly user inter- face to configure all the parameters. The challenge addressed in this paper is to propose intuitive and simple, thus natural lan- guage interfaces to interact with low-level devices. Su...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	24,128
2002.05185	A Tensor Network Approach to Finite Markov Decision Processes	Tensor network (TN) techniques - often used in the context of quantum many-body physics - have shown promise as a tool for tackling machine learning (ML) problems. The application of TNs to ML, however, has mostly focused on supervised and unsupervised learning. Yet, with their direct connection to hidden Markov chains...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,819
2303.02512	Visual Saliency-Guided Channel Pruning for Deep Visual Detectors in Autonomous Driving	Deep neural network (DNN) pruning has become a de facto component for deploying on resource-constrained devices since it can reduce memory requirements and computation costs during inference. In particular, channel pruning gained more popularity due to its structured nature and direct savings on general hardware. Howev...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	349,389
2410.11097	DMOSpeech: Direct Metric Optimization via Distilled Diffusion Model in Zero-Shot Speech Synthesis	Diffusion models have demonstrated significant potential in speech synthesis tasks, including text-to-speech (TTS) and voice cloning. However, their iterative denoising processes are computationally intensive, and previous distillation attempts have shown consistent quality degradation. Moreover, existing TTS approache...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	498,381
2305.12138	LMs: Understanding Code Syntax and Semantics for Code Analysis	Large language models~(LLMs) demonstrate significant potential to revolutionize software engineering (SE) by exhibiting outstanding performance in SE tasks such as code and document generation. However, the high reliability and risk control requirements in software engineering raise concerns about the lack of interpret...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	365,857
2305.13659	Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification	Multi-spectral vehicle re-identification aims to address the challenge of identifying vehicles in complex lighting conditions by incorporating complementary visible and infrared information. However, in harsh environments, the discriminative cues in RGB and NIR modalities are often lost due to strong flares from vehicl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	366,612
2303.03272	Accelerated Rates between Stochastic and Adversarial Online Convex Optimization	Stochastic and adversarial data are two widely studied settings in online learning. But many optimization tasks are neither i.i.d. nor fully adversarial, which makes it of fundamental interest to get a better theoretical understanding of the world between these extremes. In this work we establish novel regret bounds fo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	349,664
2308.05522	Models Matter: The Impact of Single-Step Retrosynthesis on Synthesis Planning	Retrosynthesis consists of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found with the goal to provide a synthesis route. Its two primary research directions, single-step retrosynthesis prediction, which models the chemical react...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	384,819
2306.14874	ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots	Performing agile navigation with four-legged robots is a challenging task due to the highly dynamic motions, contacts with various parts of the robot, and the limited field of view of the perception sensors. In this paper, we propose a fully-learned approach to train such robots and conquer scenarios that are reminisce...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	375,842
2102.07800	Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy	Motivated by modern applications, such as online advertisement and recommender systems, we study the top-$k$ extreme contextual bandits problem, where the total number of arms can be enormous, and the learner is allowed to select $k$ arms and observe all or some of the rewards for the chosen arms. We first propose an a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	220,221
1709.04121	Sketch-pix2seq: a Model to Generate Sketches of Multiple Categories	Sketch is an important media for human to communicate ideas, which reflects the superiority of human intelligence. Studies on sketch can be roughly summarized into recognition and generation. Existing models on image recognition failed to obtain satisfying performance on sketch classification. But for sketch generation...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,606
1808.05120	Trust-based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop	Symbolic motion planning for robots is the process of specifying and planning robot tasks in a discrete space, then carrying them out in a continuous space in a manner that preserves the discrete-level task specifications. Despite progress in symbolic motion planning, many challenges remain, including addressing scalab...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	105,294
2310.08597	A Method for Multi-Robot Asynchronous Trajectory Execution in MoveIt2	This work presents an extension to the MoveIt2 planning library supporting asynchronous execution for multi-robot / multi-arm robotic setups. The proposed method introduces a unified way for the execution of both synchronous and asynchronous trajectories by implementing a simple scheduler and guarantees collision-free ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	399,454
1806.08723	Keypoint Transfer for Fast Whole-Body Segmentation	We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images. Keypoints represent automatically identified distinctive image locations, where each keypoint correspondence suggests a transformation between images. We use these correspondences to transfe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,205
2501.07705	Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML	Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to character...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	524,473
2310.03312	Certifiably Robust Graph Contrastive Learning	Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph representation learning method. However, it has been shown that GCL is vulnerable to adversarial attacks on both the graph structure and node attributes. Although empirical approaches have been proposed to enhance the robustness of GCL, the ce...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	397,231
2112.05351	Exploring Pixel-level Self-supervision for Weakly Supervised Semantic Segmentation	Existing studies in weakly supervised semantic segmentation (WSSS) have utilized class activation maps (CAMs) to localize the class objects. However, since a classification loss is insufficient for providing precise object regions, CAMs tend to be biased towards discriminative patterns (i.e., sparseness) and do not pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	270,816
2201.03156	$m^\ast$ of two-dimensional electron gas: a neural canonical transformation study	The quasiparticle effective mass $m^\ast$ of interacting electrons is a fundamental quantity in the Fermi liquid theory. However, the precise value of the effective mass of uniform electron gas is still elusive after decades of research. The newly developed neural canonical transformation approach [Xie et al., J. Mach....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	274,765
0903.0479	Combining Symmetry Breaking and Global Constraints	We propose a new family of constraints which combine together lexicographical ordering constraints for symmetry breaking with other common global constraints. We give a general purpose propagator for this family of constraints, and show how to improve its complexity by exploiting properties of the included global const...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	3,273
2407.19463	HD-maps as Prior Information for Globally Consistent Mapping in GPS-denied Environments	In recent years, prior maps have become a mainstream tool in autonomous navigation. However, commonly available prior maps are still tailored to control-and-decision tasks, and the use of these maps for localization remains largely unexplored. To bridge this gap, we propose a lidar-based localization and mapping (LOAM)...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	476,797
1909.11304	Asymptotics of Wide Networks from Feynman Diagrams	Understanding the asymptotic behavior of wide networks is of considerable interest. In this work, we present a general method for analyzing this large width behavior. The method is an adaptation of Feynman diagrams, a standard tool for computing multivariate Gaussian integrals. We apply our method to study training dyn...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,779
2401.16335	Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF	Reinforcement Learning from Human Feedback (RLHF) is a pivotal technique that aligns language models closely with human-centric values. The initial phase of RLHF involves learning human values using a reward model from ranking data. It is observed that the performance of the reward model degrades after one epoch of tra...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	424,783
2408.03124	Closed-loop Diffusion Control of Complex Physical Systems	The control problems of complex physical systems have broad applications in science and engineering. Previous studies have shown that generative control methods based on diffusion models offer significant advantages for solving these problems. However, existing generative control approaches face challenges in both perf...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	478,902
2105.04833	Optimal Transmit Strategy for Multi-user MIMO WPT Systems With Non-linear Energy Harvesters	In this paper, we study multi-user multi-antenna wireless power transfer (WPT) systems, where each antenna at the energy harvesting (EH) nodes is connected to a dedicated non-linear rectifier. We propose an optimal transmit strategy which maximizes a weighted sum of the average harvested powers at the EH nodes under a ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	234,632
2102.10073	Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations	Pyserini is an easy-to-use Python toolkit that supports replicable IR research by providing effective first-stage retrieval in a multi-stage ranking architecture. Our toolkit is self-contained as a standard Python package and comes with queries, relevance judgments, pre-built indexes, and evaluation scripts for many co...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	220,969
1102.4922	Counting Solutions of Constraint Satisfiability Problems:Exact Phase Transitions and Approximate Algorithm	The study of phase transition phenomenon of NP complete problems plays an important role in understanding the nature of hard problems. In this paper, we follow this line of research by considering the problem of counting solutions of Constraint Satisfaction Problems (#CSP). We consider the random model, i.e. RB model. ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	9,342
2404.11677	Cross-Problem Learning for Solving Vehicle Routing Problems	Existing neural heuristics often train a deep architecture from scratch for each specific vehicle routing problem (VRP), ignoring the transferable knowledge across different VRP variants. This paper proposes the cross-problem learning to assist heuristics training for different downstream VRP variants. Particularly, we...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	447,569
2204.08939	Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics	Optimization and uncertainty quantification have been playing an increasingly important role in computational hemodynamics. However, existing methods based on principled modeling and classic numerical techniques have faced significant challenges, particularly when it comes to complex 3D patient-specific shapes in the r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	292,261
2404.14741	Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering	To address the issues of insufficient knowledge and hallucination in Large Language Models (LLMs), numerous studies have explored integrating LLMs with Knowledge Graphs (KGs). However, these methods are typically evaluated on conventional Knowledge Graph Question Answering (KGQA) with complete KGs, where all factual tr...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	448,796
2004.02340	Enhancing Social Recommendation with Adversarial Graph Convolutional Networks	Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. However, recent reports from industry show that social recommender systems consistently fail in practice. According to the negative findings, the failure is attr...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	171,198
2407.15992	Multimodal Input Aids a Bayesian Model of Phonetic Learning	One of the many tasks facing the typically-developing child language learner is learning to discriminate between the distinctive sounds that make up words in their native language. Here we investigate whether multimodal information--specifically adult speech coupled with video frames of speakers' faces--benefits a comp...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	475,415
2010.05143	PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation	De-identification is the task of identifying protected health information (PHI) in the clinical text. Existing neural de-identification models often fail to generalize to a new dataset. We propose a simple yet effective data augmentation method PHICON to alleviate the generalization issue. PHICON consists of PHI augmen...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	200,005
1307.6544	Veni Vidi Vici, A Three-Phase Scenario For Parameter Space Analysis in Image Analysis and Visualization	Automatic analysis of the enormous sets of images is a critical task in life sciences. This faces many challenges such as: algorithms are highly parameterized, significant human input is intertwined, and lacking a standard meta-visualization approach. This paper proposes an alternative iterative approach for optimizing...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	26,034
2109.12843	A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions	Recommender system is one of the most important information services on today's Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems. We first ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	257,431
1909.02702	Port-Hamiltonian Approach to Neural Network Training	Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights which are iteratively optimized via difference equations. Recent work proposes networks with layer outputs which are no longer quantized but are solutions of an ordinary differential equation (ODE); however, these network...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	144,260
1105.2255	On the Limitations of Provenance for Queries With Difference	The annotation of the results of database transformations was shown to be very effective for various applications. Until recently, most works in this context focused on positive query languages. The provenance semirings is a particular approach that was proven effective for these languages, and it was shown that when p...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	10,328
2210.12623	Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings	Zero-resource cross-lingual transfer approaches aim to apply supervised models from a source language to unlabelled target languages. In this paper we perform an in-depth study of the two main techniques employed so far for cross-lingual zero-resource sequence labelling, based either on data or model transfer. Although...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	325,826
1704.07986	Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization	We present in this paper our approach for modeling inter-topic preferences of Twitter users: for example, those who agree with the Trans-Pacific Partnership (TPP) also agree with free trade. This kind of knowledge is useful not only for stance detection across multiple topics but also for various real-world application...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	72,455
2406.12616	Learning diffusion at lightspeed	Diffusion regulates numerous natural processes and the dynamics of many successful generative models. Existing models to learn the diffusion terms from observational data rely on complex bilevel optimization problems and model only the drift of the system. We propose a new simple model, JKOnet*, which bypasses the comp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	465,489
2410.24187	Chasing Better Deep Image Priors between Over- and Under-parameterization	Deep Neural Networks (DNNs) are well-known to act as over-parameterized deep image priors (DIP) that regularize various image inverse problems. Meanwhile, researchers also proposed extremely compact, under-parameterized image priors (e.g., deep decoder) that are strikingly competent for image restoration too, despite a...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	504,368
1304.0725	Improved Performance of Unsupervised Method by Renovated K-Means	Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for c...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	23,406
2209.04275	Temporally Adjustable Longitudinal Fluid-Attenuated Inversion Recovery MRI Estimation / Synthesis for Multiple Sclerosis	Multiple Sclerosis (MS) is a chronic progressive neurological disease characterized by the development of lesions in the white matter of the brain. T2-fluid-attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) provides superior visualization and characterization of MS lesions, relative to other ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	316,739
2410.24214	ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs	Mixed precision quantization has become an important technique for optimizing the execution of deep neural networks (DNNs). Certified robustness, which provides provable guarantees about a model's ability to withstand different adversarial perturbations, has rarely been addressed in quantization due to unacceptably hig...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	504,383
2502.14772	Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination	We study the algorithmic problem of robust mean estimation of an identity covariance Gaussian in the presence of mean-shift contamination. In this contamination model, we are given a set of points in $\mathbb{R}^d$ generated i.i.d. via the following process. For a parameter $\alpha<1/2$, the $i$-th sample $x_i$ is obta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	535,967
2003.05268	Human-in-the-Loop Design Cycles -- A Process Framework that Integrates Design Sprints, Agile Processes, and Machine Learning with Humans	Demands on more transparency of the backbox nature of machine learning models have led to the recent rise of human-in-the-loop in machine learning, i.e. processes that integrate humans in the training and application of machine learning models. The present work argues that this process requirement does not represent an...	true	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	167,822
2402.01617	A GP-based Robust Motion Planning Framework for Agile Autonomous Robot Navigation and Recovery in Unknown Environments	For autonomous mobile robots, uncertainties in the environment and system model can lead to failure in the motion planning pipeline, resulting in potential collisions. In order to achieve a high level of robust autonomy, these robots should be able to proactively predict and recover from such failures. To this end, we ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	426,101
1301.5898	Phase Diagram and Approximate Message Passing for Blind Calibration and Dictionary Learning	We consider dictionary learning and blind calibration for signals and matrices created from a random ensemble. We study the mean-squared error in the limit of large signal dimension using the replica method and unveil the appearance of phase transitions delimiting impossible, possible-but-hard and possible inference re...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	21,364
cs/9609102	Cue Phrase Classification Using Machine Learning	Cue phrases may be used in a discourse sense to explicitly signal discourse structure, but also in a sentential sense to convey semantic rather than structural information. Correctly classifying cue phrases as discourse or sentential is critical in natural language processing systems that exploit discourse structure, e...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	540,348
2110.00090	Information Design for a Non-atomic Service Scheduling Game	We study an information design problem for a non-atomic service scheduling game. The service starts at a random time and there is a continuum of agent population who have a prior belief about the service start time but do not observe the actual realization of it. The agents want to make decisions of when to join the qu...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	258,278
2303.12234	Pre-NeRF 360: Enriching Unbounded Appearances for Neural Radiance Fields	Neural radiance fields (NeRF) appeared recently as a powerful tool to generate realistic views of objects and confined areas. Still, they face serious challenges with open scenes, where the camera has unrestricted movement and content can appear at any distance. In such scenarios, current NeRF-inspired models frequentl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,179
2103.09635	SILT: Efficient transformer training for inter-lingual inference	The ability of transformers to perform precision tasks such as question answering, Natural Language Inference (NLI) or summarising, have enabled them to be ranked as one of the best paradigm to address Natural Language Processing (NLP) tasks. NLI is one of the best scenarios to test these architectures, due to the know...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	225,217
2502.04664	Implicit Bias of SignGD and Adam on Multiclass Separable Data	In the optimization of overparameterized models, different gradient-based methods can achieve zero training error yet converge to distinctly different solutions inducing different generalization properties. While a decade of research on implicit optimization bias has illuminated this phenomenon in various settings, eve...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	531,262
1512.01749	Combinatorial Message Sharing and a New Achievable Region for Multiple Descriptions	This paper presents a new achievable rate-distortion region for the general L channel multiple descriptions problem. A well known general region for this problem is due to Venkataramani, Kramer and Goyal (VKG) [1]. Their encoding scheme is an extension of the El-Gamal-Cover (EC) and Zhang- Berger (ZB) coding schemes to...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	49,853
2112.03765	In-flight Novelty Detection with Convolutional Neural Networks	Gas turbine engines are complex machines that typically generate a vast amount of data, and require careful monitoring to allow for cost-effective preventative maintenance. In aerospace applications, returning all measured data to ground is prohibitively expensive, often causing useful, high value, data to be discarded...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	270,335
0903.0279	An introduction to DSmT	The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems involving artificial reasoning. In this introduction, we present a surve...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	3,259
2403.08942	Collision-Free Platooning of Mobile Robots through a Set-Theoretic Predictive Control Approach	This paper proposes a control solution to achieve collision-free platooning control of input-constrained mobile robots. The platooning policy is based on a leader-follower approach where the leader tracks a reference trajectory while followers track the leader's pose with an inter-agent delay. First, the leader and the...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	437,550
1907.09217	Single Image based Head Pose Estimation with Spherical Parameterization and 3D Morphing	Head pose estimation plays a vital role in various applications, e.g., driverassistance systems, human-computer interaction, virtual reality technology, and so on. We propose a novel geometry based algorithm for accurately estimating the head pose from a single 2D face image at a very low computational cost. Specifical...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	139,305
2306.03254	Characterizing the Effects of Single Bus Perturbation on Power Systems Graph Signals	This article explores the effects of a single bus perturbation in the electrical grid using a Graph Signal Processing (GSP) perspective. The perturbation is characterized by a sudden change in real-power load demand or generation. The study focuses on analyzing the spread of the perturbation throughout the grid and pro...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	371,247
2401.03459	BCLNet: Bilateral Consensus Learning for Two-View Correspondence Pruning	Correspondence pruning aims to establish reliable correspondences between two related images and recover relative camera motion. Existing approaches often employ a progressive strategy to handle the local and global contexts, with a prominent emphasis on transitioning from local to global, resulting in the neglect of i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	420,111
2305.11996	EEG and EMG dataset for the detection of errors introduced by an active orthosis device	This paper presents a dataset containing recordings of the electroencephalogram (EEG) and the electromyogram (EMG) from eight subjects who were assisted in moving their right arm by an active orthosis device. The supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the ort...	true	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	365,788
2205.11773	Constrained Error Pattern Generation for GRAND	Maximum-likelihood (ML) decoding can be used to obtain the optimal performance of error correction codes. However, the size of the search space and consequently the decoding complexity grows exponentially, making it impractical to be employed for long codes. In this paper, we propose an approach to constrain the search...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	298,276
1702.01923	Comparative Study of CNN and RNN for Natural Language Processing	Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle various NLP tasks. CNN is supposed to be good at extracting position-invariant feat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	67,893
1810.02244	Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks	In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Up to now, GNNs have only been evaluated empirically -- showing promising results. The following work investigates GNNs from a theoretical...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	109,553
2311.08149	Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes	In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories. We aim to find meaningful temporal latent representations of an underlying generative process that explain the observed disease trajectories in an interp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	407,608
2407.11265	Mix-and-Conquer: Beamforming Design with Interconnected RIS for Multi-User Networks	We propose a new reconfigurable intelligent surface (RIS) structure, referred to as interconnected RIS (I-RIS), which allows the RIS elements to be interconnected and share the incident signals using simple binary radio frequency (RF) switches and mix them into the reflecting signals. This structure enables multi-user ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	473,370
2006.08453	Bayesian Neural Network via Stochastic Gradient Descent	The goal of bayesian approach used in variational inference is to minimize the KL divergence between variational distribution and unknown posterior distribution. This is done by maximizing the Evidence Lower Bound (ELBO). A neural network is used to parametrize these distributions using Stochastic Gradient Descent. Thi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,195
2212.00455	Consensus of hierarchical multi-agent systems with a time-varying set of active agents	Time-varying hierarchical multi-agent systems are common in many applications. A well-known solution to control these systems is to use state feedback controllers that depend on the adjacency matrix to reach consensus. This solution has been applied so far to multi-agent systems with fixed or time-varying communication...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	334,061
2010.07021	Better Patch Stitching for Parametric Surface Reconstruction	Recently, parametric mappings have emerged as highly effective surface representations, yielding low reconstruction error. In particular, the latest works represent the target shape as an atlas of multiple mappings, which can closely encode object parts. Atlas representations, however, suffer from one major drawback: T...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	200,680
2303.06877	Progressive Open Space Expansion for Open-Set Model Attribution	Despite the remarkable progress in generative technology, the Janus-faced issues of intellectual property protection and malicious content supervision have arisen. Efforts have been paid to manage synthetic images by attributing them to a set of potential source models. However, the closed-set classification setting li...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	351,035
2011.03526	Identifying Stress Responsive Genes using Overlapping Communities in Co-expression Networks	This paper proposes a workflow to identify genes that respond to specific treatments in plants. The workflow takes as input the RNA sequencing read counts and phenotypical data of different genotypes, measured under control and treatment conditions. It outputs a reduced group of genes marked as relevant for treatment r...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	205,268
1812.07710	Training on Art Composition Attributes to Influence CycleGAN Art Generation	I consider how to influence CycleGAN, image-to-image translation, by using additional constraints from a neural network trained on art composition attributes. I show how I trained the the Art Composition Attributes Network (ACAN) by incorporating domain knowledge based on the rules of art evaluation and the result of a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	116,863
2403.20190	Homomorphic WiSARDs: Efficient Weightless Neural Network training over encrypted data	The widespread application of machine learning algorithms is a matter of increasing concern for the data privacy research community, and many have sought to develop privacy-preserving techniques for it. Among existing approaches, the homomorphic evaluation of ML algorithms stands out by performing operations directly o...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	442,652
2310.19767	Autoregressive Attention Neural Networks for Non-Line-of-Sight User Tracking with Dynamic Metasurface Antennas	User localization and tracking in the upcoming generation of wireless networks have the potential to be revolutionized by technologies such as the Dynamic Metasurface Antennas (DMAs). Commonly proposed algorithmic approaches rely on assumptions about relatively dominant Line-of-Sight (LoS) paths, or require pilot trans...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	404,118
1409.8125	Random Access Protocols with Collision Resolution in a Noncoherent Setting	Wireless systems are increasingly used for Machine-Type Communication (MTC), where the users sporadically send very short messages. In such a setting, the overhead imposed by channel estimation is substantial, thereby demanding noncoherent communication. In this paper we consider a noncoherent setup in which users rand...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	36,386
2104.05112	iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform	Stereo matching is a critical task for robot navigation and autonomous vehicles, providing the depth estimation of surroundings. Among all stereo matching algorithms, Efficient Large-scale Stereo (ELAS) offers one of the best tradeoffs between efficiency and accuracy. However, due to the inherent iterative process and ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	229,604
1810.03367	Split-Correctness in Information Extraction	Programs for extracting structured information from text, namely information extractors, often operate separately on document segments obtained from a generic splitting operation such as sentences, paragraphs, k-grams, HTTP requests, and so on. An automated detection of this behavior of extractors, which we refer to as...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	109,789
2010.10103	Two-stage generative adversarial networks for document image binarization with color noise and background removal	Document image enhancement and binarization methods are often used to improve the accuracy and efficiency of document image analysis tasks such as text recognition. Traditional non-machine-learning methods are constructed on low-level features in an unsupervised manner but have difficulty with binarization on documents...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	201,784
2212.00800	The purpose of qualia: What if human thinking is not (only) information processing?	Despite recent breakthroughs in the field of artificial intelligence (AI) - or more specifically machine learning (ML) algorithms for object recognition and natural language processing - it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	334,202
2208.06568	On the Limitations of Continual Learning for Malware Classification	Malicious software (malware) classification offers a unique challenge for continual learning (CL) regimes due to the volume of new samples received on a daily basis and the evolution of malware to exploit new vulnerabilities. On a typical day, antivirus vendors receive hundreds of thousands of unique pieces of software...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	312,756
2109.14076	RAFT: A Real-World Few-Shot Text Classification Benchmark	Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? Existing benchmarks are not designed to measure progress in applied s...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	257,836
1906.10799	Computer-aided modelling of complex physical systems with BondGraphTools	BondGraphTools is a Python library for scripted modelling of complex multi-physics systems. In contrast to existing modelling solutions, BondGraphTools is based upon the well established bond graph methodology, provides a programming interface for symbolic model composition, and is intended to be used in conjunction wi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	136,517
1910.12050	Facility Location Problem in Differential Privacy Model Revisited	In this paper we study the uncapacitated facility location problem in the model of differential privacy (DP) with uniform facility cost. Specifically, we first show that, under the hierarchically well-separated tree (HST) metrics and the super-set output setting that was introduced in Gupta et. al., there is an $\epsil...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	150,957
1807.00488	A Simple but Effective Classification Model for Grammatical Error Correction	We treat grammatical error correction (GEC) as a classification problem in this study, where for different types of errors, a target word is identified, and the classifier predicts the correct word form from a set of possible choices. We propose a novel neural network based feature representation and classification mod...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	101,851
2402.10457	Learning-Augmented Skip Lists	We study the integration of machine learning advice into the design of skip lists to improve upon traditional data structure design. Given access to a possibly erroneous oracle that outputs estimated fractional frequencies for search queries on a set of items, we construct a skip list that provably provides the optimal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	429,971

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