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
1909.01423	Exploration Without Global Consistency Using Local Volume Consolidation	In exploration, the goal is to build a map of an unknown environment. Most state-of-the-art approaches use map representations that require drift-free state estimates to function properly. Real-world state estimators, however, exhibit drift. In this paper, we present a 2D map representation for exploration that is robu...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	143,894
2104.10326	A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation	Instance level detection and segmentation of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images. Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN. The SAR-Net con...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,535
1511.06408	Feature-based Attention in Convolutional Neural Networks	Convolutional neural networks (CNNs) have proven effective for image processing tasks, such as object recognition and classification. Recently, CNNs have been enhanced with concepts of attention, similar to those found in biology. Much of this work on attention has focused on effective serial spatial processing. In thi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	49,229
2502.06632	Few-Shot Classification and Anatomical Localization of Tissues in SPECT Imaging	Accurate classification and anatomical localization are essential for effective medical diagnostics and research, which may be efficiently performed using deep learning techniques. However, availability of limited labeled data poses a significant challenge. To address this, we adapted Prototypical Networks and the Prop...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	532,159
2304.09439	Local object crop collision network for efficient simulation of non-convex objects in GPU-based simulators	Our goal is to develop an efficient contact detection algorithm for large-scale GPU-based simulation of non-convex objects. Current GPU-based simulators such as IsaacGym and Brax must trade-off speed with fidelity, generality, or both when simulating non-convex objects. Their main issue lies in contact detection (CD): ...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	359,057
1511.00542	Optimal Vector Linear Index Codes for Some Symmetric Side Information Problems	This paper deals with vector linear index codes for multiple unicast index coding problems where there is a source with K messages and there are K receivers each wanting a unique message and having symmetric (with respect to the receiver index) two-sided antidotes (side information). Optimal scalar linear index codes f...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	48,419
2201.01845	Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation	Common designs of model evaluation typically focus on monolingual settings, where different models are compared according to their performance on a single data set that is assumed to be representative of all possible data for the task at hand. While this may be reasonable for a large data set, this assumption is diffic...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	274,372
2201.04461	Blackbox Post-Processing for Multiclass Fairness	Applying standard machine learning approaches for classification can produce unequal results across different demographic groups. When then used in real-world settings, these inequities can have negative societal impacts. This has motivated the development of various approaches to fair classification with machine learn...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	275,109
1509.04521	Discrete-time optimal attitude control of spacecraft with momentum and control constraints	This article solves an optimal control problem arising in attitude control of a spacecraft under state and control constraints. We first derive the discrete-time attitude dynamics by employing discrete mechanics. The orientation transfer, with initial and final values of the orientation and momentum and the time durati...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	46,939
2211.12044	Backdoor Cleansing with Unlabeled Data	Due to the increasing computational demand of Deep Neural Networks (DNNs), companies and organizations have begun to outsource the training process. However, the externally trained DNNs can potentially be backdoor attacked. It is crucial to defend against such attacks, i.e., to postprocess a suspicious model so that it...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	331,974
2010.06402	Which Model to Transfer? Finding the Needle in the Growing Haystack	Transfer learning has been recently popularized as a data-efficient alternative to training models from scratch, in particular for computer vision tasks where it provides a remarkably solid baseline. The emergence of rich model repositories, such as TensorFlow Hub, enables the practitioners and researchers to unleash t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	200,473
2210.12927	The Design and Realization of Multi-agent Obstacle Avoidance based on Reinforcement Learning	Intelligence agents and multi-agent systems play important roles in scenes like the control system of grouped drones, and multi-agent navigation and obstacle avoidance which is the foundational function of advanced application has great importance. In multi-agent navigation and obstacle avoidance tasks, the decision-ma...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	325,973
2110.08712	Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models	Traffic state prediction is necessary for many Intelligent Transportation Systems applications. Recent developments of the topic have focused on network-wide, multi-step prediction, where state of the art performance is achieved via deep learning models, in particular, graph neural network-based models. While the predi...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	261,522
1712.07781	On the Outage Analysis and Finite SNR Diversity-Multiplexing Tradeoff of Hybrid-Duplex Systems for Aeronautical Communications	A hybrid-duplex aeronautical communication system (HBD-ACS) consisting of a full-duplex (FD) enabled ground station (GS), and two half-duplex (HD) air-stations (ASs) is proposed as a direct solution to the spectrum crunch faced by the aviation industry. Closed-form outage probability and finite signal-to-noise ratio (S...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	87,095
2104.10033	Safety-enhanced UAV Path Planning with Spherical Vector-based Particle Swarm Optimization	This paper presents a new algorithm named spherical vector-based particle swarm optimization (SPSO) to deal with the problem of path planning for unmanned aerial vehicles (UAVs) in complicated environments subjected to multiple threats. A cost function is first formulated to convert the path planning into an optimizati...	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	true	false	false	231,437
2205.01128	Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systems	What explains the dramatic progress from 20th-century to 21st-century AI, and how can the remaining limitations of current AI be overcome? The widely accepted narrative attributes this progress to massive increases in the quantity of computational and data resources available to support statistical learning in deep art...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	true	294,481
2310.16724	Spherical Wavefront Near-Field DoA Estimation in THz Automotive Radar	Automotive radar at terahertz (THz) band has the potential to provide compact design. The availability of wide bandwidth at THz-band leads to high range resolution. Further, very narrow beamwidth arising from large arrays yields high angular resolution up to milli-degree level direction-of-arrival (DoA) estimation. At ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	402,845
1910.02555	Domain Differential Adaptation for Neural Machine Translation	Neural networks are known to be data hungry and domain sensitive, but it is nearly impossible to obtain large quantities of labeled data for every domain we are interested in. This necessitates the use of domain adaptation strategies. One common strategy encourages generalization by aligning the global distribution sta...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	148,278
2210.05496	Experiment Design for Identification of Marine Models	In this work, experiment design for marine vessels is explored. A dictionary-based approach is used, i.e., a systematic way of choosing the most informative combination of independent experiments out of a predefined set of candidates. This idea is quite general but is here tailored to an instrumental variable (IV) esti...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	322,878
2405.12016	Conformalized Strategy-Proof Auctions	Auctions are key for maximizing sellers' revenue and ensuring truthful bidding among buyers. Recently, an approach known as differentiable economics based on machine learning (ML) has shown promise in learning powerful auction mechanisms for multiple items and participants. However, this approach has no guarantee of st...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	455,383
2211.05675	Causal Modeling of Soil Processes for Improved Generalization	Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems. Soil organic carbon not only enriches nutrition in soil, but also has a gamut of co-benefits such as improving water storage and limiting physical erosion. Despite a litany of work ...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	329,644
2211.15009	BJTU-WeChat's Systems for the WMT22 Chat Translation Task	This paper introduces the joint submission of the Beijing Jiaotong University and WeChat AI to the WMT'22 chat translation task for English-German. Based on the Transformer, we apply several effective variants. In our experiments, we utilize the pre-training-then-fine-tuning paradigm. In the first pre-training stage, w...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	333,082
2405.14018	Watermarking Generative Tabular Data	In this paper, we introduce a simple yet effective tabular data watermarking mechanism with statistical guarantees. We show theoretically that the proposed watermark can be effectively detected, while faithfully preserving the data fidelity, and also demonstrates appealing robustness against additive noise attack. The ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	456,203
2310.00534	Safe Optimal Interactions Between Automated and Human-Driven Vehicles in Mixed Traffic with Event-triggered Control Barrier Functions	This paper studies safe driving interactions between Human-Driven Vehicles (HDVs) and Connected and Automated Vehicles (CAVs) in mixed traffic where the dynamics and control policies of HDVs are unknown and hard to predict. In order to address this challenge, we employ event-triggered Control Barrier Functions (CBFs) t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	396,024
2407.00562	Automated Robot Recovery from Assumption Violations of High-Level Specifications	This paper presents a framework that enables robots to automatically recover from assumption violations of high-level specifications during task execution. In contrast to previous methods relying on user intervention to impose additional assumptions for failure recovery, our approach leverages synthesis-based repair to...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	468,910
2210.11983	Pyrit: A Finite Element Based Field Simulation Software Written in Python	Pyrit is a field simulation software based on the finite element method written in Python to solve coupled systems of partial differential equations. It is designed as a modular software that is easily modifiable and extendable. The framework can, therefore, be adapted to various activities, i.e. research, education an...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	325,533
1805.01111	A Robust Algorithm for Online Switched System Identification	In this paper, we consider the problem of online identification of Switched AutoRegressive eXogenous (SARX) systems, where the goal is to estimate the parameters of each subsystem and identify the switching sequence as data are obtained in a streaming fashion. Previous works in this area are sensitive to initialization...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	96,589
2402.11505	Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources	Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs). While promising, it raises significant challenges due to the heterogeneous resources and data distributions of clients. This study introduces FlexLoRA, a simple yet effective aggregation scheme for ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	430,430
2307.00741	UnLoc: A Universal Localization Method for Autonomous Vehicles using LiDAR, Radar and/or Camera Input	Localization is a fundamental task in robotics for autonomous navigation. Existing localization methods rely on a single input data modality or train several computational models to process different modalities. This leads to stringent computational requirements and sub-optimal results that fail to capitalize on the co...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	377,112
2112.06780	Explanation Container in Case-Based Biomedical Question-Answering	The National Center for Advancing Translational Sciences(NCATS) Biomedical Data Translator (Translator) aims to attenuate problems faced by translational scientists. Translator is a multi-agent architecture consisting of six autonomous relay agents (ARAs) and eight knowledge providers (KPs). In this paper, we present t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	271,300
1712.00321	Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images	In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject. Specifically, the proposed autoencoder transforms an input face image such that the transformed image can be successfully used for face recognition but not for gender classification. In or...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	85,877
1801.06126	Non-Adversarial Unsupervised Word Translation	Unsupervised word translation from non-parallel inter-lingual corpora has attracted much research interest. Very recently, neural network methods trained with adversarial loss functions achieved high accuracy on this task. Despite the impressive success of the recent techniques, they suffer from the typical drawbacks o...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	88,560
2006.01244	The Power of Factorial Powers: New Parameter settings for (Stochastic) Optimization	The convergence rates for convex and non-convex optimization methods depend on the choice of a host of constants, including step sizes, Lyapunov function constants and momentum constants. In this work we propose the use of factorial powers as a flexible tool for defining constants that appear in convergence proofs. We ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	179,715
2107.01390	Memory and attention in deep learning	Intelligence necessitates memory. Without memory, humans fail to perform various nontrivial tasks such as reading novels, playing games or solving maths. As the ultimate goal of machine learning is to derive intelligent systems that learn and act automatically just like human, memory construction for machine is inevita...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	244,470
2201.13103	Detecting False Rumors from Retweet Dynamics on Social Media	False rumors are known to have detrimental effects on society. To prevent the spread of false rumors, social media platforms such as Twitter must detect them early. In this work, we develop a novel probabilistic mixture model that classifies true vs. false rumors based on the underlying spreading process. Specifically,...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	277,884
2412.07313	FaceX: Understanding Face Attribute Classifiers through Summary Model Explanations	EXplainable Artificial Intelligence (XAI) approaches are widely applied for identifying fairness issues in Artificial Intelligence (AI) systems. However, in the context of facial analysis, existing XAI approaches, such as pixel attribution methods, offer explanations for individual images, posing challenges in assessin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	515,619
1905.12413	VecHGrad for Solving Accurately Complex Tensor Decomposition	Tensor decomposition, a collection of factorization techniques for multidimensional arrays, are among the most general and powerful tools for scientific analysis. However, because of their increasing size, today's data sets require more complex tensor decomposition involving factorization with multiple matrices and dia...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,762
2410.03122	RIPPLECOT: Amplifying Ripple Effect of Knowledge Editing in Language Models via Chain-of-Thought In-Context Learning	The ripple effect poses a significant challenge in knowledge editing for large language models. Namely, when a single fact is edited, the model struggles to accurately update the related facts in a sequence, which is evaluated by multi-hop questions linked to a chain of related facts. Recent strategies have moved away ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	494,631
1705.10453	Twitter Hashtag Recommendation using Matrix Factorization	Twitter, one of the biggest and most popular microblogging Websites, has evolved into a powerful communication platform which allows millions of active users to generate huge volume of microposts and queries on a daily basis. To accommodate effective categorization and easy search, users are allowed to make use of hash...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	74,398
1710.11253	Approximation Algorithms for $\ell_0$-Low Rank Approximation	We study the $\ell_0$-Low Rank Approximation Problem, where the goal is, given an $m \times n$ matrix $A$, to output a rank-$k$ matrix $A'$ for which $\\|A'-A\\|_0$ is minimized. Here, for a matrix $B$, $\\|B\\|_0$ denotes the number of its non-zero entries. This NP-hard variant of low rank approximation is natural for pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	83,554
2012.01380	Deep Graph Neural Networks with Shallow Subgraph Samplers	While Graph Neural Networks (GNNs) are powerful models for learning representations on graphs, most state-of-the-art models do not have significant accuracy gain beyond two to three layers. Deep GNNs fundamentally need to address: 1). expressivity challenge due to oversmoothing, and 2). computation challenge due to nei...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,402
2306.01941	AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap	The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications are developed and deployed responsibly. However, a central pillar of re...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	370,683
2203.04860	PET: An Annotated Dataset for Process Extraction from Natural Language Text	Process extraction from text is an important task of process discovery, for which various approaches have been developed in recent years. However, in contrast to other information extraction tasks, there is a lack of gold-standard corpora of business process descriptions that are carefully annotated with all the entiti...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	284,629
1512.09295	Strategies and Principles of Distributed Machine Learning on Big Data	The rise of Big Data has led to new demands for Machine Learning (ML) systems to learn complex models with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics thereupon. In order to run ML algorithms at such scales, on a distributed clust...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	50,581
2402.07242	A Differentiable Model for Optimizing the Genetic Drivers of Synaptogenesis	There is a growing consensus among neuroscientists that many neural circuits critical for survival result from a process of genomic decompression, hence are constructed based on the information contained within the genome. Aligning with this perspective, we introduce SynaptoGen, a novel computational framework designed...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	428,625
2110.13658	Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?	Recent impressive improvements in NLP, largely based on the success of contextual neural language models, have been mostly demonstrated on at most a couple dozen high-resource languages. Building language models and, more generally, NLP systems for non-standardized and low-resource languages remains a challenging task....	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	263,269
2112.15403	Fast Graph Subset Selection Based on G-optimal Design	Graph sampling theory extends the traditional sampling theory to graphs with topological structures. As a key part of the graph sampling theory, subset selection chooses nodes on graphs as samples to reconstruct the original signal. Due to the eigen-decomposition operation for Laplacian matrices of graphs, however, exi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	273,775
2406.16253	LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing	This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and question answering, significantly reducing the time required for many routine tasks. On the other hand, researchers, whose work is not only time...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	467,055
1803.06360	Transport information geometry I: Riemannian calculus on probability simplex	We formulate the Riemannian calculus of the probability set embedded with $L^2$-Wasserstein metric. This is an initial work of transport information geometry. Our investigation starts with the probability simplex (probability manifold) supported on vertices of a finite graph. The main idea is to embed the probability m...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	92,816
2309.16248	Spider4SPARQL: A Complex Benchmark for Evaluating Knowledge Graph Question Answering Systems	With the recent spike in the number and availability of Large Language Models (LLMs), it has become increasingly important to provide large and realistic benchmarks for evaluating Knowledge Graph Question Answering (KGQA) systems. So far the majority of benchmarks rely on pattern-based SPARQL query generation approache...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	395,279
1206.3293	Propagation using Chain Event Graphs	A Chain Event Graph (CEG) is a graphial model which designed to embody conditional independencies in problems whose state spaces are highly asymmetric and do not admit a natural product structure. In this paer we present a probability propagation algorithm which uses the topology of the CEG to build a transporter CEG. ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	16,550
2307.00008	Investigating Masking-based Data Generation in Language Models	The current era of natural language processing (NLP) has been defined by the prominence of pre-trained language models since the advent of BERT. A feature of BERT and models with similar architecture is the objective of masked language modeling, in which part of the input is intentionally masked and the model is traine...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	376,832
2010.09904	Robust & Asymptotically Locally Optimal UAV-Trajectory Generation Based on Spline Subdivision	Generating locally optimal UAV-trajectories is challenging due to the non-convex constraints of collision avoidance and actuation limits. We present the first local, optimization-based UAV-trajectory generator that simultaneously guarantees the validity and asymptotic optimality for known environments. \textit{Validity...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	201,696
2406.17989	Learning Neural Networks with Sparse Activations	A core component present in many successful neural network architectures, is an MLP block of two fully connected layers with a non-linear activation in between. An intriguing phenomenon observed empirically, including in transformer architectures, is that, after training, the activations in the hidden layer of this MLP...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	467,823
2308.04352	3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment	3D vision-language grounding (3D-VL) is an emerging field that aims to connect the 3D physical world with natural language, which is crucial for achieving embodied intelligence. Current 3D-VL models rely heavily on sophisticated modules, auxiliary losses, and optimization tricks, which calls for a simple and unified mo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	384,379
2305.14378	Predicting Stock Market Time-Series Data using CNN-LSTM Neural Network Model	Stock market is often important as it represents the ownership claims on businesses. Without sufficient stocks, a company cannot perform well in finance. Predicting a stock market performance of a company is nearly hard because every time the prices of a company stock keeps changing and not constant. So, its complex to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	367,005
2202.10967	Learning Cluster Patterns for Abstractive Summarization	Nowadays, pre-trained sequence-to-sequence models such as BERTSUM and BART have shown state-of-the-art results in abstractive summarization. In these models, during fine-tuning, the encoder transforms sentences to context vectors in the latent space and the decoder learns the summary generation task based on the contex...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	281,728
2203.06832	Semi-Discrete Normalizing Flows through Differentiable Tessellation	Mapping between discrete and continuous distributions is a difficult task and many have had to resort to heuristical approaches. We propose a tessellation-based approach that directly learns quantization boundaries in a continuous space, complete with exact likelihood evaluations. This is done through constructing norm...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	285,235
1810.05065	Regularized Contextual Bandits	We consider the stochastic contextual bandit problem with additional regularization. The motivation comes from problems where the policy of the agent must be close to some baseline policy which is known to perform well on the task. To tackle this problem we use a nonparametric model and propose an algorithm splitting t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	110,159
2309.04427	Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach	Several domains increasingly rely on machine learning in their applications. The resulting heavy dependence on data has led to the emergence of various laws and regulations around data ethics and privacy and growing awareness of the need for privacy-preserving machine learning (ppML). Current ppML techniques utilize me...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	390,721
1802.00721	Improved Runtime Bounds for the Univariate Marginal Distribution Algorithm via Anti-Concentration	Unlike traditional evolutionary algorithms which produce offspring via genetic operators, Estimation of Distribution Algorithms (EDAs) sample solutions from probabilistic models which are learned from selected individuals. It is hoped that EDAs may improve optimisation performance on epistatic fitness landscapes by lea...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	89,461
1607.03881	Opinion Dynamics in Networks: Convergence, Stability and Lack of Explosion	Inspired by the work of [Kempe, Kleinberg, Oren, Slivkins, EC13] we introduce and analyze a model on opinion formation; the update rule of our dynamics is a simplified version of that of Kempe et. al. We assume that the population is partitioned into types whose interaction pattern is specified by a graph. Interaction ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	58,565
2310.17561	Bifurcations and loss jumps in RNN training	Recurrent neural networks (RNNs) are popular machine learning tools for modeling and forecasting sequential data and for inferring dynamical systems (DS) from observed time series. Concepts from DS theory (DST) have variously been used to further our understanding of both, how trained RNNs solve complex tasks, and the ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	403,189
2412.00789	A Cognac shot to forget bad memories: Corrective Unlearning in GNNs	Graph Neural Networks (GNNs) are increasingly being used for a variety of ML applications on graph data. Because graph data does not follow the independently and identically distributed (i.i.d.) assumption, adversarial manipulations or incorrect data can propagate to other data points through message passing, which det...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	512,820
1902.00625	Uncovering Political Promotion in China: A Network Analysis of Patronage Relationship in Autocracy	Understanding patronage networks in Chinese Bureaucracy helps us quantify the promotion mechanism underlying autocratic political systems. Although there are qualitative studies analyzing political promotions, few use quantitative methods to model promotions and make inferences on the fitted mathematical model. Using p...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	120,456
1504.01085	Stable Signal Recovery from Phaseless Measurements	The aim of this paper is to study the stability of the $\ell_1$ minimization for the compressive phase retrieval and to extend the instance-optimality in compressed sensing to the real phase retrieval setting. We first show that the $m={\mathcal O}(k\log(N/k))$ measurements is enough to guarantee the $\ell_1$ minimizat...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	41,767
2008.11815	An immersed phase field fracture model for fluid-infiltrating porous media with evolving Beavers-Joseph-Saffman condition	This study presents a phase field model for brittle fracture in fluid-infiltrating vuggy porous media. While the state-of-the-art in hydraulic phase field fracture considers Darcian fracture flow with enhanced permeability along the crack, in this study, the phase field not only acts as a damage variable that provides ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	193,381
2108.06895	Interpreting Attributions and Interactions of Adversarial Attacks	This paper aims to explain adversarial attacks in terms of how adversarial perturbations contribute to the attacking task. We estimate attributions of different image regions to the decrease of the attacking cost based on the Shapley value. We define and quantify interactions among adversarial perturbation pixels, and ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	250,766
1810.09832	Mechanism Design for Social Good	Across various domains--such as health, education, and housing--improving societal welfare involves allocating resources, setting policies, targeting interventions, and regulating activities. These solutions have an immense impact on the day-to-day lives of individuals, whether in the form of access to quality healthca...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	true	111,141
2103.06859	Understanding the Origin of Information-Seeking Exploration in Probabilistic Objectives for Control	The exploration-exploitation trade-off is central to the description of adaptive behaviour in fields ranging from machine learning, to biology, to economics. While many approaches have been taken, one approach to solving this trade-off has been to equip or propose that agents possess an intrinsic 'exploratory drive' wh...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	224,420
1908.11813	Multi-Task Learning with Language Modeling for Question Generation	This paper explores the task of answer-aware questions generation. Based on the attention-based pointer generator model, we propose to incorporate an auxiliary task of language modeling to help question generation in a hierarchical multi-task learning structure. Our joint-learning model enables the encoder to learn a b...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	143,477
1605.05716	Space-Time Codes Based on Rank-Metric Codes and Their Decoding	We propose a new class of space-time block codes based on finite-field rank-metric codes in combination with a rank-metric-preserving mapping to the set of Eisenstein integers. It is shown that these codes achieve maximum diversity order and improve upon certain existing constructions. Moreover, we present a new decodi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	56,035
2206.06568	Distributed and Distribution-Robust Meta Reinforcement Learning (D2-RMRL) for Data Pre-storing and Routing in Cube Satellite Networks	In this paper, the problem of data pre-storing and routing in dynamic, resource-constrained cube satellite networks is studied. In such a network, each cube satellite delivers requested data to user clusters under its coverage. A group of ground gateways will route and pre-store certain data to the satellites, such tha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	302,417
2203.09088	Deep Point Cloud Simplification for High-quality Surface Reconstruction	The growing size of point clouds enlarges consumptions of storage, transmission, and computation of 3D scenes. Raw data is redundant, noisy, and non-uniform. Therefore, simplifying point clouds for achieving compact, clean, and uniform points is becoming increasingly important for 3D vision and graphics tasks. Previous...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	286,021
2409.04880	Towards identifying Source credibility on Information Leakage in Digital Gadget Market	The use of Social media to share content is on a constant rise. One of the capsize effect of information sharing on Social media includes the spread of sensitive information on the public domain. With the digital gadget market becoming highly competitive and ever-evolving, the trend of an increasing number of sensitive...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	486,546
1710.10883	Weak Stability of $\ell_1$-minimization Methods in Sparse Data Reconstruction	As one of the most plausible convex optimization methods for sparse data reconstruction, $\ell_1$-minimization plays a fundamental role in the development of sparse optimization theory. The stability of this method has been addressed in the literature under various assumptions such as restricted isometry property (RIP)...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	83,489
2312.14035	GRIL-Calib: Targetless Ground Robot IMU-LiDAR Extrinsic Calibration Method using Ground Plane Motion Constraints	Targetless IMU-LiDAR extrinsic calibration methods are gaining significant attention as the importance of the IMU-LiDAR fusion system increases. Notably, existing calibration methods derive calibration parameters under the assumption that the methods require full motion in all axes. When IMU and LiDAR are mounted on a ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	417,485
2311.13735	Surpassing GPT-4 Medical Coding with a Two-Stage Approach	Recent advances in large language models (LLMs) show potential for clinical applications, such as clinical decision support and trial recommendations. However, the GPT-4 LLM predicts an excessive number of ICD codes for medical coding tasks, leading to high recall but low precision. To tackle this challenge, we introdu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	409,853
2306.13361	Neural 360$^\circ$ Structured Light with Learned Metasurfaces	Structured light has proven instrumental in 3D imaging, LiDAR, and holographic light projection. Metasurfaces, comprised of sub-wavelength-sized nanostructures, facilitate 180$^\circ$ field-of-view (FoV) structured light, circumventing the restricted FoV inherent in traditional optics like diffractive optical elements....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	375,253
1910.03112	Application of Machine Learning in Forecasting International Trade Trends	International trade policies have recently garnered attention for limiting cross-border exchange of essential goods (e.g. steel, aluminum, soybeans, and beef). Since trade critically affects employment and wages, predicting future patterns of trade is a high-priority for policy makers around the world. While traditiona...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	148,413
1303.5427	Possibilistic Constraint Satisfaction Problems or "How to handle soft constraints?"	Many AI synthesis problems such as planning or scheduling may be modelized as constraint satisfaction problems (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many real tasks such as j...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,115
2008.00305	Self-supervised Learning of Point Clouds via Orientation Estimation	Point clouds provide a compact and efficient representation of 3D shapes. While deep neural networks have achieved impressive results on point cloud learning tasks, they require massive amounts of manually labeled data, which can be costly and time-consuming to collect. In this paper, we leverage 3D self-supervision fo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	189,968
1303.2108	Classification of Segments in PolSAR Imagery by Minimum Stochastic Distances Between Wishart Distributions	A new classifier for Polarimetric SAR (PolSAR) images is proposed and assessed in this paper. Its input consists of segments, and each one is assigned the class which minimizes a stochastic distance. Assuming the complex Wishart model, several stochastic distances are obtained from the h-phi family of divergences, and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	22,791
2011.04405	Combining Propositional Logic Based Decision Diagrams with Decision Making in Urban Systems	Solving multiagent problems can be an uphill task due to uncertainty in the environment, partial observability, and scalability of the problem at hand. Especially in an urban setting, there are more challenges since we also need to maintain safety for all users while minimizing congestion of the agents as well as their...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	205,573
2012.15353	Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings	Models based on the transformer architecture, such as BERT, have marked a crucial step forward in the field of Natural Language Processing. Importantly, they allow the creation of word embeddings that capture important semantic information about words in context. However, as single entities, these embeddings are diffic...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	213,752
1401.6571	Keyword and Keyphrase Extraction Using Centrality Measures on Collocation Networks	Keyword and keyphrase extraction is an important problem in natural language processing, with applications ranging from summarization to semantic search to document clustering. Graph-based approaches to keyword and keyphrase extraction avoid the problem of acquiring a large in-domain training corpus by applying variant...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	30,373
1805.08313	Learning Safe Policies with Expert Guidance	We propose a framework for ensuring safe behavior of a reinforcement learning agent when the reward function may be difficult to specify. In order to do this, we rely on the existence of demonstrations from expert policies, and we provide a theoretical framework for the agent to optimize in the space of rewards consist...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	98,099
2306.06663	LF-PGVIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments	In this paper, we propose LF-PGVIO, a Visual-Inertial-Odometry (VIO) framework for large Field-of-View (FoV) cameras with a negative plane using points and geodesic segments. The purpose of our research is to unleash the potential of point-line odometry with large-FoV omnidirectional cameras, even for cameras with nega...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	372,703
2103.16935	Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network	Near-field Acoustic Holography (NAH) is a well-known problem aimed at estimating the vibrational velocity field of a structure by means of acoustic measurements. In this paper, we propose a NAH technique based on Convolutional Neural Network (CNN). The devised CNN predicts the vibrational field on the surface of arbitr...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	227,748
1308.5576	A Comparison of Algorithms for Learning Hidden Variables in Normal Graphs	A Bayesian factor graph reduced to normal form consists in the interconnection of diverter units (or equal constraint units) and Single-Input/Single-Output (SISO) blocks. In this framework localized adaptation rules are explicitly derived from a constrained maximum likelihood (ML) formulation and from a minimum KL-dive...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	26,649
2203.07697	Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation	In this paper, we present a novel Distribution-Aware Single-stage (DAS) model for tackling the challenging multi-person 3D pose estimation problem. Different from existing top-down and bottom-up methods, the proposed DAS model simultaneously localizes person positions and their corresponding body joints in the 3D camer...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	285,526
2002.05769	The Efficiency of Human Cognition Reflects Planned Information Processing	Planning is useful. It lets people take actions that have desirable long-term consequences. But, planning is hard. It requires thinking about consequences, which consumes limited computational and cognitive resources. Thus, people should plan their actions, but they should also be smart about how they deploy resources ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	163,996
1806.08899	Robust Navigation In GNSS Degraded Environment Using Graph Optimization	Robust navigation in urban environments has received a considerable amount of both academic and commercial interest over recent years. This is primarily due to large commercial organizations such as Google and Uber stepping into the autonomous navigation market. Most of this research has shied away from Global Navigati...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	101,245
2401.16658	OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer	Recent studies have highlighted the importance of fully open foundation models. The Open Whisper-style Speech Model (OWSM) is an initial step towards reproducing OpenAI Whisper using public data and open-source toolkits. However, previous versions of OWSM (v1 to v3) are still based on standard Transformer, which might ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	424,924
1809.09621	Inferring Complementary Products from Baskets and Browsing Sessions	Complementary products recommendation is an important problem in e-commerce. Such recommendations increase the average order price and the number of products in baskets. Complementary products are typically inferred from basket data. In this study, we propose the BB2vec model. The BB2vec model learns vector representat...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	108,747
2306.10830	3D VR Sketch Guided 3D Shape Prototyping and Exploration	3D shape modeling is labor-intensive, time-consuming, and requires years of expertise. To facilitate 3D shape modeling, we propose a 3D shape generation network that takes a 3D VR sketch as a condition. We assume that sketches are created by novices without art training and aim to reconstruct geometrically realistic 3D...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	374,383
2407.10805	Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation	Retrieval-augmented generation (RAG) has improved large language models (LLMs) by using knowledge retrieval to overcome knowledge deficiencies. However, current RAG methods often fall short of ensuring the depth and completeness of retrieved information, which is necessary for complex reasoning tasks. In this work, we ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	473,135
2306.10228	CStream: Parallel Data Stream Compression on Multicore Edge Devices	In the burgeoning realm of Internet of Things (IoT) applications on edge devices, data stream compression has become increasingly pertinent. The integration of added compression overhead and limited hardware resources on these devices calls for a nuanced software-hardware co-design. This paper introduces CStream, a pio...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	374,151
2209.11767	Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG	In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefit from BCIs as a means of communication and for the restoration of motor functions. Electroencephalography (EEG) is one of most used for evaluating the neuronal act...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	319,292
1806.04965	The streaming rollout of deep networks - towards fully model-parallel execution	Deep neural networks, and in particular recurrent networks, are promising candidates to control autonomous agents that interact in real-time with the physical world. However, this requires a seamless integration of temporal features into the network's architecture. For the training of and inference with recurrent neura...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	100,361

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