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
1710.00519	Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms	In NLP, convolutional neural networks (CNNs) have benefited less than recurrent neural networks (RNNs) from attention mechanisms. We hypothesize that this is because the attention in CNNs has been mainly implemented as attentive pooling (i.e., it is applied to pooling) rather than as attentive convolution (i.e., it is ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	81,879
2501.04012	FlexCache: Flexible Approximate Cache System for Video Diffusion	Text-to-Video applications receive increasing attention from the public. Among these, diffusion models have emerged as the most prominent approach, offering impressive quality in visual content generation. However, it still suffers from substantial computational complexity, often requiring several minutes to generate a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	523,076
2410.00352	Interleaved One-Shot SPS Performance under Smart DoS Attacks in C-V2X Networks	This paper evaluates the performance of the one-shot Semi-Persistent Scheduling (SPS) mechanism in Cellular Vehicle-to-Everything (C-V2X) networks under Denial-of-Service (DoS) smart attack scenarios. The study focuses on the impact of these attacks on key performance metrics, including Packet Delivery Ratio (PDR), Int...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	493,332
2409.10681	Online Diffusion-Based 3D Occupancy Prediction at the Frontier with Probabilistic Map Reconciliation	Autonomous navigation and exploration in unmapped environments remains a significant challenge in robotics due to the difficulty robots face in making commonsense inference of unobserved geometries. Recent advancements have demonstrated that generative modeling techniques, particularly diffusion models, can enable syst...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	488,841
1603.02023	Supervisor Localization of Timed Discrete-Event Systems under Partial Observation and Communication Delay	We study supervisor localization for timed discrete-event systems under partial observation and communication delay in the Brandin-Wonham framework. First, we employ timed relative observability to synthesize a partial-observation monolithic supervisor; the control actions of this supervisor include not only disabling ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	52,971
2106.11879	Asynchronous Stochastic Optimization Robust to Arbitrary Delays	We consider stochastic optimization with delayed gradients where, at each time step $t$, the algorithm makes an update using a stale stochastic gradient from step $t - d_t$ for some arbitrary delay $d_t$. This setting abstracts asynchronous distributed optimization where a central server receives gradient updates compu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	242,539
2411.19387	Enhancing Accuracy and Efficiency in Calibration of Drinking Water Distribution Networks Through Evolutionary Artificial Neural Networks and Expert Systems	The importance of drinking water distribution networks (DWDNs) as critical urban infrastructures has led to the development and utilization of models for the analysis, design, operation, and management of DWDNs, to ensure optimal efficiency and water quality. In order to provide models that accurately represent real-wo...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	512,223
1810.07225	Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories	Predicting the motion of a mobile agent from a third-person perspective is an important component for many robotics applications, such as autonomous navigation and tracking. With accurate motion prediction of other agents, robots can plan for more intelligent behaviors to achieve specified objectives, instead of acting...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	110,594
2312.16388	Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding	In the weakly supervised temporal video grounding study, previous methods use predetermined single Gaussian proposals which lack the ability to express diverse events described by the sentence query. To enhance the expression ability of a proposal, we propose a Gaussian mixture proposal (GMP) that can depict arbitrary ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	418,368
2502.06126	Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond	Understanding disease progression at the molecular pathway level usually requires capturing both structural dependencies between pathways and the temporal dynamics of disease evolution. In this work, we solve the former challenge by developing a biologically informed graph-forming method to efficiently construct pathwa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	531,936
2303.03553	Robust Dominant Periodicity Detection for Time Series with Missing Data	Periodicity detection is an important task in time series analysis, but still a challenging problem due to the diverse characteristics of time series data like abrupt trend change, outlier, noise, and especially block missing data. In this paper, we propose a robust and effective periodicity detection algorithm for tim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	349,767
2105.08475	AI and Shared Prosperity	Future advances in AI that automate away human labor may have stark implications for labor markets and inequality. This paper proposes a framework to analyze the effects of specific types of AI systems on the labor market, based on how much labor demand they will create versus displace, while taking into account that p...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	235,766
2203.01069	Enhanced Decentralized Autonomous Aerial Robot Teams with Group Planning	Designing autonomous aerial robot team systems remains a grand challenge in robotics. Existing works in this field can be categorized as centralized and decentralized. Centralized methods suffer from scale dilemmas, while decentralized ones often lead to poor planning quality. In this paper, we propose an enhanced dece...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	283,241
2404.00608	Sample Complexity of Chance Constrained Optimization in Dynamic Environment	We study the scenario approach for solving chance-constrained optimization in time-coupled dynamic environments. Scenario generation methods approximate the true feasible region from scenarios generated independently and identically from the actual distribution. In this paper, we consider this problem in a dynamic envi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	443,021
2004.07778	Privacy-Preserving Policy Synthesis in Markov Decision Processes	In decision-making problems, the actions of an agent may reveal sensitive information that drives its decisions. For instance, a corporation's investment decisions may reveal its sensitive knowledge about market dynamics. To prevent this type of information leakage, we introduce a policy synthesis algorithm that protec...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	172,873
1706.07535	Efficient Approximate Solutions to Mutual Information Based Global Feature Selection	Mutual Information (MI) is often used for feature selection when developing classifier models. Estimating the MI for a subset of features is often intractable. We demonstrate, that under the assumptions of conditional independence, MI between a subset of features can be expressed as the Conditional Mutual Information (...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	75,864
1909.06161	Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs	Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream. While initially inspired by brain anatomy, over the past years, these ANNs have evolved from a simple eight-layer architecture in AlexNet to extremely deep...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	145,308
2312.12544	The Dark Side of NFTs: A Large-Scale Empirical Study of Wash Trading	NFTs (Non-Fungible Tokens) have seen significant growth since they first captured public attention in 2021. However, the NFT market is plagued by fake transactions and economic bubbles, e.g., NFT wash trading. Wash trading typically refers to a transaction involving the same person or two colluding individuals, and has...	false	true	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	416,999
2306.11014	Physics Constrained Unsupervised Deep Learning for Rapid, High Resolution Scanning Coherent Diffraction Reconstruction	By circumventing the resolution limitations of optics, coherent diffractive imaging (CDI) and ptychography are making their way into scientific fields ranging from X-ray imaging to astronomy. Yet, the need for time consuming iterative phase recovery hampers real-time imaging. While supervised deep learning strategies h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	374,447
2408.09976	Preference-Optimized Pareto Set Learning for Blackbox Optimization	Multi-Objective Optimization (MOO) is an important problem in real-world applications. However, for a non-trivial problem, no single solution exists that can optimize all the objectives simultaneously. In a typical MOO problem, the goal is to find a set of optimum solutions (Pareto set) that trades off the preferences ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	481,672
2005.10539	An approach to Beethoven's 10th Symphony	Ludwig van Beethoven composed his symphonies between 1799 and 1825, when he was writing his Tenth symphony. As we dispose of a great amount of data belonging to his work, the purpose of this paper is to investigate the possibility of extracting patterns on his compositional model from symbolic data and generate what wo...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	178,212
2107.08593	Inverse Problem of Nonlinear Schr\"odinger Equation as Learning of Convolutional Neural Network	In this work, we use an explainable convolutional neural network (NLS-Net) to solve an inverse problem of the nonlinear Schr\"odinger equation, which is widely used in fiber-optic communications. The landscape and minimizers of the non-convex loss function of the learning problem are studied empirically. It provides a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	246,777
2304.07145	EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments	EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios. Recommender systems are often evaluated only through accuracy metrics, which fall short of fully character...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	358,247
1103.0744	Model Identification of a Network as Compressing Sensing	In many applications, it is important to derive information about the topology and the internal connections of dynamical systems interacting together. Examples can be found in fields as diverse as Economics, Neuroscience and Biochemistry. The paper deals with the problem of deriving a descriptive model of a network, co...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	9,469
2104.13162	Communicating with Extremely Large-Scale Array/Surface: Unified Modelling and Performance Analysis	Wireless communications with extremely large-scale array (XL-array) correspond to systems whose antenna sizes are so large that conventional modelling assumptions, such as uniform plane wave (UPW) impingement, are longer valid. This paper studies the mathematical modelling and performance analysis of XL-array communica...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	232,416
2204.03390	Electricity generation from renewable energy based on abandoned wind fan	In the 21st century, our world is facing difficult conditions for serious environmental pollution and the problem of energy shortage. An innovative idea has emerged to recycle wind energy from air conditioning condenser fans in outdoor buildings. Therefore, the main goal of this research is to develop renewable wind en...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	290,289
2412.01864	Learning Aggregation Rules in Participatory Budgeting: A Data-Driven Approach	Participatory Budgeting (PB) offers a democratic process for communities to allocate public funds across various projects through voting. In practice, PB organizers face challenges in selecting aggregation rules either because they are not familiar with the literature and the exact details of every existing rule or bec...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	true	513,298
1802.03345	A Two-Stage Method for Text Line Detection in Historical Documents	This work presents a two-stage text line detection method for historical documents. Each detected text line is represented by its baseline. In a first stage, a deep neural network called ARU-Net labels pixels to belong to one of the three classes: baseline, separator or other. The separator class marks beginning and en...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	89,951
2007.12729	Detecting malicious PDF using CNN	Malicious PDF files represent one of the biggest threats to computer security. To detect them, significant research has been done using handwritten signatures or machine learning based on manual feature extraction. Those approaches are both time-consuming, require significant prior knowledge and the list of features ha...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	188,902
2106.14503	Weight Divergence Driven Divide-and-Conquer Approach for Optimal Federated Learning from non-IID Data	Federated Learning allows training of data stored in distributed devices without the need for centralizing training data, thereby maintaining data privacy. Addressing the ability to handle data heterogeneity (non-identical and independent distribution or non-IID) is a key enabler for the wider deployment of Federated L...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	243,437
1910.01112	Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data	We propose a novel unsupervised generative model that learns to disentangle object identity from other low-level aspects in class-imbalanced data. We first investigate the issues surrounding the assumptions about uniformity made by InfoGAN, and demonstrate its ineffectiveness to properly disentangle object identity in ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	147,844
2211.01481	Physics-inspired machine learning for power grid frequency modelling	The operation of power systems is affected by diverse technical, economic and social factors. Social behaviour determines load patterns, electricity markets regulate the generation and weather-dependent renewables introduce power fluctuations. Thus, power system dynamics must be regarded as a non-autonomous system whos...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	328,239
2403.00041	Global and Local Prompts Cooperation via Optimal Transport for Federated Learning	Prompt learning in pretrained visual-language models has shown remarkable flexibility across various downstream tasks. Leveraging its inherent lightweight nature, recent research attempted to integrate the powerful pretrained models into federated learning frameworks to simultaneously reduce communication costs and pro...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	433,844
cs/0511098	Information and Stock Prices: A Simple Introduction	This article summarizes recent research in financial economics about why information, such as earnings announcements, moves stock prices. The article does not presume any prior exposure to finance beyond what you might read in newspapers.	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	539,109
1912.12141	encointer -- Local Community Cryptocurrencies with Universal Basic Income	Encointer proposes a blockchain platform for local community cryptocurrencies. Individuals can claim a universal basic income through issuance of fresh money. Money supply is kept in proportion to population size through the use of demurrage. Sybil attacks are prevented by regular, concurrent and randomized pseudonym k...	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	158,773
2002.07348	Adaptive Region-Based Active Learning	We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region, both phases actively requesting labels. We prove theoretical guarantees for both the generalization error and the label complexity of our al...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	164,440
2002.06397	Open Knowledge Enrichment for Long-tail Entities	Knowledge bases (KBs) have gradually become a valuable asset for many AI applications. While many current KBs are quite large, they are widely acknowledged as incomplete, especially lacking facts of long-tail entities, e.g., less famous persons. Existing approaches enrich KBs mainly on completing missing links or filli...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	164,175
2409.19963	A Self-attention Residual Convolutional Neural Network for Health Condition Classification of Cow Teat Images	Milk is a highly important consumer for Americans and the health of the cows' teats directly affects the quality of the milk. Traditionally, veterinarians manually assessed teat health by visually inspecting teat-end hyperkeratosis during the milking process which is limited in time, usually only tens of seconds, and w...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	492,934
2306.06839	Single-Integrator Consensus Dynamics over Minimally Reactive Networks	The problem of achieving consensus in a network of connected systems arises in many science and engineering applications. In contrast to previous works, we focus on the system reactivity, i.e., the initial amplification of the norm of the system states. We identify a class of networks that we call minimally reactive, w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	372,772
2007.12371	Dopant Network Processing Units: Towards Efficient Neural-network Emulators with High-capacity Nanoelectronic Nodes	The rapidly growing computational demands of deep neural networks require novel hardware designs. Recently, tunable nanoelectronic devices were developed based on hopping electrons through a network of dopant atoms in silicon. These "Dopant Network Processing Units" (DNPUs) are highly energy-efficient and have potentia...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	188,802
2107.11587	Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?	We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent. We find that on an environment that requires multimodal posterior predictives, mixture density nets outperform all other models by a large margin. When m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,623
2108.06228	One-shot Transfer Learning for Population Mapping	Fine-grained population distribution data is of great importance for many applications, e.g., urban planning, traffic scheduling, epidemic modeling, and risk control. However, due to the limitations of data collection, including infrastructure density, user privacy, and business security, such fine-grained data is hard...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	250,542
2308.13479	Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages	Large language models (LLMs) are increasingly capable and prevalent, and can be used to produce creative content. The quality of content is influenced by the prompt used, with more specific prompts that incorporate examples generally producing better results. On from this, it could be seen that using instructions writt...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	387,933
1603.01595	Sentiment Analysis in Scholarly Book Reviews	So far different studies have tackled the sentiment analysis in several domains such as restaurant and movie reviews. But, this problem has not been studied in scholarly book reviews which is different in terms of review style and size. In this paper, we propose to combine different features in order to be presented to...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	52,908
2211.13785	PuzzleFusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving	This paper presents an end-to-end neural architecture based on Diffusion Models for spatial puzzle solving, particularly jigsaw puzzle and room arrangement tasks. In the latter task, for instance, the proposed system "PuzzleFusion" takes a set of room layouts as polygonal curves in the top-down view and aligns the room...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	332,594
1705.00335	Quantifying Mental Health from Social Media with Neural User Embeddings	Mental illnesses adversely affect a significant proportion of the population worldwide. However, the methods traditionally used for estimating and characterizing the prevalence of mental health conditions are time-consuming and expensive. Consequently, best-available estimates concerning the prevalence of mental health...	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	false	false	false	72,658
2009.11087	Probabilistic Machine Learning for Healthcare	Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline wher...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	197,066
2110.02577	Efficient Multi-Modal Embeddings from Structured Data	Multi-modal word semantics aims to enhance embeddings with perceptual input, assuming that human meaning representation is grounded in sensory experience. Most research focuses on evaluation involving direct visual input, however, visual grounding can contribute to linguistic applications as well. Another motivation fo...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	259,174
2206.08569	Bootstrapped Transformer for Offline Reinforcement Learning	Offline reinforcement learning (RL) aims at learning policies from previously collected static trajectory data without interacting with the real environment. Recent works provide a novel perspective by viewing offline RL as a generic sequence generation problem, adopting sequence models such as Transformer architecture...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	303,203
0809.2639	Code diversity in multiple antenna wireless communication	The standard approach to the design of individual space-time codes is based on optimizing diversity and coding gains. This geometric approach leads to remarkable examples, such as perfect space-time block codes, for which the complexity of Maximum Likelihood (ML) decoding is considerable. Code diversity is an alternati...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,349
2307.09542	Can Neural Network Memorization Be Localized?	Recent efforts at explaining the interplay of memorization and generalization in deep overparametrized networks have posited that neural networks $\textit{memorize}$ "hard" examples in the final few layers of the model. Memorization refers to the ability to correctly predict on $\textit{atypical}$ examples of the train...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	380,204
2410.12659	Non-Conservative Obstacle Avoidance for Multi-Body Systems Leveraging Convex Hulls and Predicted Closest Points	This paper introduces a novel approach that integrates future closest point predictions into the distance constraints of a collision avoidance controller, leveraging convex hulls with closest point distance calculations. By addressing abrupt shifts in closest points, this method effectively reduces collision risks and ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	499,130
2409.08376	Learned Compression for Images and Point Clouds	Over the last decade, deep learning has shown great success at performing computer vision tasks, including classification, super-resolution, and style transfer. Now, we apply it to data compression to help build the next generation of multimedia codecs. This thesis provides three primary contributions to this new field...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	487,878
2102.05375	Strength of Minibatch Noise in SGD	The noise in stochastic gradient descent (SGD), caused by minibatch sampling, is poorly understood despite its practical importance in deep learning. This work presents the first systematic study of the SGD noise and fluctuations close to a local minimum. We first analyze the SGD noise in linear regression in detail an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,418
2403.06592	Exploiting Style Latent Flows for Generalizing Deepfake Video Detection	This paper presents a new approach for the detection of fake videos, based on the analysis of style latent vectors and their abnormal behavior in temporal changes in the generated videos. We discovered that the generated facial videos suffer from the temporal distinctiveness in the temporal changes of style latent vect...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	436,526
2409.15028	Region Mixup	This paper introduces a simple extension of mixup (Zhang et al., 2018) data augmentation to enhance generalization in visual recognition tasks. Unlike the vanilla mixup method, which blends entire images, our approach focuses on combining regions from multiple images.	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	490,728
2106.07393	Cross-replication Reliability -- An Empirical Approach to Interpreting Inter-rater Reliability	We present a new approach to interpreting IRR that is empirical and contextualized. It is based upon benchmarking IRR against baseline measures in a replication, one of which is a novel cross-replication reliability (xRR) measure based on Cohen's kappa. We call this approach the xRR framework. We opensource a replicati...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	240,911
2106.13697	Active Learning in Robotics: A Review of Control Principles	Active learning is a decision-making process. In both abstract and physical settings, active learning demands both analysis and action. This is a review of active learning in robotics, focusing on methods amenable to the demands of embodied learning systems. Robots must be able to learn efficiently and flexibly through...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	243,152
2203.16515	Fast Light-Weight Near-Field Photometric Stereo	We introduce the first end-to-end learning-based solution to near-field Photometric Stereo (PS), where the light sources are close to the object of interest. This setup is especially useful for reconstructing large immobile objects. Our method is fast, producing a mesh from 52 512$\times$384 resolution images in about ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	288,813
1903.12243	DEEP-FRI: Sampling outside the box improves soundness	Motivated by the quest for scalable and succinct zero knowledge arguments, we revisit worst-case-to-average-case reductions for linear spaces, raised by [Rothblum, Vadhan, Wigderson, STOC 2013]. We first show a sharp quantitative form of a theorem which says that if an affine space $U$ is $\delta$-far in relative Hammi...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	true	125,667
1908.00722	Learning to combine primitive skills: A step towards versatile robotic manipulation	Manipulation tasks such as preparing a meal or assembling furniture remain highly challenging for robotics and vision. Traditional task and motion planning (TAMP) methods can solve complex tasks but require full state observability and are not adapted to dynamic scene changes. Recent learning methods can operate direct...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	140,581
1503.03175	Benchmarking NLopt and state-of-art algorithms for Continuous Global Optimization via Hybrid IACO$_\mathbb{R}$	This paper presents a comparative analysis of the performance of the Incremental Ant Colony algorithm for continuous optimization ($IACO_\mathbb{R}$), with different algorithms provided in the NLopt library. The key objective is to understand how the various algorithms in the NLopt library perform in combination with t...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	41,021
2105.12789	RSCA: Real-time Segmentation-based Context-Aware Scene Text Detection	Segmentation-based scene text detection methods have been widely adopted for arbitrary-shaped text detection recently, since they make accurate pixel-level predictions on curved text instances and can facilitate real-time inference without time-consuming processing on anchors. However, current segmentation-based models...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	237,095
2007.06544	Free-running SIMilarity-Based Angiography (SIMBA) for simplified anatomical MR imaging of the heart	Purpose: Whole-heart MRA techniques typically target pre-determined motion states and address cardiac and respiratory dynamics independently. We propose a novel fast reconstruction algorithm, applicable to ungated free-running sequences, that leverages inherent similarities in the acquired data to avoid such physiologi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	187,041
1202.0351	The weighted tunable clustering in local-world networks with incremental behaviors	Since some realistic networks are influenced not only by increment behavior but also by tunable clustering mechanism with new nodes to be added to networks, it is interesting to characterize the model for those actual networks. In this paper, a weighted local-world model, which incorporates increment behavior and tunab...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	14,074
2502.05505	Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model	Differentially private (DP) synthetic data, which closely resembles the original private data while maintaining strong privacy guarantees, has become a key tool for unlocking the value of private data without compromising privacy. Recently, Private Evolution (PE) has emerged as a promising method for generating DP synt...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	531,646
1805.04720	Do Outliers Ruin Collaboration?	We consider the problem of learning a binary classifier from $n$ different data sources, among which at most an $\eta$ fraction are adversarial. The overhead is defined as the ratio between the sample complexity of learning in this setting and that of learning the same hypothesis class on a single data distribution. We...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	97,293
2308.05742	A Characterization of Entropy as a Universal Monoidal Natural Transformation	We show that the essential properties of entropy (monotonicity, additivity and subadditivity) are consequences of entropy being a monoidal natural transformation from the under category functor $-/\mathsf{LProb}_{\rho}$ (where $\mathsf{LProb}_{\rho}$ is category of $\rho$-th-power-summable probability distributions, $0...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	384,893
2210.13706	Gaussian Mean Testing Made Simple	We study the following fundamental hypothesis testing problem, which we term Gaussian mean testing. Given i.i.d. samples from a distribution $p$ on $\mathbb{R}^d$, the task is to distinguish, with high probability, between the following cases: (i) $p$ is the standard Gaussian distribution, $\mathcal{N}(0,I_d)$, and (ii...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	326,270
2409.09621	Stutter-Solver: End-to-end Multi-lingual Dysfluency Detection	Current de-facto dysfluency modeling methods utilize template matching algorithms which are not generalizable to out-of-domain real-world dysfluencies across languages, and are not scalable with increasing amounts of training data. To handle these problems, we propose Stutter-Solver: an end-to-end framework that detect...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	488,399
2406.16374	KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning	Knowledge-enhanced pre-trained language models (KEPLMs) leverage relation triples from knowledge graphs (KGs) and integrate these external data sources into language models via self-supervised learning. Previous works treat knowledge enhancement as two independent operations, i.e., knowledge injection and knowledge int...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	467,110
2303.01081	Can BERT Refrain from Forgetting on Sequential Tasks? A Probing Study	Large pre-trained language models help to achieve state of the art on a variety of natural language processing (NLP) tasks, nevertheless, they still suffer from forgetting when incrementally learning a sequence of tasks. To alleviate this problem, recent works enhance existing models by sparse experience replay and loc...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	348,826
1905.11472	End-to-End Pore Extraction and Matching in Latent Fingerprints: Going Beyond Minutiae	Latent fingerprint recognition is not a new topic but it has attracted a lot of attention from researchers in both academia and industry over the past 50 years. With the rapid development of pattern recognition techniques, automated fingerprint identification systems (AFIS) have become more and more ubiquitous. However...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	132,428
2205.09801	Representation Power of Graph Neural Networks: Improved Expressivity via Algebraic Analysis	Despite the remarkable success of Graph Neural Networks (GNNs), the common belief is that their representation power is limited and that they are at most as expressive as the Weisfeiler-Lehman (WL) algorithm. In this paper, we argue the opposite and show that standard GNNs, with anonymous inputs, produce more discrimin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	297,417
1711.01287	Discovering More Precise Process Models from Event Logs by Filtering Out Chaotic Activities	Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that business process. Real life event logs can contain chaotic activities. These activities are independent of the state of the process and can, therefore, happen at rather arbitrary...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	true	false	83,853
2002.11923	The Effectiveness of Johnson-Lindenstrauss Transform for High Dimensional Optimization With Adversarial Outliers, and the Recovery	In this paper, we consider robust optimization problems in high dimensions. Because a real-world dataset may contain significant noise or even specially crafted samples from some attacker, we are particularly interested in the optimization problems with arbitrary (and potentially adversarial) outliers. We focus on two ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	165,881
2404.15353	SQUWA: Signal Quality Aware DNN Architecture for Enhanced Accuracy in Atrial Fibrillation Detection from Noisy PPG Signals	Atrial fibrillation (AF), a common cardiac arrhythmia, significantly increases the risk of stroke, heart disease, and mortality. Photoplethysmography (PPG) offers a promising solution for continuous AF monitoring, due to its cost efficiency and integration into wearable devices. Nonetheless, PPG signals are susceptible...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	449,063
2008.06924	Inverse Reinforcement Learning with Natural Language Goals	Humans generally use natural language to communicate task requirements to each other. Ideally, natural language should also be usable for communicating goals to autonomous machines (e.g., robots) to minimize friction in task specification. However, understanding and mapping natural language goals to sequences of states...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	191,935
1904.08459	Stock Forecasting using M-Band Wavelet-Based SVR and RNN-LSTMs Models	The task of predicting future stock values has always been one that is heavily desired albeit very difficult. This difficulty arises from stocks with non-stationary behavior, and without any explicit form. Hence, predictions are best made through analysis of financial stock data. To handle big data sets, current conven...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	128,062
1907.09673	Multilevel Monte-Carlo for Solving POMDPs Online	Planning under partial obervability is essential for autonomous robots. A principled way to address such planning problems is the Partially Observable Markov Decision Process (POMDP). Although solving POMDPs is computationally intractable, substantial advancements have been achieved in developing approximate POMDP solv...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	139,439
2202.09741	Visual Attention Network	While originally designed for natural language processing tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	281,304
2101.05791	U-Noise: Learnable Noise Masks for Interpretable Image Segmentation	Deep Neural Networks (DNNs) are widely used for decision making in a myriad of critical applications, ranging from medical to societal and even judicial. Given the importance of these decisions, it is crucial for us to be able to interpret these models. We introduce a new method for interpreting image segmentation mode...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	215,521
2104.14046	Ten-tier and multi-scale supplychain network analysis of medical equipment: Random failure and intelligent attack analysis	Motivated by the COVID-19 pandemic, this paper explores the supply chain viability of medical equipment, an industry whose supply chain was put under a crucial test during the pandemic. This paper includes an empirical network-level analysis of supplier reachability under Random Failure Experiment (RFE) and Intelligent...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	232,690
2403.18253	Enhancing Metaphor Detection through Soft Labels and Target Word Prediction	Metaphors play a significant role in our everyday communication, yet detecting them presents a challenge. Traditional methods often struggle with improper application of language rules and a tendency to overlook data sparsity. To address these issues, we integrate knowledge distillation and prompt learning into metapho...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	441,845
2204.02399	Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification	The automatic early diagnosis of prodromal stages of Alzheimer's disease is of great relevance for patient treatment to improve quality of life. We address this problem as a multi-modal classification task. Multi-modal data provides richer and complementary information. However, existing techniques only consider either...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	289,931
2412.11375	Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP	Contrastive Language-Image Pretraining (CLIP) has been widely used in vision tasks. Notably, CLIP has demonstrated promising performance in few-shot learning (FSL). However, existing CLIP-based methods in training-free FSL (i.e., without the requirement of additional training) mainly learn different modalities independ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	517,370
2211.16564	Testing GLOM's ability to infer wholes from ambiguous parts	The GLOM architecture proposed by Hinton [2021] is a recurrent neural network for parsing an image into a hierarchy of wholes and parts. When a part is ambiguous, GLOM assumes that the ambiguity can be resolved by allowing the part to make multi-modal predictions for the pose and identity of the whole to which it belon...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	333,665
2409.14818	MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding	Recently, mobile AI agents based on VLMs have been gaining increasing attention. These works typically utilize VLM as a foundation, fine-tuning it with instruction-based mobile datasets. However, these VLMs are typically pre-trained on general-domain data, which often results in a lack of fundamental capabilities speci...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	490,642
2207.04232	Construction of MDS self-dual codes from generalized Reed-Solomon codes	MDS codes and self-dual codes are important families of classical codes in coding theory. It is of interest to investigate MDS self-dual codes. The existence of MDS self-dual codes over finite field $F_q$ is completely solved for $q$ is even. In this paper, for finite field with odd characteristic, we construct some ne...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	307,134
1702.08155	Multi-scale Image Fusion Between Pre-operative Clinical CT and X-ray Microtomography of Lung Pathology	Computational anatomy allows the quantitative analysis of organs in medical images. However, most analysis is constrained to the millimeter scale because of the limited resolution of clinical computed tomography (CT). X-ray microtomography ($\mu$CT) on the other hand allows imaging of ex-vivo tissues at a resolution of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	68,933
1207.4526	Iterative Design of L_p Digital Filters	The design of digital filters is a fundamental process in the context of digital signal processing. The purpose of this paper is to study the use of $\lp$ norms (for $2 < p < \infty$) as design criteria for digital filters, and to introduce a set of algorithms for the design of Finite (FIR) and Infinite (IIR) Impulse R...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,638
2112.07900	Environmental force sensing helps robots traverse cluttered large obstacles using physical interaction	Many applications require robots to move through complex 3-D terrain with large obstacles, such as self-driving, search and rescue, and extraterrestrial exploration. Although robots are already excellent at avoiding sparse obstacles, they still struggle in traversing cluttered large obstacles. To make progress, we need...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	271,632
2404.14997	Mining higher-order triadic interactions	Complex systems often present higher-order interactions which require us to go beyond their description in terms of pairwise networks. Triadic interactions are a fundamental type of higher-order interaction that occurs when one node regulates the interaction between two other nodes. Triadic interactions are a fundament...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	448,907
2411.00440	NAMR-RRT: Neural Adaptive Motion Planning for Mobile Robots in Dynamic Environments	Robots are increasingly deployed in dynamic and crowded environments, such as urban areas and shopping malls, where efficient and robust navigation is crucial. Traditional risk-based motion planning algorithms face challenges in such scenarios due to the lack of a well-defined search region, leading to inefficient expl...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	504,600
2307.02295	Meta-Learning Adversarial Bandit Algorithms	We study online meta-learning with bandit feedback, with the goal of improving performance across multiple tasks if they are similar according to some natural similarity measure. As the first to target the adversarial online-within-online partial-information setting, we design meta-algorithms that combine outer learner...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	377,651
0708.0361	Why the relational data model can be considered as a formal basis for group operations in object-oriented systems	Relational data model defines a specification of a type "relation". However, its simplicity does not mean that the system implementing this model must operate with structures having the same simplicity. We consider two principles allowing create a system which combines object-oriented paradigm (OOP) and relational data...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	519
2206.06929	Scaling ResNets in the Large-depth Regime	Deep ResNets are recognized for achieving state-of-the-art results in complex machine learning tasks. However, the remarkable performance of these architectures relies on a training procedure that needs to be carefully crafted to avoid vanishing or exploding gradients, particularly as the depth $L$ increases. No consen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	302,542
2408.08216	The Dawn of KAN in Image-to-Image (I2I) Translation: Integrating Kolmogorov-Arnold Networks with GANs for Unpaired I2I Translation	Image-to-Image translation in Generative Artificial Intelligence (Generative AI) has been a central focus of research, with applications spanning healthcare, remote sensing, physics, chemistry, photography, and more. Among the numerous methodologies, Generative Adversarial Networks (GANs) with contrastive learning have...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	480,906
2205.07299	Regulating Facial Processing Technologies: Tensions Between Legal and Technical Considerations in the Application of Illinois BIPA	Harms resulting from the development and deployment of facial processing technologies (FPT) have been met with increasing controversy. Several states and cities in the U.S. have banned the use of facial recognition by law enforcement and governments, but FPT are still being developed and used in a wide variety of conte...	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	296,549
2112.05504	BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering	Neural radiance fields (NeRF) has achieved outstanding performance in modeling 3D objects and controlled scenes, usually under a single scale. In this work, we focus on multi-scale cases where large changes in imagery are observed at drastically different scales. This scenario vastly exists in real-world 3D environment...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	270,864

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