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
1811.04860	Bio-YODIE: A Named Entity Linking System for Biomedical Text	Ever-expanding volumes of biomedical text require automated semantic annotation techniques to curate and put to best use. An established field of research seeks to link mentions in text to knowledge bases such as those included in the UMLS (Unified Medical Language System), in order to enable a more sophisticated under...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	113,191
2311.18071	Turn Down the Noise: Leveraging Diffusion Models for Test-time Adaptation via Pseudo-label Ensembling	The goal of test-time adaptation is to adapt a source-pretrained model to a continuously changing target domain without relying on any source data. Typically, this is either done by updating the parameters of the model (model adaptation) using inputs from the target domain or by modifying the inputs themselves (input a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	411,543
1812.07695	Index-based, High-dimensional, Cosine Threshold Querying with Optimality Guarantees	Given a database of vectors, a cosine threshold query returns all vectors in the database having cosine similarity to a query vector above a given threshold {\theta}. These queries arise naturally in many applications, such as document retrieval, image search, and mass spectrometry. The present paper considers the effi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	116,860
1506.03289	Characterizing the intrinsic correlations of scale-free networks	Very often, when studying topological or dynamical properties of random scale-free networks, it is tacitly assumed that degree-degree correlations are not present. However, simple constraints, such as the absence of multiple edges and self-loops, can give rise to intrinsic correlations in these structures. In the same ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	44,030
2312.04063	An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything	Foundation models are currently driving a paradigm shift in computer vision tasks for various fields including biology, astronomy, and robotics among others, leveraging user-generated prompts to enhance their performance. In the Laser Additive Manufacturing (LAM) domain, accurate image-based defect segmentation is impe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	413,534
1802.05591	Towards End-to-End Lane Detection: an Instance Segmentation Approach	Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lan...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	90,465
0810.2598	New avenue to the Parton Distribution Functions: Self-Organizing Maps	Neural network algorithms have been recently applied to construct Parton Distribution Function (PDF) parametrizations which provide an alternative to standard global fitting procedures. We propose a technique based on an interactive neural network algorithm using Self-Organizing Maps (SOMs). SOMs are a class of cluster...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	2,503
2408.12416	Unlearning Trojans in Large Language Models: A Comparison Between Natural Language and Source Code	This work investigates the application of Machine Unlearning (MU) for mitigating the impact of trojans embedded in conventional large language models of natural language (Text-LLMs) and large language models of code (Code-LLMs) We propose a novel unlearning approach, LYA, that leverages both gradient ascent and elastic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	482,723
2312.12641	Robust Point Matching with Distance Profiles	We show the outlier robustness and noise stability of practical matching procedures based on distance profiles. Although the idea of matching points based on invariants like distance profiles has a long history in the literature, there has been little understanding of the theoretical properties of such procedures, espe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	417,034
2005.01020	Investigating the Effects of Robot Engagement Communication on Learning from Demonstration	Robot Learning from Demonstration (RLfD) is a technique for robots to derive policies from instructors' examples. Although the reciprocal effects of student engagement on teacher behavior are widely recognized in the educational community, it is unclear whether the same phenomenon holds true for RLfD. To fill this gap,...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	175,471
1612.02605	Towards Information-Seeking Agents	We develop a general problem setting for training and testing the ability of agents to gather information efficiently. Specifically, we present a collection of tasks in which success requires searching through a partially-observed environment, for fragments of information which can be pieced together to accomplish vari...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	65,256
2209.10385	Long-Lived Accurate Keypoints in Event Streams	We present a novel end-to-end approach to keypoint detection and tracking in an event stream that provides better precision and much longer keypoint tracks than previous methods. This is made possible by two contributions working together. First, we propose a simple procedure to generate stable keypoint labels, which...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	318,845
2207.03546	BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus	BibleTTS is a large, high-quality, open speech dataset for ten languages spoken in Sub-Saharan Africa. The corpus contains up to 86 hours of aligned, studio quality 48kHz single speaker recordings per language, enabling the development of high-quality text-to-speech models. The ten languages represented are: Akuapem Tw...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	306,879
2307.11465	A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing Values	In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical domain, our primary objective is to develop an AI model capable of dynamically handling this missing...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	380,916
1906.06492	A formal approach for customization of schema.org based on SHACL	Schema.org is a widely adopted vocabulary for semantic annotation of content and data. However, its generic nature makes it complicated for data publishers to pick right types and properties for a specific domain and task. In this paper we propose a formal approach, a domain specification process that generates domain ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	135,315
2004.06632	A Survey on Activation Functions and their relation with Xavier and He Normal Initialization	In artificial neural network, the activation function and the weight initialization method play important roles in training and performance of a neural network. The question arises is what properties of a function are important/necessary for being a well-performing activation function. Also, the most widely used weight...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	172,560
2302.05073	Digital Twin-Aided Learning for Managing Reconfigurable Intelligent Surface-Assisted, Uplink, User-Centric Cell-Free Systems	This paper puts forth a new, reconfigurable intelligent surface (RIS)-assisted, uplink, user-centric cell-free (UCCF) system managed with the assistance of a digital twin (DT). Specifically, we propose a novel learning framework that maximizes the sum-rate by jointly optimizing the access point and user association (AU...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	344,920
1903.00984	Tight Robot Packing in the Real World: A Complete Manipulation Pipeline with Robust Primitives	Many order fulfillment applications in logistics, such as packing, involve picking objects from unstructured piles before tightly arranging them in bins or shipping containers. Desirable robotic solutions in this space need to be low-cost, robust, easily deployable and simple to control. The current work proposes a com...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	123,156
1508.07097	A Dynamical Model of Twitter Activity Profiles	The advent of the era of Big Data has allowed many researchers to dig into various socio-technical systems, including social media platforms. In particular, these systems have provided them with certain verifiable means to look into certain aspects of human behavior. In this work, we are specifically interested in the ...	true	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	46,378
2410.02258	Physics-Constrained Taylor Neural Networks for Learning and Control of Dynamical Systems	Data-driven approaches are increasingly popular for identifying dynamical systems due to improved accuracy and availability of sensor data. However, relying solely on data for identification does not guarantee that the identified systems will maintain their physical properties or that the predicted models will generali...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	494,196
2006.04340	The Strength of Nesterov's Extrapolation in the Individual Convergence of Nonsmooth Optimization	The extrapolation strategy raised by Nesterov, which can accelerate the convergence rate of gradient descent methods by orders of magnitude when dealing with smooth convex objective, has led to tremendous success in training machine learning tasks. In this article, the convergence of individual iterates of projected su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	180,656
2103.08133	$\omega-$nonblocking supervisory control of discrete-event systems with infinite behavior	In the supervisory control framework of discrete-event systems (DES) with infinite behavior initiated by Thistle and Wonham, a supervisor satisfying the minimal acceptable specification and the maximal legal specification is synthesized. However, this supervisor may incur livelocks as it cannot ensure that the infinite...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	224,806
1503.06900	New Classes of Partial Geometries and Their Associated LDPC Codes	The use of partial geometries to construct parity-check matrices for LDPC codes has resulted in the design of successful codes with a probability of error close to the Shannon capacity at bit error rates down to $10^{-15}$. Such considerations have motivated this further investigation. A new and simple construction of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	41,413
2012.06158	Reduced-Order Nonlinear Observers via Contraction Analysis and Convex Optimization	In this paper, we propose a new approach to design globally convergent reduced-order observers for nonlinear control systems via contraction analysis and convex optimization. Despite the fact that contraction is a concept naturally suitable for state estimation, the existing solutions are either local or relatively con...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	211,019
2410.03885	Collaborative Safety-Critical Formation Control with Obstacle Avoidance	This work explores a collaborative method for ensuring safety in multi-agent formation control problems. We formulate a control barrier function (CBF) based safety filter control law for a generic distributed formation controller and extend our previously developed collaborative safety framework to an obstacle avoidanc...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	495,024
1401.7474	The phenotypic expansion and its boundaries	The development of sport performances in the future is a subject of myth and disagreement among experts. As arguments favoring and opposing such methodology were discussed, other publications empirically showed that the past development of performances followed a non linear trend. Other works, while deeply exploring th...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	30,459
2110.07498	End-to-end Keyword Spotting using Xception-1d	The field of conversational agents is growing fast and there is an increasing need for algorithms that enhance natural interaction. In this work we show how we achieved state of the art results in the Keyword Spotting field by adapting and tweaking the Xception algorithm, which achieved outstanding results in several c...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	261,012
2310.08743	Development and Validation of a Deep Learning-Based Microsatellite Instability Predictor from Prostate Cancer Whole-Slide Images	Microsatellite instability-high (MSI-H) is a tumor agnostic biomarker for immune checkpoint inhibitor therapy. However, MSI status is not routinely tested in prostate cancer, in part due to low prevalence and assay cost. As such, prediction of MSI status from hematoxylin and eosin (H&E) stained whole-slide images (WSIs...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	399,506
1703.10926	EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning	The Android operating system has become the most popular operating system for smartphones and tablets leading to a rapid rise in malware. Sophisticated Android malware employ detection avoidance techniques in order to hide their malicious activities from analysis tools. These include a wide range of anti-emulator techn...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	70,991
2208.11574	Improving on the Markov-Switching Regression Model by the Use of an Adaptive Moving Average	Regime detection is vital for the effective operation of trading and investment strategies. However, the most popular means of doing this, the two-state Markov-switching regression model (MSR), is not an optimal solution, as two volatility states do not fully capture the complexity of the market. Past attempts to exten...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	314,479
2407.16073	KaPQA: Knowledge-Augmented Product Question-Answering	Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs). However, accurately assessing the performance of these applications remains a challenge, mainly due to the lack of suitable benchmarks that effectively simulate real-w...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	475,441
1408.0985	Speech earthquakes: scaling and universality in human voice	Speech is a distinctive complex feature of human capabilities. In order to understand the physics underlying speech production, in this work we empirically analyse the statistics of large human speech datasets ranging several languages. We first show that during speech the energy is unevenly released and power-law dist...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	35,125
2211.17204	Semisoft Task Clustering for Multi-Task Learning	Multi-task learning (MTL) aims to improve the performance of multiple related prediction tasks by leveraging useful information from them. Due to their flexibility and ability to reduce unknown coefficients substantially, the task-clustering-based MTL approaches have attracted considerable attention. Motivated by the i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	333,899
2012.02097	Recursive Tree Grammar Autoencoders	Machine learning on trees has been mostly focused on trees as input to algorithms. Much less research has investigated trees as output, which has many applications, such as molecule optimization for drug discovery, or hint generation for intelligent tutoring systems. In this work, we propose a novel autoencoder approac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	209,644
2003.12801	Probability error bounds for approximation of functions in reproducing kernel Hilbert spaces	We find probability error bounds for approximations of functions $f$ in a separable reproducing kernel Hilbert space $\mathcal{H}$ with reproducing kernel $K$ on a base space $X$, firstly in terms of finite linear combinations of functions of type $K_{x_i}$ and then in terms of the projection $\pi^n_x$ on $\mathrm{Span...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	170,017
1506.05790	Scalable Semi-Supervised Aggregation of Classifiers	We present and empirically evaluate an efficient algorithm that learns to aggregate the predictions of an ensemble of binary classifiers. The algorithm uses the structure of the ensemble predictions on unlabeled data to yield significant performance improvements. It does this without making assumptions on the structure...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	44,335
2001.00281	ZeroQ: A Novel Zero Shot Quantization Framework	Quantization is a promising approach for reducing the inference time and memory footprint of neural networks. However, most existing quantization methods require access to the original training dataset for retraining during quantization. This is often not possible for applications with sensitive or proprietary data, e....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	159,174
2401.12764	Fast Nonlinear Two-Time-Scale Stochastic Approximation: Achieving $O(1/k)$ Finite-Sample Complexity	This paper proposes to develop a new variant of the two-time-scale stochastic approximation to find the roots of two coupled nonlinear operators, assuming only noisy samples of these operators can be observed. Our key idea is to leverage the classic Ruppert-Polyak averaging technique to dynamically estimate the operato...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	423,492
2501.07331	Efficient Event-based Delay Learning in Spiking Neural Networks	Spiking Neural Networks (SNNs) are attracting increased attention as a more energy-efficient alternative to traditional Artificial Neural Networks. Spiking neurons are stateful and intrinsically recurrent, making them well-suited for spatio-temporal tasks. However, this intrinsic memory is limited by synaptic and membr...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	524,361
2411.06291	TinyML NLP Approach for Semantic Wireless Sentiment Classification	Natural Language Processing (NLP) operations, such as semantic sentiment analysis and text synthesis, may often impair users' privacy and demand significant on device computational resources. Centralized learning (CL) on the edge offers an alternative energy-efficient approach, yet requires the collection of raw inform...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	507,047
2312.02213	JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization	In this study, we introduce JarviX, a sophisticated data analytics framework. JarviX is designed to employ Large Language Models (LLMs) to facilitate an automated guide and execute high-precision data analyzes on tabular datasets. This framework emphasizes the significance of varying column types, capitalizing on state...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	412,766
1203.6750	Adaptive Gaussian Mixture Filter Based on Statistical Linearization	Gaussian mixtures are a common density representation in nonlinear, non-Gaussian Bayesian state estimation. Selecting an appropriate number of Gaussian components, however, is difficult as one has to trade of computational complexity against estimation accuracy. In this paper, an adaptive Gaussian mixture filter based ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	15,188
2410.04408	Anti-Malicious ISAC Using Proactive Monitoring	In this paper, we investigate proactive monitoring to mitigate malicious activities in integrated sensing and communication (ISAC) systems. Our focus is on a scenario where a cell-free massive multiple-input multiple-output (CF-mMIMO) architecture is exploited by malicious actors. Malicious actors use multiple access p...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	495,267
math/0103007	Source Coding, Large Deviations, and Approximate Pattern Matching	We present a development of parts of rate-distortion theory and pattern- matching algorithms for lossy data compression, centered around a lossy version of the Asymptotic Equipartition Property (AEP). This treatment closely parallels the corresponding development in lossless compression, a point of view that was advanc...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,603
1801.00218	Game-theoretic Network Centrality: A Review	Game-theoretic centrality is a flexible and sophisticated approach to identify the most important nodes in a network. It builds upon the methods from cooperative game theory and network theory. The key idea is to treat nodes as players in a cooperative game, where the value of each coalition is determined by certain gr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	87,527
1401.7739	Stability robustness of a feedback interconnection of systems with negative imaginary frequency response	A necessary and sufficient condition, expressed simply as the DC loop gain (ie the loop gain at zero frequency) being less than unity, is given in this paper to guarantee the internal stability of a feedback interconnection of Linear Time-Invariant (LTI) Multiple-Input Multiple-Output (MIMO) systems with negative imagi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	30,482
2412.10644	Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeB	Pervasive and high-accuracy positioning has become increasingly important as a fundamental enabler for intelligent connected devices in mobile networks. Nevertheless, current wireless networks heavily rely on pure model-driven techniques to achieve positioning functionality, often succumbing to performance deterioratio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	517,030
2412.00366	Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path Generation	Multi-robot motion planning for high degree-of-freedom manipulators in shared, constrained, and narrow spaces is a complex problem and essential for many scenarios such as construction, surgery, and more. Traditional coupled and decoupled methods either scale poorly or lack completeness, and hybrid methods that compose...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	512,619
2002.06288	Let Me At Least Learn What You Really Like: Dealing With Noisy Humans When Learning Preferences	Learning the preferences of a human improves the quality of the interaction with the human. The number of queries available to learn preferences maybe limited especially when interacting with a human, and so active learning is a must. One approach to active learning is to use uncertainty sampling to decide the informat...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	164,142
2004.12169	Learning to Update Natural Language Comments Based on Code Changes	We formulate the novel task of automatically updating an existing natural language comment based on changes in the body of code it accompanies. We propose an approach that learns to correlate changes across two distinct language representations, to generate a sequence of edits that are applied to the existing comment t...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	174,152
2007.07453	Graph-Based Social Relation Reasoning	Human beings are fundamentally sociable -- that we generally organize our social lives in terms of relations with other people. Understanding social relations from an image has great potential for intelligent systems such as social chatbots and personal assistants. In this paper, we propose a simpler, faster, and more ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	187,337
1703.05291	Deep Embedding Forest: Forest-based Serving with Deep Embedding Features	Deep Neural Networks (DNN) have demonstrated superior ability to extract high level embedding vectors from low level features. Despite the success, the serving time is still the bottleneck due to expensive run-time computation of multiple layers of dense matrices. GPGPU, FPGA, or ASIC-based serving systems require addi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	70,052
2205.08263	Contact-less Material Probing with Distributed Sensors: Joint Sensing and Communication Optimization	The utilization of RF signals to probe material properties of objects is of huge interest both in academia as well as industry. To this end, a setup is investigated, in which a transmitter equipped with a two-dimensional multi-antenna array dispatches a signal, which hits objects in the environment and the reflections ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	296,873
2303.10103	Image comparison and scaling via nonlinear elasticity	A nonlinear elasticity model for comparing images is formulated and analyzed, in which optimal transformations between images are sought as minimizers of an integral functional. The existence of minimizers in a suitable class of homeomorphisms between image domains is established under natural hypotheses. We investigat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	352,307
2402.09654	GPT-4's assessment of its performance in a USMLE-based case study	This study investigates GPT-4's assessment of its performance in healthcare applications. A simple prompting technique was used to prompt the LLM with questions taken from the United States Medical Licensing Examination (USMLE) questionnaire and it was tasked to evaluate its confidence score before posing the question ...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	429,615
1705.06510	Entropic selection of concepts unveils hidden topics in documents corpora	The organization and evolution of science has recently become itself an object of scientific quantitative investigation, thanks to the wealth of information that can be extracted from scientific documents, such as citations between papers and co-authorship between researchers. However, only few studies have focused on ...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	true	73,644
2110.14031	Surrogate Regret Bounds for Polyhedral Losses	Surrogate risk minimization is an ubiquitous paradigm in supervised machine learning, wherein a target problem is solved by minimizing a surrogate loss on a dataset. Surrogate regret bounds, also called excess risk bounds, are a common tool to prove generalization rates for surrogate risk minimization. While surrogate ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	263,385
2105.13017	Minimax Optimal Fixed-Budget Best Arm Identification in Linear Bandits	We study the problem of best arm identification in linear bandits in the fixed-budget setting. By leveraging properties of the G-optimal design and incorporating it into the arm allocation rule, we design a parameter-free algorithm, Optimal Design-based Linear Best Arm Identification (OD-LinBAI). We provide a theoretic...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	237,179
2012.05489	AI Driven Knowledge Extraction from Clinical Practice Guidelines: Turning Research into Practice	Background and Objectives: Clinical Practice Guidelines (CPGs) represent the foremost methodology for sharing state-of-the-art research findings in the healthcare domain with medical practitioners to limit practice variations, reduce clinical cost, improve the quality of care, and provide evidence based treatment. Howe...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	210,801
1812.06309	Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach	Thanks to their versatility, ease of deployment and high-performance, surrogate models have become staple tools in the arsenal of uncertainty quantification (UQ). From local interpolants to global spectral decompositions, surrogates are characterised by their ability to efficiently emulate complex computational models ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	116,586
2405.01205	Error-Driven Uncertainty Aware Training	Neural networks are often overconfident about their predictions, which undermines their reliability and trustworthiness. In this work, we present a novel technique, named Error-Driven Uncertainty Aware Training (EUAT), which aims to enhance the ability of neural classifiers to estimate their uncertainty correctly, name...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	451,273
1202.0876	A Coding Theoretic Approach for Evaluating Accumulate Distribution on Minimum Cut Capacity of Weighted Random Graphs	The multicast capacity of a directed network is closely related to the $s$-$t$ maximum flow, which is equal to the $s$-$t$ minimum cut capacity due to the max-flow min-cut theorem. If the topology of a network (or link capacities) is dynamically changing or have stochastic nature, it is not so trivial to predict statis...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	14,138
2002.06460	HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery	Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views....	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	164,193
2305.02782	A Momentum-Incorporated Non-Negative Latent Factorization of Tensors Model for Dynamic Network Representation	A large-scale dynamic network (LDN) is a source of data in many big data-related applications due to their large number of entities and large-scale dynamic interactions. They can be modeled as a high-dimensional incomplete (HDI) tensor that contains a wealth of knowledge about time patterns. A Latent factorization of t...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	362,172
1306.3682	Frequency Domain Design of Fractional Order PID Controller for AVR System Using Chaotic Multi-objective Optimization	A fractional order (FO) PID or FOPID controller is designed for an Automatic Voltage Regulator (AVR) system with the consideration of contradictory performance objectives. An improved evolutionary Non-dominated Sorting Genetic Algorithm (NSGA-II), augmented with a chaotic Henon map is used for the multi-objective optim...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	25,231
1702.07409	Founsure 1.0: An Erasure Code Library with Efficient Repair and Update Features	Founsure is an open-source software library that implements a multi-dimensional graph-based erasure coding entirely based on fast exclusive OR (XOR) logic. Its implementation utilizes compiler optimizations and multi-threading to generate the right assembly code for the given multi-core CPU architecture with vector pro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,777
2310.01297	Co-audit: tools to help humans double-check AI-generated content	Users are increasingly being warned to check AI-generated content for correctness. Still, as LLMs (and other generative models) generate more complex output, such as summaries, tables, or code, it becomes harder for the user to audit or evaluate the output for quality or correctness. Hence, we are seeing the emergence ...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	396,358
2205.15555	Graph-level Neural Networks: Current Progress and Future Directions	Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edges) are ubiquitous. Graph-level learning is a matter of studying a collection of graphs instead of a single graph. Traditional graph-level learning methods used to be the mainstream. However, with the increasing scale an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	299,775
2311.07075	GazeForensics: DeepFake Detection via Gaze-guided Spatial Inconsistency Learning	DeepFake detection is pivotal in personal privacy and public safety. With the iterative advancement of DeepFake techniques, high-quality forged videos and images are becoming increasingly deceptive. Prior research has seen numerous attempts by scholars to incorporate biometric features into the field of DeepFake detect...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	407,186
2004.04295	Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events	In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events within a span of text as input and they identify temporal relations (Before, After, Equal, Vague) between them. Given that a key challenge with...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	171,834
2203.13843	Quantifying Demonstration Quality for Robot Learning and Generalization	Learning from Demonstration (LfD) seeks to democratize robotics by enabling diverse end-users to teach robots to perform a task by providing demonstrations. However, most LfD techniques assume users provide optimal demonstrations. This is not always the case in real applications where users are likely to provide demons...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	287,772
2305.18921	Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles	Car-Following (CF), as a fundamental driving behaviour, has significant influences on the safety and efficiency of traffic flow. Investigating how human drivers react differently when following autonomous vs. human-driven vehicles (HV) is thus critical for mixed traffic flow. Research in this field can be expedited wit...	true	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	369,298
2312.15571	A Survey on Open-Set Image Recognition	Open-set image recognition (OSR) aims to both classify known-class samples and identify unknown-class samples in the testing set, which supports robust classifiers in many realistic applications, such as autonomous driving, medical diagnosis, security monitoring, etc. In recent years, open-set recognition methods have ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	418,051
2304.07022	Label Dependencies-aware Set Prediction Networks for Multi-label Text Classification	Multi-label text classification involves extracting all relevant labels from a sentence. Given the unordered nature of these labels, we propose approaching the problem as a set prediction task. To address the correlation between labels, we leverage Graph Convolutional Networks and construct an adjacency matrix based on...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	358,196
2202.12808	High-Dimensional Sparse Bayesian Learning without Covariance Matrices	Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem. However, the most popular inference algorithms for SBL become too expensive for high-dimensional settings, due to the need to store and compute a large covariance matrix. We introduce a new inference scheme that avoids explic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,367
2210.11680	Twin Contrastive Learning for Online Clustering	This paper proposes to perform online clustering by conducting twin contrastive learning (TCL) at the instance and cluster level. Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	325,406
1805.03482	Full 3D Reconstruction of Transparent Objects	Numerous techniques have been proposed for reconstructing 3D models for opaque objects in past decades. However, none of them can be directly applied to transparent objects. This paper presents a fully automatic approach for reconstructing complete 3D shapes of transparent objects. Through positioning an object on a tu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	97,059
1912.00998	A Multigrid Method for Efficiently Training Video Models	Training competitive deep video models is an order of magnitude slower than training their counterpart image models. Slow training causes long research cycles, which hinders progress in video understanding research. Following standard practice for training image models, video model training assumes a fixed mini-batch s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	155,955
2010.15423	Tilde at WMT 2020: News Task Systems	This paper describes Tilde's submission to the WMT2020 shared task on news translation for both directions of the English-Polish language pair in both the constrained and the unconstrained tracks. We follow our submissions from the previous years and build our baseline systems to be morphologically motivated sub-word u...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	203,766
1808.05475	Strong Coordination over Noisy Channels	We study the problem of strong coordination of the actions of two nodes $X$ and $Y$ that communicate over a discrete memoryless channel (DMC) such that the actions follow a prescribed joint probability distribution. We propose two novel random coding schemes and a polar coding scheme for this noisy strong coordination ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	105,355
1010.4603	Write Channel Model for Bit-Patterned Media Recording	We propose a new write channel model for bit-patterned media recording that reflects the data dependence of write synchronization errors. It is shown that this model accommodates both substitution-like errors and insertion-deletion errors whose statistics are determined by an underlying channel state process. We study ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,984
0712.2223	Entanglement-Assisted Quantum Convolutional Coding	We show how to protect a stream of quantum information from decoherence induced by a noisy quantum communication channel. We exploit preshared entanglement and a convolutional coding structure to develop a theory of entanglement-assisted quantum convolutional coding. Our construction produces a Calderbank-Shor-Steane (...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,027
2203.14126	Robust No-Regret Learning in Min-Max Stackelberg Games	The behavior of no-regret learning algorithms is well understood in two-player min-max (i.e, zero-sum) games. In this paper, we investigate the behavior of no-regret learning in min-max games with dependent strategy sets, where the strategy of the first player constrains the behavior of the second. Such games are best ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	287,888
2501.10499	Learning More With Less: Sample Efficient Dynamics Learning and Model-Based RL for Loco-Manipulation	Combining the agility of legged locomotion with the capabilities of manipulation, loco-manipulation platforms have the potential to perform complex tasks in real-world applications. To this end, state-of-the-art quadrupeds with attached manipulators, such as the Boston Dynamics Spot, have emerged to provide a capable a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	525,552
2203.06457	3D-GIF: 3D-Controllable Object Generation via Implicit Factorized Representations	While NeRF-based 3D-aware image generation methods enable viewpoint control, limitations still remain to be adopted to various 3D applications. Due to their view-dependent and light-entangled volume representation, the 3D geometry presents unrealistic quality and the color should be re-rendered for every desired viewpo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	285,122
1508.02556	Assessment of LTE Wireless Access for Monitoring of Energy Distribution in the Smart Grid	While LTE is becoming widely rolled out for human-type services, it is also a promising solution for cost-efficient connectivity of the smart grid monitoring equipment. This is a type of machine-to-machine (M2M) traffic that consists mainly of sporadic uplink transmissions. In such a setting, the amount of traffic that...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	45,916
0704.1818	Low-density graph codes that are optimal for source/channel coding and binning	We describe and analyze the joint source/channel coding properties of a class of sparse graphical codes based on compounding a low-density generator matrix (LDGM) code with a low-density parity check (LDPC) code. Our first pair of theorems establish that there exist codes from this ensemble, with all degrees remaining ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45
1611.01578	Neural Architecture Search with Reinforcement Learning	Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and trai...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	63,393
1711.03576	Performance of Source transmit Antenna selection for MIMO cooperative communication System Based DF protocol: Symbol Error Rate and Diversity order	In this work, we study the performance of a single relay Multiple Input Multiple Output (MIMO) cooperative communication system based on Decode and Forward (DF) relaying protocol for two strategies using transmit antenna selection at the source. The first strategy uses one antenna between the relay and the destination,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	84,237
2404.14678	3DBench: A Scalable 3D Benchmark and Instruction-Tuning Dataset	Evaluating the performance of Multi-modal Large Language Models (MLLMs), integrating both point cloud and language, presents significant challenges. The lack of a comprehensive assessment hampers determining whether these models truly represent advancements, thereby impeding further progress in the field. Current evalu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	448,767
2309.10149	Analysis of the Memorization and Generalization Capabilities of AI Agents: Are Continual Learners Robust?	In continual learning (CL), an AI agent (e.g., autonomous vehicles or robotics) learns from non-stationary data streams under dynamic environments. For the practical deployment of such applications, it is important to guarantee robustness to unseen environments while maintaining past experiences. In this paper, a novel...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	392,876
1307.2342	Model Selection with Low Complexity Priors	Regularization plays a pivotal role when facing the challenge of solving ill-posed inverse problems, where the number of observations is smaller than the ambient dimension of the object to be estimated. A line of recent work has studied regularization models with various types of low-dimensional structures. In such set...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,708
2210.11931	Deep Reinforcement Learning for Inverse Inorganic Materials Design	A major obstacle to the realization of novel inorganic materials with desirable properties is the inability to perform efficient optimization across both materials properties and synthesis of those materials. In this work, we propose a reinforcement learning (RL) approach to inverse inorganic materials design, which ca...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	325,510
2502.14397	PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data	We introduce PhotoDoodle, a novel image editing framework designed to facilitate photo doodling by enabling artists to overlay decorative elements onto photographs. Photo doodling is challenging because the inserted elements must appear seamlessly integrated with the background, requiring realistic blending, perspectiv...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	535,815
2205.07598	Cell-Free MmWave Massive MIMO Systems with Low-Capacity Fronthaul Links and Low-Resolution ADC/DACs	In this paper, we consider the uplink channel estimation phase and downlink data transmission phase of cell-free millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with low-capacity fronthaul links and low-resolution analog-to-digital converters/digital-to-analog converters (ADC/DACs). In ce...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	296,656
2411.02065	AM Flow: Adapters for Temporal Processing in Action Recognition	Deep learning models, in particular \textit{image} models, have recently gained generalisability and robustness. %are becoming more general and robust by the day. In this work, we propose to exploit such advances in the realm of \textit{video} classification. Video foundation models suffer from the requirement of exten...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	505,339
2206.10298	ViralBERT: A User Focused BERT-Based Approach to Virality Prediction	Recently, Twitter has become the social network of choice for sharing and spreading information to a multitude of users through posts called 'tweets'. Users can easily re-share these posts to other users through 'retweets', which allow information to cascade to many more users, increasing its outreach. Clearly, being a...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	303,862
1601.07124	LIA-RAG: a system based on graphs and divergence of probabilities applied to Speech-To-Text Summarization	This paper aims to introduces a new algorithm for automatic speech-to-text summarization based on statistical divergences of probabilities and graphs. The input is a text from speech conversations with noise, and the output a compact text summary. Our results, on the pilot task CCCS Multiling 2015 French corpus are ver...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	51,384
1610.04005	Stream Reasoning-Based Control of Caching Strategies in CCN Routers	Content-Centric Networking (CCN) research addresses the mismatch between the modern usage of the Internet and its outdated architecture. Importantly, CCN routers may locally cache frequently requested content in order to speed up delivery to end users. Thus, the issue of caching strategies arises, i.e., which content s...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	62,329
1703.02921	Transformation-Grounded Image Generation Network for Novel 3D View Synthesis	We present a transformation-grounded image generation network for novel 3D view synthesis from a single image. Instead of taking a 'blank slate' approach, we first explicitly infer the parts of the geometry visible both in the input and novel views and then re-cast the remaining synthesis problem as image completion. S...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	69,644

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