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
2201.08379	Learning Pixel Trajectories with Multiscale Contrastive Random Walks	A range of video modeling tasks, from optical flow to multiple object tracking, share the same fundamental challenge: establishing space-time correspondence. Yet, approaches that dominate each space differ. We take a step towards bridging this gap by extending the recent contrastive random walk formulation to much dens...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	276,321
1402.1792	Binary Excess Risk for Smooth Convex Surrogates	In statistical learning theory, convex surrogates of the 0-1 loss are highly preferred because of the computational and theoretical virtues that convexity brings in. This is of more importance if we consider smooth surrogates as witnessed by the fact that the smoothness is further beneficial both computationally- by at...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	30,711
1504.00215	Controlled Remote State Preparation via General Pure Three-Qubit State	The protocols for controlled remote state preparation of a single qubit and a general two-qubit state are presented in this paper. The general pure three-qubit states are chosen as shared quantum channel, which are not LOCC equivalent to the mostly used GHZ-state. It is the first time to introduce general pure three-qu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	41,684
2206.09257	Optimal Dynamic Regret in LQR Control	We consider the problem of nonstochastic control with a sequence of quadratic losses, i.e., LQR control. We provide an efficient online algorithm that achieves an optimal dynamic (policy) regret of $\tilde{O}(\text{max}\{n^{1/3} \mathcal{TV}(M_{1:n})^{2/3}, 1\})$, where $\mathcal{TV}(M_{1:n})$ is the total variation of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	303,491
2408.07395	Improving Global Parameter-sharing in Physically Heterogeneous Multi-agent Reinforcement Learning with Unified Action Space	In a multi-agent system (MAS), action semantics indicates the different influences of agents' actions toward other entities, and can be used to divide agents into groups in a physically heterogeneous MAS. Previous multi-agent reinforcement learning (MARL) algorithms apply global parameter-sharing across different types...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	480,566
2502.00902	Position: More Rigorous Software Engineering Would Improve Reproducibility in Machine Learning Research	Experimental verification and falsification of scholarly work are part of the scientific method's core. To improve the Machine Learning (ML)-communities' ability to verify results from prior work, we argue for more robust software engineering. We estimate the adoption of common engineering best practices by examining r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	529,601
1410.4013	A two-pass fuzzy-geno approach to pattern classification	The work presents an extension of the fuzzy approach to 2-D shape recognition [1] through refinement of initial or coarse classification decisions under a two pass approach. In this approach, an unknown pattern is classified by refining possible classification decisions obtained through coarse classification of the sam...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	36,763
2305.15244	Neural Lyapunov and Optimal Control	Despite impressive results, reinforcement learning (RL) suffers from slow convergence and requires a large variety of tuning strategies. In this paper, we investigate the ability of RL algorithms on simple continuous control tasks. We show that without reward and environment tuning, RL suffers from poor convergence. In...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	367,524
2211.01885	Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images	Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation is the process of algorithmically identifying tumors in brain MRI scans. While many approaches have been proposed in the literature for brain tumor segmentation, this paper proposes...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	328,398
2102.00424	An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games	Guessing games are a prototypical instance of the "learning by interacting" paradigm. This work investigates how well an artificial agent can benefit from playing guessing games when later asked to perform on novel NLP downstream tasks such as Visual Question Answering (VQA). We propose two ways to exploit playing gues...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	217,776
2408.15256	Improving Ontology Requirements Engineering with OntoChat and Participatory Prompting	Past ontology requirements engineering (ORE) has primarily relied on manual methods, such as interviews and collaborative forums, to gather user requirements from domain experts, especially in large projects. Current OntoChat offers a framework for ORE that utilises large language models (LLMs) to streamline the proces...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	483,866
1908.04346	SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis	Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks. However, most of existing methods merely consider the global contextual information and ignore the fine foreground structures, e.g., vessel, skeleton, which may conta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	141,448
1910.09630	Blind interactive learning of modulation schemes: Multi-agent cooperation without co-design	We examine the problem of learning to cooperate in the context of wireless communication. In our setting, two agents must learn modulation schemes that enable them to communicate across a power-constrained additive white Gaussian noise channel. We investigate whether learning is possible under different levels of infor...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	150,245
2202.04708	Active Learning Improves Performance on Symbolic RegressionTasks in StackGP	In this paper we introduce an active learning method for symbolic regression using StackGP. The approach begins with a small number of data points for StackGP to model. To improve the model the system incrementally adds a data point such that the new point maximizes prediction uncertainty as measured by the model ensem...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,634
2301.09919	Opportunities and Challenges in Neural Dialog Tutoring	Designing dialog tutors has been challenging as it involves modeling the diverse and complex pedagogical strategies employed by human tutors. Although there have been significant recent advances in neural conversational systems using large language models (LLMs) and growth in available dialog corpora, dialog tutoring h...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	341,648
1912.06825	Knowledge forest: a novel model to organize knowledge fragments	With the rapid growth of knowledge, it shows a steady trend of knowledge fragmentization. Knowledge fragmentization manifests as that the knowledge related to a specific topic in a course is scattered in isolated and autonomous knowledge sources. We term the knowledge of a facet in a specific topic as a knowledge fragm...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	157,438
1204.2731	How do Ontology Mappings Change in the Life Sciences?	Mappings between related ontologies are increasingly used to support data integration and analysis tasks. Changes in the ontologies also require the adaptation of ontology mappings. So far the evolution of ontology mappings has received little attention albeit ontologies change continuously especially in the life scien...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	15,438
2401.15752	Integrated Sensing and Communication in the Finite Blocklength Regime	A point-to-point integrated sensing and communication (ISAC) system is considered where a transmitter conveys a message to a receiver over a discrete memoryless channel (DMC) and simultaneously estimates the state of the channel through the backscattered signals of the emitted waveform. We derive achievability and co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	424,572
2401.15193	Overview of Sensing Attacks on Autonomous Vehicle Technologies and Impact on Traffic Flow	While perception systems in Connected and Autonomous Vehicles (CAVs), which encompass both communication technologies and advanced sensors, promise to significantly reduce human driving errors, they also expose CAVs to various cyberattacks. These include both communication and sensing attacks, which potentially jeopard...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	424,348
2403.19976	eTraM: Event-based Traffic Monitoring Dataset	Event cameras, with their high temporal and dynamic range and minimal memory usage, have found applications in various fields. However, their potential in static traffic monitoring remains largely unexplored. To facilitate this exploration, we present eTraM - a first-of-its-kind, fully event-based traffic monitoring da...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	442,563
1707.04596	DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging	Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit several downstream applications such as recommendation and search. In this work, we propose a novel yet simple approach called DocTag2Vec to ac...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	77,069
2210.03540	Multi-Agent Systems for Computational Economics and Finance	In this article we survey the main research topics of our group at the University of Essex. Our research interests lie at the intersection of theoretical computer science, artificial intelligence, and economic theory. In particular, we focus on the design and analysis of mechanisms for systems involving multiple strate...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	322,081
2405.17677	Understanding differences in applying DETR to natural and medical images	Transformer-based detectors have shown success in computer vision tasks with natural images. These models, exemplified by the Deformable DETR, are optimized through complex engineering strategies tailored to the typical characteristics of natural scenes. However, medical imaging data presents unique challenges such as ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	458,056
2405.17720	MindFormer: Semantic Alignment of Multi-Subject fMRI for Brain Decoding	Research efforts for visual decoding from fMRI signals have attracted considerable attention in research community. Still multi-subject fMRI decoding with one model has been considered intractable due to the drastic variations in fMRI signals between subjects and even within the same subject across different trials. To...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	458,079
1509.03203	Adaptive Convex Combination of APA and ZA-APA algorithms for Sparse System Identification	In general, one often encounters the systems that have sparse impulse response, with time varying system sparsity. Conventional adaptive filters which perform well for identification of non-sparse systems fail to exploit the system sparsity for improving the performance as the sparsity level increases. This paper prese...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	46,803
2209.10922	Learning to Write with Coherence From Negative Examples	Coherence is one of the critical factors that determine the quality of writing. We propose writing relevance (WR) training method for neural encoder-decoder natural language generation (NLG) models which improves coherence of the continuation by leveraging negative examples. WR loss regresses the vector representation ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	319,019
2201.02263	ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networks	State-of-the-art stereo matching networks trained only on synthetic data often fail to generalize to more challenging real data domains. In this paper, we attempt to unfold an important factor that hinders the networks from generalizing across domains: through the lens of shortcut learning. We demonstrate that the lear...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	274,487
2008.10498	Noise-induced degeneration in online learning	In order to elucidate the plateau phenomena caused by vanishing gradient, we herein analyse stability of stochastic gradient descent near degenerated subspaces in a multi-layer perceptron. In stochastic gradient descent for Fukumizu-Amari model, which is the minimal multi-layer perceptron showing non-trivial plateau ph...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	193,019
2502.10601	Data-driven Super-Resolution of Flood Inundation Maps using Synthetic Simulations	The frequency of extreme flood events is increasing throughout the world. Daily, high-resolution (30m) Flood Inundation Maps (FIM) observed from space play a key role in informing mitigation and preparedness efforts to counter these extreme events. However, the temporal frequency of publicly available high-resolution F...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	533,958
2409.14012	Test Time Learning for Time Series Forecasting	Time-series forecasting has seen significant advancements with the introduction of token prediction mechanisms such as multi-head attention. However, these methods often struggle to achieve the same performance as in language modeling, primarily due to the quadratic computational cost and the complexity of capturing lo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	490,283
1201.4908	Self-Organisation of Evolving Agent Populations in Digital Ecosystems	We investigate the self-organising behaviour of Digital Ecosystems, because a primary motivation for our research is to exploit the self-organising properties of biological ecosystems. We extended a definition for the complexity, grounded in the biological sciences, providing a measure of the information in an organism...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	13,937
2003.07162	Adversarial Multimodal Representation Learning for Click-Through Rate Prediction	For better user experience and business effectiveness, Click-Through Rate (CTR) prediction has been one of the most important tasks in E-commerce. Although extensive CTR prediction models have been proposed, learning good representation of items from multimodal features is still less investigated, considering an item i...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	168,347
2502.08884	ShapeLib: designing a library of procedural 3D shape abstractions with Large Language Models	Procedural representations are desirable, versatile, and popular shape encodings. Authoring them, either manually or using data-driven procedures, remains challenging, as a well-designed procedural representation should be compact, intuitive, and easy to manipulate. A long-standing problem in shape analysis studies how...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	533,205
1601.05880	A Beta-Beta Achievability Bound with Applications	A channel coding achievability bound expressed in terms of the ratio between two Neyman-Pearson $\beta$ functions is proposed. This bound is the dual of a converse bound established earlier by Polyanskiy and Verd\'{u} (2014). The new bound turns out to simplify considerably the analysis in situations where the channel ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,175
2105.09505	Pilot Assignment Schemes for Cell-Free Massive MIMO Systems	In this work, we propose three pilot assignment schemes to reduce the effect of pilot contamination in cell-free massive multiple-input-multiple-output (MIMO) systems. Our first algorithm, which is based on the idea of random sequential adsorption (RSA) process from the statistical physics literature, can be implemente...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	236,088
2009.03775	Accelerated Multi-Agent Optimization Method over Stochastic Networks	We propose a distributed method to solve a multi-agent optimization problem with strongly convex cost function and equality coupling constraints. The method is based on Nesterov's accelerated gradient approach and works over stochastically time-varying communication networks. We consider the standard assumptions of Nes...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	194,886
2005.11963	Non-Destructive Sample Generation From Conditional Belief Functions	This paper presents a new approach to generate samples from conditional belief functions for a restricted but non trivial subset of conditional belief functions. It assumes the factorization (decomposition) of a belief function along a bayesian network structure. It applies general conditional belief functions.	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	178,615
2105.09146	Physical Constraint Embedded Neural Networks for inference and noise regulation	Neural networks often require large amounts of data to generalize and can be ill-suited for modeling small and noisy experimental datasets. Standard network architectures trained on scarce and noisy data will return predictions that violate the underlying physics. In this paper, we present methods for embedding even--o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	235,987
1905.02636	A Complementary Learning Systems Approach to Temporal Difference Learning	Complementary Learning Systems (CLS) theory suggests that the brain uses a 'neocortical' and a 'hippocampal' learning system to achieve complex behavior. These two systems are complementary in that the 'neocortical' system relies on slow learning of distributed representations while the 'hippocampal' system relies on f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	130,014
2005.10550	Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification	The deployment of automated systems to diagnose diseases from medical images is challenged by the requirement to localise the diagnosed diseases to justify or explain the classification decision. This requirement is hard to fulfil because most of the training sets available to develop these systems only contain global ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	178,217
2310.19495	Deep Learning for Visual Navigation of Underwater Robots	This paper aims to briefly survey deep learning methods for visual navigation of underwater robotics. The scope of this paper includes the visual perception of underwater robotics with deep learning methods, the available visual underwater datasets, imitation learning, and reinforcement learning methods for navigation....	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	404,012
2103.01093	Quantifying Indirect Gender Discrimination on Collaborative Platforms	Digital collaborative platforms have become crucial venues of career advancement and individual success in many creative fields, from engineering to the arts. Indirect gender discrimination is a key component to gendered disadvantage on platforms. Such platforms carried the promise of opening avenues of advancement to ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	222,513
2401.08281	The Faiss library	Vector databases typically manage large collections of embedding vectors. Currently, AI applications are growing rapidly, and so is the number of embeddings that need to be stored and indexed. The Faiss library is dedicated to vector similarity search, a core functionality of vector databases. Faiss is a toolkit of ind...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	421,836
2501.01275	HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking	The evolution of Advanced Driver Assistance Systems (ADAS) has increased the need for robust and generalizable algorithms for multi-object tracking. Traditional statistical model-based tracking methods rely on predefined motion models and assumptions about system noise distributions. Although computationally efficient,...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	522,002
2111.08006	Disparities in Dermatology AI: Assessments Using Diverse Clinical Images	More than 3 billion people lack access to care for skin disease. AI diagnostic tools may aid in early skin cancer detection; however most models have not been assessed on images of diverse skin tones or uncommon diseases. To address this, we curated the Diverse Dermatology Images (DDI) dataset - the first publicly avai...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	266,542
2111.08772	Computer Vision for Supporting Image Search	Computer vision and multimedia information processing have made extreme progress within the last decade and many tasks can be done with a level of accuracy as if done by humans, or better. This is because we leverage the benefits of huge amounts of data available for training, we have enormous computer processing avail...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	266,803
2408.07731	Polarization dynamics: a study of individuals shifting between political communities on social media	Individuals engaging on social media often tend to establish online communities where interactions predominantly occur among like-minded peers. While considerable efforts have been devoted to studying and delineating these communities, there has been limited attention directed towards individuals who diverge from these...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	480,704
cs/0506093	On Maximum Contention-Free Interleavers and Permutation Polynomials over Integer Rings	An interleaver is a critical component for the channel coding performance of turbo codes. Algebraic constructions are of particular interest because they admit analytical designs and simple, practical hardware implementation. Contention-free interleavers have been recently shown to be suitable for parallel decoding of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	538,803
1907.00318	Multiple Landmark Detection using Multi-Agent Reinforcement Learning	The detection of anatomical landmarks is a vital step for medical image analysis and applications for diagnosis, interpretation and guidance. Manual annotation of landmarks is a tedious process that requires domain-specific expertise and introduces inter-observer variability. This paper proposes a new detection approac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	137,006
2310.06823	NECO: NEural Collapse Based Out-of-distribution detection	Detecting out-of-distribution (OOD) data is a critical challenge in machine learning due to model overconfidence, often without awareness of their epistemological limits. We hypothesize that ``neural collapse'', a phenomenon affecting in-distribution data for models trained beyond loss convergence, also influences OOD ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	398,723
2109.07045	Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Net	Accurate medical image segmentation is crucial for diagnosis and analysis. However, the models without calibrated uncertainty estimates might lead to errors in downstream analysis and exhibit low levels of robustness. Estimating the uncertainty in the measurement is vital to making definite, informed conclusions. Espec...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	255,360
2406.08113	Valeo4Cast: A Modular Approach to End-to-End Forecasting	Motion forecasting is crucial in autonomous driving systems to anticipate the future trajectories of surrounding agents such as pedestrians, vehicles, and traffic signals. In end-to-end forecasting, the model must jointly detect and track from sensor data (cameras or LiDARs) the past trajectories of the different eleme...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	463,359
2004.03728	Practical Data Poisoning Attack against Next-Item Recommendation	Online recommendation systems make use of a variety of information sources to provide users the items that users are potentially interested in. However, due to the openness of the online platform, recommendation systems are vulnerable to data poisoning attacks. Existing attack approaches are either based on simple heur...	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	171,663
2106.06158	PyGAD: An Intuitive Genetic Algorithm Python Library	This paper introduces PyGAD, an open-source easy-to-use Python library for building the genetic algorithm. PyGAD supports a wide range of parameters to give the user control over everything in its life cycle. This includes, but is not limited to, population, gene value range, gene data type, parent selection, crossover...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	240,385
1907.06570	Automated Playtesting of Matching Tile Games	Matching tile games are an extremely popular game genre. Arguably the most popular iteration, Match-3 games, are simple to understand puzzle games, making them great benchmarks for research. In this paper, we propose developing different procedural personas for Match-3 games in order to approximate different human play...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	138,654
2103.04503	End-to-End Human Object Interaction Detection with HOI Transformer	We propose HOI Transformer to tackle human object interaction (HOI) detection in an end-to-end manner. Current approaches either decouple HOI task into separated stages of object detection and interaction classification or introduce surrogate interaction problem. In contrast, our method, named HOI Transformer, streamli...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	223,656
2502.00507	A statistically consistent measure of Semantic Variability using Language Models	To address the issue of variability in the output generated by a language model, we present a measure of semantic variability that is statistically consistent under mild assumptions. This measure, denoted as semantic spectral entropy, is a easy to implement algorithm that requires just off the shelf language models. We...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	529,414
2002.10561	Learning the mapping $\mathbf{x}\mapsto \sum_{i=1}^d x_i^2$: the cost of finding the needle in a haystack	The task of using machine learning to approximate the mapping $\mathbf{x}\mapsto\sum_{i=1}^d x_i^2$ with $x_i\in[-1,1]$ seems to be a trivial one. Given the knowledge of the separable structure of the function, one can design a sparse network to represent the function very accurately, or even exactly. When such structu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	165,438
2403.00198	AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs	Pre-trained Large Language Models (LLMs) have significantly advanced natural language processing capabilities but are susceptible to biases present in their training data, leading to unfair outcomes in various applications. While numerous strategies have been proposed to mitigate bias, they often require extensive comp...	false	false	false	false	true	false	true	false	true	false	false	false	false	true	false	false	false	false	433,891
1802.00673	Representation Learning for Resource Usage Prediction	Creating a model of a computer system that can be used for tasks such as predicting future resource usage and detecting anomalies is a challenging problem. Most current systems rely on heuristics and overly simplistic assumptions about the workloads and system statistics. These heuristics are typically a one-size-fits-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	89,452
2308.00529	Variational Label-Correlation Enhancement for Congestion Prediction	The physical design process of large-scale designs is a time-consuming task, often requiring hours to days to complete, with routing being the most critical and complex step. As the the complexity of Integrated Circuits (ICs) increases, there is an increased demand for accurate routing quality prediction. Accurate cong...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	382,973
2004.00553	Coronavirus Covid-19 spreading in Italy: optimizing an epidemiological model with dynamic social distancing through Differential Evolution	The aim of this paper consists in the application of a recent epidemiological model, namely SEIR with Social Distancing (SEIR--SD), extended here through the definition of a social distancing function varying over time, to assess the situation related to the spreading of the coronavirus Covid--19 in Italy and in two of...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	170,673
2502.04552	Reinforcement Learning Based Prediction of PID Controller Gains for Quadrotor UAVs	A reinforcement learning (RL) based methodology is proposed and implemented for online fine-tuning of PID controller gains, thus, improving quadrotor effective and accurate trajectory tracking. The RL agent is first trained offline on a quadrotor PID attitude controller and then validated through simulations and experi...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	531,210
2412.03230	PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction	ASR correction methods have predominantly focused on general datasets and have not effectively utilized Pinyin information, unique to the Chinese language. In this study, we address this gap by proposing a Pinyin Enhanced Rephrasing Language Model (PERL), specifically designed for N-best correction scenarios. Additiona...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	513,875
2007.01980	Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design	Motivated by practical needs such as large-scale learning, we study the impact of adaptivity constraints to linear contextual bandits, a central problem in online active learning. We consider two popular limited adaptivity models in literature: batch learning and rare policy switches. We show that, when the context vec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	185,592
2502.14070	DiffExp: Efficient Exploration in Reward Fine-tuning for Text-to-Image Diffusion Models	Fine-tuning text-to-image diffusion models to maximize rewards has proven effective for enhancing model performance. However, reward fine-tuning methods often suffer from slow convergence due to online sample generation. Therefore, obtaining diverse samples with strong reward signals is crucial for improving sample eff...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	535,643
1401.6626	Completion Time Reduction in Instantly Decodable Network Coding Through Decoding Delay Control	For several years, the completion time and decoding delay problems in Instantly Decodable Network Coding (IDNC) were considered separately and were thought to completely act against each other. Recently, some works aimed to balance the effects of these two important IDNC metrics but none of them studied a further optim...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	30,382
1809.02598	Mobility-Aware Resource Allocation in VLC Networks Using T-Step Look-Ahead Policy	Visible light communication (VLC) uses huge license-free spectral bandwidth of visible light for high-speed wireless communication. Since each VLC access point covers a small area, handovers of mobile users are inevitable. In order to deal with these handovers, developing fast and effective resource allocation algorith...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	107,083
1312.4162	New Method for Localization and Human Being Detection using UWB Technology: Helpful Solution for Rescue Robots	Two challenges for rescue robots are to detect human beings and to have an accurate positioning system. In indoor positioning, GPS receivers cannot be used due to the reflections or attenuation caused by obstacles. To detect human beings, sensors such as thermal camera, ultrasonic and microphone can be embedded on the ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	29,105
2406.03827	Chaos with Keywords: Exposing Large Language Models Sycophantic Hallucination to Misleading Keywords and Evaluating Defense Strategies	This study explores the sycophantic tendencies of Large Language Models (LLMs), where these models tend to provide answers that match what users want to hear, even if they are not entirely correct. The motivation behind this exploration stems from the common behavior observed in individuals searching the internet for f...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	461,409
1910.00883	Exploiting BERT for End-to-End Aspect-based Sentiment Analysis	In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e.g. BERT, on the E2E-ABSA task. Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA. The experimental results show that even with a simple linear classification la...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	147,784
2001.08540	Stochastic Item Descent Method for Large Scale Equal Circle Packing Problem	Stochastic gradient descent (SGD) is a powerful method for large-scale optimization problems in the area of machine learning, especially for a finite-sum formulation with numerous variables. In recent years, mini-batch SGD gains great success and has become a standard technique for training deep neural networks fed wit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	161,313
2302.06949	Camera Calibration without Camera Access -- A Robust Validation Technique for Extended PnP Methods	A challenge in image based metrology and forensics is intrinsic camera calibration when the used camera is unavailable. The unavailability raises two questions. The first question is how to find the projection model that describes the camera, and the second is to detect incorrect models. In this work, we use off-the-sh...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	345,589
2112.00270	An Enhanced Decoding Algorithm for Coded Compressed Sensing with Applications to Unsourced Random Access	Unsourced random access (URA) has emerged as a pragmatic framework for next-generation distributed sensor networks. Within URA, concatenated coding structures are often employed to ensure that the central base station can accurately recover the set of sent codewords during a given transmission period. Many URA algorith...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	269,076
1711.02361	FADO: A Deterministic Detection/Learning Algorithm	This paper proposes and studies a detection technique for adversarial scenarios (dubbed deterministic detection). This technique provides an alternative detection methodology in case the usual stochastic methods are not applicable: this can be because the studied phenomenon does not follow a stochastic sampling scheme,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	84,052
1907.01159	Bundled Causal History Interaction	Complex system arises as a result of the nonlinear interactions between components. In particular, the evolutionary dynamics of a multivariate system encodes the ways in which different variables interact with each other individually or in groups. One fundamental question that remains unanswered is: how do two non-over...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	137,245
1801.07698	ArcFace: Additive Angular Margin Loss for Deep Face Recognition	Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first introduce an Additive Angular Margin Loss (ArcFace), which not only has a clear geometric interpretation but also significantly enhances the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	88,830
2502.13280	Value Gradient Sampler: Sampling as Sequential Decision Making	We propose the Value Gradient Sampler (VGS), a trainable sampler based on the interpretation of sampling as discrete-time sequential decision-making. VGS generates samples from a given unnormalized density (i.e., energy) by drifting and diffusing randomly initialized particles. In VGS, finding the optimal drift is equi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	535,294
2212.11192	Continual Learning Approaches for Anomaly Detection	Anomaly Detection is a relevant problem that arises in numerous real-world applications, especially when dealing with images. However, there has been little research for this task in the Continual Learning setting. In this work, we introduce a novel approach called SCALE (SCALing is Enough) to perform Compressed Replay...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	337,722
2209.12435	STD: Stable Triangle Descriptor for 3D place recognition	In this work, we present a novel global descriptor termed stable triangle descriptor (STD) for 3D place recognition. For a triangle, its shape is uniquely determined by the length of the sides or included angles. Moreover, the shape of triangles is completely invariant to rigid transformations. Based on this property, ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	319,536
2303.12421	Region-wise matching for image inpainting based on adaptive weighted low-rank decomposition	Digital image inpainting is an interpolation problem, inferring the content in the missing (unknown) region to agree with the known region data such that the interpolated result fulfills some prior knowledge. Low-rank and nonlocal self-similarity are two important priors for image inpainting. Based on the nonlocal self...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,262
2302.01203	Online Learning under Budget and ROI Constraints via Weak Adaptivity	We study online learning problems in which a decision maker has to make a sequence of costly decisions, with the goal of maximizing their expected reward while adhering to budget and return-on-investment (ROI) constraints. Existing primal-dual algorithms designed for constrained online learning problems under adversari...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	343,521
2210.13113	Interactive inference: a multi-agent model of cooperative joint actions	We advance a novel computational model of multi-agent, cooperative joint actions that is grounded in the cognitive framework of active inference. The model assumes that to solve a joint task, such as pressing together a red or blue button, two (or more) agents engage in a process of interactive inference. Each agent ma...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	true	false	false	false	326,052
2204.11135	AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs	We present the first whiteness test for graphs, i.e., a whiteness test for multivariate time series associated with the nodes of a dynamic graph. The statistical test aims at finding serial dependencies among close-in-time observations, as well as spatial dependencies among neighboring observations given the underlying...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	293,042
1707.07591	Transition-Based Generation from Abstract Meaning Representations	This work addresses the task of generating English sentences from Abstract Meaning Representation (AMR) graphs. To cope with this task, we transform each input AMR graph into a structure similar to a dependency tree and annotate it with syntactic information by applying various predefined actions to it. Subsequently, a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	77,658
1706.06122	VAIN: Attentional Multi-agent Predictive Modeling	Multi-agent predictive modeling is an essential step for understanding physical, social and team-play systems. Recently, Interaction Networks (INs) were proposed for the task of modeling multi-agent physical systems, INs scale with the number of interactions in the system (typically quadratic or higher order in the num...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	75,623
2210.03044	Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?	Modern deep learning involves training costly, highly overparameterized networks, thus motivating the search for sparser networks that can still be trained to the same accuracy as the full network (i.e. matching). Iterative magnitude pruning (IMP) is a state of the art algorithm that can find such highly sparse matchin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	321,874
2405.18795	Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost	In this paper, we consider model-free federated reinforcement learning for tabular episodic Markov decision processes. Under the coordination of a central server, multiple agents collaboratively explore the environment and learn an optimal policy without sharing their raw data. Despite recent advances in federated Q-le...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	458,601
2409.00924	MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM	The Medical Segment Anything Model (MedSAM) has shown remarkable performance in medical image segmentation, drawing significant attention in the field. However, its sensitivity to varying prompt types and locations poses challenges. This paper addresses these challenges by focusing on the development of reliable prompt...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	485,136
1810.04456	Invariance Analysis of Saliency Models versus Human Gaze During Scene Free Viewing	Most of current studies on human gaze and saliency modeling have used high-quality stimuli. In real world, however, captured images undergo various types of distortions during the whole acquisition, transmission, and displaying chain. Some distortion types include motion blur, lighting variations and rotation. Despite ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	110,047
1410.1120	Security Formalizations and Their Relationships for Encryption and Key Agreement in Information-Theoretic Cryptography	This paper revisits formalizations of information-theoretic security for symmetric-key encryption and key agreement protocols which are very fundamental primitives in cryptography. In general, we can formalize information-theoretic security in various ways: some of them can be formalized as stand-alone security by exte...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	36,530
2412.07812	Multi-Response Preference Optimization with Augmented Ranking Dataset	Recent advancements in Large Language Models (LLMs) have been remarkable, with new models consistently surpassing their predecessors. These advancements are underpinned by extensive research on various training mechanisms. Among these, Preference Optimization has played a significant role in improving the performance o...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	515,822
1508.07468	Image Annotation Incorporating Low-Rankness, Tag and Visual Correlation and Inhomogeneous Errors	Tag-based image retrieval (TBIR) has drawn much attention in recent years due to the explosive amount of digital images and crowdsourcing tags. However, TBIR is still suffering from the incomplete and inaccurate tags provided by users, posing a great challenge for tag-based image management applications. In this work, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	46,408
2408.15497	On the Existence of Linear Observed Systems on Manifolds with Connection	Linear observed systems on manifolds are a special class of nonlinear systems whose state spaces are smooth manifolds but possess properties similar to linear systems. Such properties can be characterized by preintegration and exact linearization with Jacobians independent of the linearization point. Non-biased IMU dyn...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	483,956
2305.18706	HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality Decoder	Decoders play significant roles in recovering scene depths. However, the decoders used in previous works ignore the propagation of multilevel lossless fine-grained information, cannot adaptively capture local and global information in parallel, and cannot perform sufficient global statistical analyses on the final outp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	369,199
1805.07869	Learning Device Models with Recurrent Neural Networks	Recurrent neural networks (RNNs) are powerful constructs capable of modeling complex systems, up to and including Turing Machines. However, learning such complex models from finite training sets can be difficult. In this paper we empirically show that RNNs can learn models of computer peripheral devices through input a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	97,969
2110.04984	Advances in Multi-turn Dialogue Comprehension: A Survey	Training machines to understand natural language and interact with humans is an elusive and essential task of artificial intelligence. A diversity of dialogue systems has been designed with the rapid development of deep learning techniques, especially the recent pre-trained language models (PrLMs). Among these studies,...	true	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	260,117
2410.05102	SparsePO: Controlling Preference Alignment of LLMs via Sparse Token Masks	Preference Optimization (PO) has proven an effective step for aligning language models to human-desired behaviors. Current variants, following the offline Direct Preference Optimization objective, have focused on a strict setting where all tokens are contributing signals of KL divergence and rewards to the loss functio...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	495,571
2403.18178	Online Embedding Multi-Scale CLIP Features into 3D Maps	This study introduces a novel approach to online embedding of multi-scale CLIP (Contrastive Language-Image Pre-Training) features into 3D maps. By harnessing CLIP, this methodology surpasses the constraints of conventional vocabulary-limited methods and enables the incorporation of semantic information into the resulta...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	441,801
2101.03024	LiteMuL: A Lightweight On-Device Sequence Tagger using Multi-task Learning	Named entity detection and Parts-of-speech tagging are the key tasks for many NLP applications. Although the current state of the art methods achieved near perfection for long, formal, structured text there are hindrances in deploying these models on memory-constrained devices such as mobile phones. Furthermore, the pe...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	214,798

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