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
2410.15360	Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing	Volumetric medical image segmentation is a fundamental problem in medical image analysis where the objective is to accurately classify a given 3D volumetric medical image with voxel-level precision. In this work, we propose a novel hierarchical encoder-decoder-based framework that strives to explicitly capture the loca...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	500,499
1902.06370	Evolutionary Multitasking for Semantic Web Service Composition	Web services are basic functions of a software system to support the concept of service-oriented architecture. They are often composed together to provide added values, known as web service composition. Researchers often employ Evolutionary Computation techniques to efficiently construct composite services with near-op...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	121,751
2403.00826	LLMGuard: Guarding Against Unsafe LLM Behavior	Although the rise of Large Language Models (LLMs) in enterprise settings brings new opportunities and capabilities, it also brings challenges, such as the risk of generating inappropriate, biased, or misleading content that violates regulations and can have legal concerns. To alleviate this, we present "LLMGuard", a to...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	434,136
1904.11737	Using Sub-Optimal Plan Detection to Identify Commitment Abandonment in Discrete Environments	Assessing whether an agent has abandoned a goal or is actively pursuing it is important when multiple agents are trying to achieve joint goals, or when agents commit to achieving goals for each other. Making such a determination for a single goal by observing only plan traces is not trivial as agents often deviate from...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	128,934
2412.02292	Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering	Recently, deep matrix factorization has been established as a powerful model for unsupervised tasks, achieving promising results, especially for multi-view clustering. However, existing methods often lack effective feature selection mechanisms and rely on empirical hyperparameter selection. To address these issues, we ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	513,481
2107.07502	MultiBench: Multiscale Benchmarks for Multimodal Representation Learning	Learning multimodal representations involves integrating information from multiple heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world applications in multimedia, affective computing, robotics, finance, human-computer interaction, and healthcare. Unfortunately, multimodal resear...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	true	246,447
2209.09630	Detection of Malicious Websites Using Machine Learning Techniques	In detecting malicious websites, a common approach is the use of blacklists which are not exhaustive in themselves and are unable to generalize to new malicious sites. Detecting newly encountered malicious websites automatically will help reduce the vulnerability to this form of attack. In this study, we explored the u...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	318,580
1207.3107	Expectation-Maximization Gaussian-Mixture Approximate Message Passing	When recovering a sparse signal from noisy compressive linear measurements, the distribution of the signal's non-zero coefficients can have a profound effect on recovery mean-squared error (MSE). If this distribution was apriori known, then one could use computationally efficient approximate message passing (AMP) techn...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,442
1908.06087	3D Rigid Motion Segmentation with Mixed and Unknown Number of Models	Many real-world video sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography model for motion segmentation on video sequences would lead to difficulty. Even when we are confronted with a general scene-motion, the fun...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	141,908
2306.11252	HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation	We introduce HK-LegiCoST, a new three-way parallel corpus of Cantonese-English translations, containing 600+ hours of Cantonese audio, its standard traditional Chinese transcript, and English translation, segmented and aligned at the sentence level. We describe the notable challenges in corpus preparation: segmentation...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	374,526
2106.12027	ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences	Atomic clauses are fundamental text units for understanding complex sentences. Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, and question answering. Previous work mainly relies on rule-based methods ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	242,588
1912.10858	"The Squawk Bot": Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering	Multimodal analysis that uses numerical time series and textual corpora as input data sources is becoming a promising approach, especially in the financial industry. However, the main focus of such analysis has been on achieving high prediction accuracy while little effort has been spent on the important task of unders...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	158,438
2305.19470	Label Embedding via Low-Coherence Matrices	Label embedding is a framework for multiclass classification problems where each label is represented by a distinct vector of some fixed dimension, and training involves matching model output to the vector representing the correct label. While label embedding has been successfully applied in extreme classification and ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	369,538
2307.16862	Modulation-Enhanced Excitation for Continuous-Time Reinforcement Learning via Symmetric Kronecker Products	This work introduces new results in continuous-time reinforcement learning (CT-RL) control of affine nonlinear systems to address a major algorithmic challenge due to a lack of persistence of excitation (PE). This PE design limitation has previously stifled CT-RL numerical performance and prevented these algorithms fro...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	382,762
2212.00334	Parametric Information Maximization for Generalized Category Discovery	We introduce a Parametric Information Maximization (PIM) model for the Generalized Category Discovery (GCD) problem. Specifically, we propose a bi-level optimization formulation, which explores a parameterized family of objective functions, each evaluating a weighted mutual information between the features and the late...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	334,024
2402.03625	Convex Relaxations of ReLU Neural Networks Approximate Global Optima in Polynomial Time	In this paper, we study the optimality gap between two-layer ReLU networks regularized with weight decay and their convex relaxations. We show that when the training data is random, the relative optimality gap between the original problem and its relaxation can be bounded by a factor of O(log n^0.5), where n is the num...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	427,100
2412.01784	Noise Injection Reveals Hidden Capabilities of Sandbagging Language Models	Capability evaluations play a critical role in ensuring the safe deployment of frontier AI systems, but this role may be undermined by intentional underperformance or ``sandbagging.'' We present a novel model-agnostic method for detecting sandbagging behavior using noise injection. Our approach is founded on the observ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	513,259
2207.10710	Interpretable Boosted Decision Tree Analysis for the Majorana Demonstrator	The Majorana Demonstrator is a leading experiment searching for neutrinoless double-beta decay with high purity germanium detectors (HPGe). Machine learning provides a new way to maximize the amount of information provided by these detectors, but the data-driven nature makes it less interpretable compared to traditiona...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	309,351
2208.05757	A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception	In recent years there has been substantial growth in the capabilities of systems designed to generate text that mimics the fluency and coherence of human language. From this, there has been considerable research aimed at examining the potential uses of these natural language generators (NLG) towards a wide number of ta...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	312,496
1010.2741	MIMO Interference Alignment Over Correlated Channels with Imperfect CSI	Interference alignment (IA), given uncorrelated channel components and perfect channel state information, obtains the maximum degrees of freedom in an interference channel. Little is known, however, about how the sum rate of IA behaves at finite transmit power, with imperfect channel state information, or antenna corre...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,898
1703.07710	Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning	Statistical performance bounds for reinforcement learning (RL) algorithms can be critical for high-stakes applications like healthcare. This paper introduces a new framework for theoretically measuring the performance of such algorithms called Uniform-PAC, which is a strengthening of the classical Probably Approximatel...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	70,442
2501.18617	DarkMind: Latent Chain-of-Thought Backdoor in Customized LLMs	With the growing demand for personalized AI solutions, customized LLMs have become a preferred choice for businesses and individuals, driving the deployment of millions of AI agents across various platforms, e.g., GPT Store hosts over 3 million customized GPTs. Their popularity is partly driven by advanced reasoning ca...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	528,776
2304.12776	State Spaces Aren't Enough: Machine Translation Needs Attention	Structured State Spaces for Sequences (S4) is a recently proposed sequence model with successful applications in various tasks, e.g. vision, language modeling, and audio. Thanks to its mathematical formulation, it compresses its input to a single hidden state, and is able to capture long range dependencies while avoidi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	360,346
2409.04576	ActionFlow: Equivariant, Accurate, and Efficient Policies with Spatially Symmetric Flow Matching	Spatial understanding is a critical aspect of most robotic tasks, particularly when generalization is important. Despite the impressive results of deep generative models in complex manipulation tasks, the absence of a representation that encodes intricate spatial relationships between observations and actions often lim...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	486,431
2206.02795	Forecasting COVID- 19 cases using Statistical Models and Ontology-based Semantic Modelling: A real time data analytics approach	SARS-COV-19 is the most prominent issue which many countries face today. The frequent changes in infections, recovered and deaths represents the dynamic nature of this pandemic. It is very crucial to predict the spreading rate of this virus for accurate decision making against fighting with the situation of getting inf...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,038
1907.10072	Searching the Landscape of Flux Vacua with Genetic Algorithms	In this paper, we employ genetic algorithms to explore the landscape of type IIB flux vacua. We show that genetic algorithms can efficiently scan the landscape for viable solutions satisfying various criteria. More specifically, we consider a symmetric $T^{6}$ as well as the conifold region of a Calabi-Yau hypersurface...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	139,531
1907.12674	One-to-X analogical reasoning on word embeddings: a case for diachronic armed conflict prediction from news texts	We extend the well-known word analogy task to a one-to-X formulation, including one-to-none cases, when no correct answer exists. The task is cast as a relation discovery problem and applied to historical armed conflicts datasets, attempting to predict new relations of type `location:armed-group' based on data about pa...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	140,170
2207.06644	Source-Free Domain Adaptation for Real-world Image Dehazing	Deep learning-based source dehazing methods trained on synthetic datasets have achieved remarkable performance but suffer from dramatic performance degradation on real hazy images due to domain shift. Although certain Domain Adaptation (DA) dehazing methods have been presented, they inevitably require access to the sou...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	307,941
2001.08903	Runtime Performances of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem	Randomized search heuristics such as evolutionary algorithms are frequently applied to dynamic combinatorial optimization problems. Within this paper, we present a dynamic model of the classic Weighted Vertex Cover problem and analyze the runtime performances of the well-studied algorithms Randomized Local Search and (...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	161,424
0709.3586	Une adaptation des cartes auto-organisatrices pour des donn\'ees d\'ecrites par un tableau de dissimilarit\'es	Many data analysis methods cannot be applied to data that are not represented by a fixed number of real values, whereas most of real world observations are not readily available in such a format. Vector based data analysis methods have therefore to be adapted in order to be used with non standard complex data. A flexib...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	678
2402.09865	Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking	Traditional tracking-by-detection systems typically employ Kalman filters (KF) for state estimation. However, the KF requires domain-specific design choices and it is ill-suited to handling non-linear motion patterns. To address these limitations, we propose two innovative data-driven filtering methods. Our first metho...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	429,710
2403.15170	Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorders	Self-supervised learning (SSL) has been investigated to generate task-agnostic representations across various domains. However, such investigation has not been conducted for detecting multiple mental disorders. The rationale behind the existence of a task-agnostic representation lies in the overlapping symptoms among m...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	440,429
2305.10662	Private Gradient Estimation is Useful for Generative Modeling	While generative models have proved successful in many domains, they may pose a privacy leakage risk in practical deployment. To address this issue, differentially private generative model learning has emerged as a solution to train private generative models for different downstream tasks. However, existing private gen...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	365,168
1905.03907	Building 3D Object Models during Manipulation by Reconstruction-Aware Trajectory Optimization	Object shape provides important information for robotic manipulation; for instance, selecting an effective grasp depends on both the global and local shape of the object of interest, while reaching into clutter requires accurate surface geometry to avoid unintended contact with the environment. Model-based 3D object ma...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	130,320
2411.17762	MUSE-VL: Modeling Unified VLM through Semantic Discrete Encoding	We introduce MUSE-VL, a Unified Vision-Language Model through Semantic discrete Encoding for multimodal understanding and generation. Recently, the research community has begun exploring unified models for visual generation and understanding. However, existing vision tokenizers (e.g., VQGAN) only consider low-level inf...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	511,587
2403.11187	Task-Based Quantizer Design for Sensing With Random Signals	In integrated sensing and communication (ISAC) systems, random signaling is used to convey useful information as well as sense the environment. Such randomness poses challenges in various components in sensing signal processing. In this paper, we investigate quantizer design for sensing in ISAC systems. Unlike quantize...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	438,580
1908.10324	On the Minimax Optimality of Estimating the Wasserstein Metric	We study the minimax optimal rate for estimating the Wasserstein-$1$ metric between two unknown probability measures based on $n$ i.i.d. empirical samples from them. We show that estimating the Wasserstein metric itself between probability measures, is not significantly easier than estimating the probability measures u...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	143,085
2402.14642	Distributed Radiance Fields for Edge Video Compression and Metaverse Integration in Autonomous Driving	The metaverse is a virtual space that combines physical and digital elements, creating immersive and connected digital worlds. For autonomous mobility, it enables new possibilities with edge computing and digital twins (DTs) that offer virtual prototyping, prediction, and more. DTs can be created with 3D scene reconstr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	431,770
1606.08328	Maps of sparse Markov chains efficiently reveal community structure in network flows with memory	To better understand the flows of ideas or information through social and biological systems, researchers develop maps that reveal important patterns in network flows. In practice, network flow models have implied memoryless first-order Markov chains, but recently researchers have introduced higher-order Markov chain m...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	57,852
2403.20135	Parallel performance of shared memory parallel spectral deferred corrections	We investigate parallel performance of parallel spectral deferred corrections, a numerical approach that provides small-scale parallelism for the numerical solution of initial value problems. The scheme is applied to the shallow water equation and uses an IMEX splitting that integrates fast modes implicitly and slow mo...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	442,626
2010.06973	Neural Databases	In recent years, neural networks have shown impressive performance gains on long-standing AI problems, and in particular, answering queries from natural language text. These advances raise the question of whether they can be extended to a point where we can relax the fundamental assumption of database management, namel...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	200,660
2410.00179	Evaluating the fairness of task-adaptive pretraining on unlabeled test data before few-shot text classification	Few-shot learning benchmarks are critical for evaluating modern NLP techniques. It is possible, however, that benchmarks favor methods which easily make use of unlabeled text, because researchers can use unlabeled text from the test set to pretrain their models. Given the dearth of research on this potential problem, w...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	493,259
2102.10527	Delayed Rewards Calibration via Reward Empirical Sufficiency	Appropriate credit assignment for delay rewards is a fundamental challenge for reinforcement learning. To tackle this problem, we introduce a delay reward calibration paradigm inspired from a classification perspective. We hypothesize that well-represented state vectors share similarities with each other since they con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	221,125
1210.0794	A Semantic Approach for Automatic Structuring and Analysis of Software Process Patterns	The main contribution of this paper, is to propose a novel semantic approach based on a Natural Language Processing technique in order to ensure a semantic unification of unstructured process patterns which are expressed not only in different formats but also, in different forms. This approach is implemented using the ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	18,899
1705.00748	Robust, Informative Human-in-the-Loop Predictions via Empirical Reachable Sets	In order to develop provably safe human-in-the-loop systems, accurate and precise models of human behavior must be developed. In the case of intelligent vehicles, one can imagine the need for predicting driver behavior to develop minimally invasive active safety systems or to safely interact with other vehicles on the ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	72,740
2411.09623	Vision-based Manipulation of Transparent Plastic Bags in Industrial Setups	This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, aligning with the Industry 4.0 paradigm. Industry 4.0, driven by data, connectivity, analytics, and robotics, promises enhanced accessibility and sustainability through...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	508,311
2306.15832	Easing Color Shifts in Score-Based Diffusion Models	Generated images of score-based models can suffer from errors in their spatial means, an effect, referred to as a color shift, which grows for larger images. This paper investigates a previously-introduced approach to mitigate color shifts in score-based diffusion models. We quantify the performance of a nonlinear bypa...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	376,169
2002.01547	Accelerating Psychometric Screening Tests With Bayesian Active Differential Selection	Classical methods for psychometric function estimation either require excessive measurements or produce only a low-resolution approximation of the target psychometric function. In this paper, we propose a novel solution for rapid screening for a change in the psychometric function estimation of a given patient. We use ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	162,674
2212.00532	EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks	Background and Purpose: Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. Timely detection of cancer in its early stages is essential for treating the disease. Currently, there is a lack of datasets for histopathological image segm...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	334,091
2003.01452	Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns	Pay-per-click advertising includes various formats (\emph{e.g.}, search, contextual, social) with a total investment of more than 200 billion USD per year worldwide. An advertiser is given a daily budget to allocate over several, even thousands, campaigns, mainly distinguishing for the ad, target, or channel. Furthermo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	166,656
1602.08741	Gibberish Semantics: How Good is Russian Twitter in Word Semantic Similarity Task?	The most studied and most successful language models were developed and evaluated mainly for English and other close European languages, such as French, German, etc. It is important to study applicability of these models to other languages. The use of vector space models for Russian was recently studied for multiple co...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	52,684
2412.03993	LaserGuider: A Laser Based Physical Backdoor Attack against Deep Neural Networks	Backdoor attacks embed hidden associations between triggers and targets in deep neural networks (DNNs), causing them to predict the target when a trigger is present while maintaining normal behavior otherwise. Physical backdoor attacks, which use physical objects as triggers, are feasible but lack remote control, tempo...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	514,210
1403.1618	Design a Persian Automated Plagiarism Detector (AMZPPD)	Currently there are lots of plagiarism detection approaches. But few of them implemented and adapted for Persian languages. In this paper, our work on designing and implementation of a plagiarism detection system based on pre-processing and NLP technics will be described. And the results of testing on a corpus will be ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	31,411
2206.08570	Event-triggered Design for Optimal Output Consensus of High-order Multi-agent Systems	This paper studies the optimal output consensus problem for a group of heterogeneous linear multi-agent systems. Different from existing results, we aim at effective controllers for these high-order agents under both event-triggered control and event-triggered communication settings. We conduct an embedded design for t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	303,204
2209.01347	Explanation Guided Contrastive Learning for Sequential Recommendation	Recently, contrastive learning has been applied to the sequential recommendation task to address data sparsity caused by users with few item interactions and items with few user adoptions. Nevertheless, the existing contrastive learning-based methods fail to ensure that the positive (or negative) sequence obtained by s...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	315,853
2502.02310	Gaussian processes for dynamics learning in model predictive control	Due to its state-of-the-art estimation performance complemented by rigorous and non-conservative uncertainty bounds, Gaussian process regression is a popular tool for enhancing dynamical system models and coping with their inaccuracies. This has enabled a plethora of successful implementations of Gaussian process-based...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	530,258
1912.05812	An Integral Representation of the Logarithmic Function with Applications in Information Theory	We explore a well-known integral representation of the logarithmic function, and demonstrate its usefulness in obtaining compact, easily-computable exact formulas for quantities that involve expectations and higher moments of the logarithm of a positive random variable (or the logarithm of a sum of positive random vari...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	157,197
2006.13318	A Note on Over-Smoothing for Graph Neural Networks	Graph Neural Networks (GNNs) have achieved a lot of success on graph-structured data. However, it is observed that the performance of graph neural networks does not improve as the number of layers increases. This effect, known as over-smoothing, has been analyzed mostly in linear cases. In this paper, we build upon pre...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,863
2304.09412	hDesigner: Real-Time Haptic Feedback Pattern Designer	Haptic sensing can provide a new dimension to enhance people's musical and cinematic experiences. However, designing a haptic pattern is neither intuitive nor trivial. Imagined haptic patterns tend to be different from experienced ones. As a result, researchers use simple step-curve patterns to create haptic stimuli. T...	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	359,045
2301.10823	Reflective Artificial Intelligence	Artificial Intelligence (AI) is about making computers that do the sorts of things that minds can do, and as we progress towards this goal, we tend to increasingly delegate human tasks to machines. However, AI systems usually do these tasks with an unusual imbalance of insight and understanding: new, deeper insights ar...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	341,916
2412.02980	Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models	Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct comparisons among synthetic data generation algorithms are scarce, making it difficult to understand where improvement comes from and what bottleneck...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	513,763
1704.07942	POMDPs for Robotic Arm Search and Reach to Known Objects	We propose an approach based on probabilistic models, in particular POMDPs, to plan optimized search processes of known objects by intelligent eye in hand robotic arms. Searching and reaching for a known object (a pen, a book, or a hammer) in one's office is an operation that humans perform frequently in their daily ac...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	72,445
1207.1384	Modeling Transportation Routines using Hybrid Dynamic Mixed Networks	This paper describes a general framework called Hybrid Dynamic Mixed Networks (HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of discrete deterministic information in the form of constraints. We propose approximate inference algorithms that integrate and adjust well known algorithmic princi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	17,265
2502.11401	Following the Autoregressive Nature of LLM Embeddings via Compression and Alignment	A new trend uses LLMs as dense text encoders via contrastive learning. However, since LLM embeddings predict the probability distribution of the next token, they are inherently generative and distributive, conflicting with contrastive learning, which requires embeddings to capture full-text semantics and align via cosi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	534,344
2103.07234	Coded-Caching using Adaptive Transmission	Coded-caching is a promising technique to reduce the peak rate requirement of backhaul links during high traffic periods. In this letter, we study the effect of adaptive transmission on the performance of coded-caching based networks. Particularly, concentrating on the reduction of backhaul peak load during the high tr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	224,535
2404.02502	Nonlinear integral extension of PID control with improved convergence of perturbed second-order dynamic systems	Nonlinear extension of the integral part of a standard proportional-integral-derivative (PID) feedback control is proposed for the perturbed second-order systems. For the matched constant perturbations, the global asymptotic stability is shown, while for Lipschitz perturbations an ultimately bounded output error is gua...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	443,882
2101.10200	Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolution	The PROBA-V Super-Resolution challenge distributes real low-resolution image series and corresponding high-resolution targets to advance research on Multi-Image Super Resolution (MISR) for satellite images. However, in the PROBA-V dataset the low-resolution image corresponding to the high-resolution target is not ident...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	216,852
2108.11283	Automatic Feature Highlighting in Noisy RES Data With CycleGAN	Radio echo sounding (RES) is a common technique used in subsurface glacial imaging, which provides insight into the underlying rock and ice. However, systematic noise is introduced into the data during collection, complicating interpretation of the results. Researchers most often use a combination of manual interpretat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	252,140
2003.07305	DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction	Deep reinforcement learning can learn effective policies for a wide range of tasks, but is notoriously difficult to use due to instability and sensitivity to hyperparameters. The reasons for this remain unclear. When using standard supervised methods (e.g., for bandits), on-policy data collection provides "hard negativ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	168,381
2410.08058	Closing the Loop: Learning to Generate Writing Feedback via Language Model Simulated Student Revisions	Providing feedback is widely recognized as crucial for refining students' writing skills. Recent advances in language models (LMs) have made it possible to automatically generate feedback that is actionable and well-aligned with human-specified attributes. However, it remains unclear whether the feedback generated by t...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	496,929
2401.02759	Detection and Classification of Diabetic Retinopathy using Deep Learning Algorithms for Segmentation to Facilitate Referral Recommendation for Test and Treatment Prediction	This research paper addresses the critical challenge of diabetic retinopathy (DR), a severe complication of diabetes leading to potential blindness. The proposed methodology leverages transfer learning with convolutional neural networks (CNNs) for automatic DR detection using a single fundus photograph, demonstrating h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	419,839
1911.03353	SEPT: Improving Scientific Named Entity Recognition with Span Representation	We introduce a new scientific named entity recognizer called SEPT, which stands for Span Extractor with Pre-trained Transformers. In recent papers, span extractors have been demonstrated to be a powerful model compared with sequence labeling models. However, we discover that with the development of pre-trained language...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	152,627
2405.07135	Post Training Quantization of Large Language Models with Microscaling Formats	Large Language Models (LLMs) have distinguished themselves with outstanding performance in complex language modeling tasks, yet they come with significant computational and storage challenges. This paper explores the potential of quantization to mitigate these challenges. We systematically study the combined applicatio...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	453,594
2406.18173	UIO-LLMs: Unbiased Incremental Optimization for Long-Context LLMs	Managing long texts is challenging for large language models (LLMs) due to limited context window sizes. This study introduces UIO-LLMs, an unbiased incremental optimization approach for memory-enhanced transformers under long-context settings. We initially conceptualize the process as a streamlined encoder-decoder fra...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	467,906
2403.00394	List-Mode PET Image Reconstruction Using Dykstra-Like Splitting	Convergence of the block iterative method in image reconstruction for positron emission tomography (PET) requires careful control of relaxation parameters, which is a challenging task. The automatic determination of relaxation parameters for list-mode reconstructions also remains challenging. Therefore, a different app...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,963
2306.03528	Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses	For vehicular metaverses, one of the ultimate user-centric goals is to optimize the immersive experience and Quality of Service (QoS) for users on board. Semantic Communication (SemCom) has been introduced as a revolutionary paradigm that significantly eases communication resource pressure for vehicular metaverse appli...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	371,373
2206.12542	Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning	Deep reinforcement learning (RL) algorithms suffer severe performance degradation when the interaction data is scarce, which limits their real-world application. Recently, visual representation learning has been shown to be effective and promising for boosting sample efficiency in RL. These methods usually rely on cont...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	304,639
2405.06183	Arctic: A Field Programmable Quantum Array Scheduling Technique	Advancements in neutral atom quantum computers have positioned them as a valuable framework for quantum computing, largely due to their prolonged coherence times and capacity for high-fidelity gate operations. Recently, neutral atom computers have enabled coherent atom shuttling to facilitate long-range connectivity as...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	453,200
1801.02564	Sampling Almost Periodic and related Functions	We consider certain finite sets of circle-valued functions defined on intervals of real numbers and estimate how large the intervals must be for the values of these functions to be uniformly distributed in an approximate way. This is used to establish some general conditions under which a random construction introduc...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	87,943
2408.15971	BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems	Large Language Models (LLMs) are becoming increasingly powerful and capable of handling complex tasks, e.g., building single agents and multi-agent systems. Compared to single agents, multi-agent systems have higher requirements for the collaboration capabilities of language models. Many benchmarks are proposed to eval...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	484,143
2210.08478	Increasing Visual Awareness in Multimodal Neural Machine Translation from an Information Theoretic Perspective	Multimodal machine translation (MMT) aims to improve translation quality by equipping the source sentence with its corresponding image. Despite the promising performance, MMT models still suffer the problem of input degradation: models focus more on textual information while visual information is generally overlooked. ...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	324,167
1504.04208	Contextualization of topics - browsing through terms, authors, journals and cluster allocations	This paper builds on an innovative Information Retrieval tool, Ariadne. The tool has been developed as an interactive network visualization and browsing tool for large-scale bibliographic databases. It basically allows to gain insights into a topic by contextualizing a search query (Koopman et al., 2015). In this paper...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	42,115
1812.05929	Model-free Training of End-to-end Communication Systems	The idea of end-to-end learning of communication systems through neural network-based autoencoders has the shortcoming that it requires a differentiable channel model. We present in this paper a novel learning algorithm which alleviates this problem. The algorithm enables training of communication systems with an unkno...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	116,506
1702.07965	Distributed Frequency Control with Operational Constraints, Part I: Per-Node Power Balance	This paper addresses the distributed optimal frequency control of multi-area power system with operational constraints, including the regulation capacity of individual control area and the power limits on tie-lines. Both generators and controllable loads are utilized to recover nominal frequencies while minimizing regu...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	68,879
2311.11482	Meta Prompting for AI Systems	In this work, we present a comprehensive study of Meta Prompting (MP), an innovative technique reshaping the utilization of language models (LMs) and AI systems in problem-solving and data interaction. Grounded in type theory and category theory, Meta Prompting emphasizes the structure and syntax of information over tr...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	408,964
2109.04834	An Evaluation Dataset and Strategy for Building Robust Multi-turn Response Selection Model	Multi-turn response selection models have recently shown comparable performance to humans in several benchmark datasets. However, in the real environment, these models often have weaknesses, such as making incorrect predictions based heavily on superficial patterns without a comprehensive understanding of the context. ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	254,557
2004.01800	Temporally Distributed Networks for Fast Video Semantic Segmentation	We present TDNet, a temporally distributed network designed for fast and accurate video semantic segmentation. We observe that features extracted from a certain high-level layer of a deep CNN can be approximated by composing features extracted from several shallower sub-networks. Leveraging the inherent temporal contin...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	171,011
2009.11483	Quarantined! Examining the Effects of a Community-Wide Moderation Intervention on Reddit	Should social media platforms override a community's self-policing when it repeatedly break rules? What actions can they consider? In light of this debate, platforms have begun experimenting with softer alternatives to outright bans. We examine one such intervention called quarantining, that impedes direct access to an...	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	197,183
1705.01759	Deep 360 Pilot: Learning a Deep Agent for Piloting through 360{\deg} Sports Video	Watching a 360{\deg} sports video requires a viewer to continuously select a viewing angle, either through a sequence of mouse clicks or head movements. To relieve the viewer from this "360 piloting" task, we propose "deep 360 pilot" -- a deep learning-based agent for piloting through 360{\deg} sports videos automatica...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	72,884
2307.11770	Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization	Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving spatializations for text corpora as two-dimensional scatter plots, reflecting semantic similarity b...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	381,021
2210.07468	Transparency Helps Reveal When Language Models Learn Meaning	Many current NLP systems are built from language models trained to optimize unsupervised objectives on large amounts of raw text. Under what conditions might such a procedure acquire meaning? Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denota...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	323,723
2304.13536	Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models	Recent work in XAI for eye tracking data has evaluated the suitability of feature attribution methods to explain the output of deep neural sequence models for the task of oculomotric biometric identification. These methods provide saliency maps to highlight important input features of a specific eye gaze sequence. Howe...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	360,614
1709.00149	Learning what to read: Focused machine reading	Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch machine reading of literature at today's scale (PubMed alone indexes o...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	79,859
1709.04889	Control-Oriented Learning on the Fly	This paper focuses on developing a strategy for control of systems whose dynamics are almost entirely unknown. This situation arises naturally in a scenario where a system undergoes a critical failure. In that case, it is imperative to retain the ability to satisfy basic control objectives in order to avert an imminent...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	80,748
2411.15084	Leapfrog Latent Consistency Model (LLCM) for Medical Images Generation	The scarcity of accessible medical image data poses a significant obstacle in effectively training deep learning models for medical diagnosis, as hospitals refrain from sharing their data due to privacy concerns. In response, we gathered a diverse dataset named MedImgs, which comprises over 250,127 images spanning 61 d...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	510,430
2101.07423	Submodular Maximization via Taylor Series Approximation	We study submodular maximization problems with matroid constraints, in particular, problems where the objective can be expressed via compositions of analytic and multilinear functions. We show that for functions of this form, the so-called continuous greedy algorithm attains a ratio arbitrarily close to $(1-1/e) \appro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	216,028
2209.04836	Git Re-Basin: Merging Models modulo Permutation Symmetries	The success of deep learning is due in large part to our ability to solve certain massive non-convex optimization problems with relative ease. Though non-convex optimization is NP-hard, simple algorithms -- often variants of stochastic gradient descent -- exhibit surprising effectiveness in fitting large neural network...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	316,900
2302.01384	Energy-Inspired Self-Supervised Pretraining for Vision Models	Motivated by the fact that forward and backward passes of a deep network naturally form symmetric mappings between input and output representations, we introduce a simple yet effective self-supervised vision model pretraining framework inspired by energy-based models (EBMs). In the proposed framework, we model energy e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	343,575
2010.14627	Wikipedia: A Challenger's Best Friend? Utilising Information-seeking Behaviour Patterns to Predict US Congressional Elections	Election prediction has long been an evergreen in political science literature. Traditionally, such efforts included polling aggregates, economic indicators, partisan affiliation, and campaign effects to predict aggregate voting outcomes. With increasing secondary usage of online-generated data in social science, resea...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	203,514
2303.11191	A Survey of Demonstration Learning	With the fast improvement of machine learning, reinforcement learning (RL) has been used to automate human tasks in different areas. However, training such agents is difficult and restricted to expert users. Moreover, it is mostly limited to simulation environments due to the high cost and safety concerns of interactio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	352,748

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