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
2010.13624	Wind Power Transmission System Integration -- a Case Study of China Wind Power Base	Due to a series of supporting policies in recent years, China wind power has developed rapidly through a large-scale and centralized mode. This paper analyzes the two major concerns faced by wind power development in China: wind generation reliability and wind energy balancing. More specifically, wind farm tripping-off...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	203,195
2403.10438	Data Ethics Emergency Drill: A Toolbox for Discussing Responsible AI for Industry Teams	Researchers urge technology practitioners such as data scientists to consider the impacts and ethical implications of algorithmic decisions. However, unlike programming, statistics, and data management, discussion of ethical implications is rarely included in standard data science training. To begin to address this gap...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	438,201
2009.13863	Distributed ADMM with Synergetic Communication and Computation	In this paper, we propose a novel distributed alternating direction method of multipliers (ADMM) algorithm with synergetic communication and computation, called SCCD-ADMM, to reduce the total communication and computation cost of the system. Explicitly, in the proposed algorithm, each node interacts with only part of i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	197,872
2210.10947	Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?	Decentralized learning has been advocated and widely deployed to make efficient use of distributed datasets, with an extensive focus on supervised learning (SL) problems. Unfortunately, the majority of real-world data are unlabeled and can be highly heterogeneous across sources. In this work, we carefully study decentr...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	325,112
2001.05759	Smart Data driven Decision Trees Ensemble Methodology for Imbalanced Big Data	Differences in data size per class, also known as imbalanced data distribution, have become a common problem affecting data quality. Big Data scenarios pose a new challenge to traditional imbalanced classification algorithms, since they are not prepared to work with such amount of data. Split data strategies and lack o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	160,631
2312.04803	SuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration	We present SuperNormal, a fast, high-fidelity approach to multi-view 3D reconstruction using surface normal maps. With a few minutes, SuperNormal produces detailed surfaces on par with 3D scanners. We harness volume rendering to optimize a neural signed distance function (SDF) powered by multi-resolution hash encoding....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	413,831
2411.07940	Automatic dataset shift identification to support root cause analysis of AI performance drift	Shifts in data distribution can substantially harm the performance of clinical AI models. Hence, various methods have been developed to detect the presence of such shifts at deployment time. However, root causes of dataset shifts are varied, and the choice of shift mitigation strategies is highly dependent on the preci...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	507,726
2107.14425	Enhancing Social Relation Inference with Concise Interaction Graph and Discriminative Scene Representation	There has been a recent surge of research interest in attacking the problem of social relation inference based on images. Existing works classify social relations mainly by creating complicated graphs of human interactions, or learning the foreground and/or background information of persons and objects, but ignore holi...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	248,465
2208.09736	C$^{2}$IMUFS: Complementary and Consensus Learning-based Incomplete Multi-view Unsupervised Feature Selection	Multi-view unsupervised feature selection (MUFS) has been demonstrated as an effective technique to reduce the dimensionality of multi-view unlabeled data. The existing methods assume that all of views are complete. However, multi-view data are usually incomplete, i.e., a part of instances are presented on some views b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	313,817
2111.14193	On data-driven control: informativity of noisy input-output data with cross-covariance bounds	In this paper we develop new data informativity based controller synthesis methods that extend existing frameworks in two relevant directions: a more general noise characterization in terms of cross-covariance bounds and informativity conditions for control based on input-output data. Previous works have derived necess...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	268,520
2108.10026	Deep Relational Metric Learning	This paper presents a deep relational metric learning (DRML) framework for image clustering and retrieval. Most existing deep metric learning methods learn an embedding space with a general objective of increasing interclass distances and decreasing intraclass distances. However, the conventional losses of metric learn...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	251,775
1910.06150	A generalized intelligent quality-based approach for fusing multi-source information	In this paper, we propose a generalized intelligent quality-based approach for fusing multi-source information. The goal of the proposed approach intends to fuse the multi-complex-valued distribution information while maintaining a high quality of the fused result by considering the usage of credible information source...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	149,273
2312.14473	Coordinated Active-Reactive Power Management of ReP2H Systems with Multiple Electrolyzers	Utility-scale renewable power-to-hydrogen (ReP2H) production typically uses thyristor rectifiers (TRs) to supply power to multiple electrolyzers (ELZs). They exhibit a nonlinear and non-decouplable relation between active and reactive power. The on-off scheduling and load allocation of multiple ELZs simultaneously impa...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	417,653
2311.07780	Parrot-Trained Adversarial Examples: Pushing the Practicality of Black-Box Audio Attacks against Speaker Recognition Models	Audio adversarial examples (AEs) have posed significant security challenges to real-world speaker recognition systems. Most black-box attacks still require certain information from the speaker recognition model to be effective (e.g., keeping probing and requiring the knowledge of similarity scores). This work aims to p...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	407,464
1704.08030	Airway segmentation from 3D chest CT volumes based on volume of interest using gradient vector flow	Some lung diseases are related to bronchial airway structures and morphology. Although airway segmentation from chest CT volumes is an important task in the computer-aided diagnosis and surgery assistance systems for the chest, complete 3-D airway structure segmentation is a quite challenging task due to its complex tr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	72,468
2103.10803	Bhattacharyya parameter of monomials codes for the Binary Erasure Channel: from pointwise to average reliability	Monomial codes were recently equipped with partial order relations, fact that allowed researchers to discover structural properties and efficient algorithm for constructing polar codes. Here, we refine the existing order relations in the particular case of Binary Erasure Channel. The new order relation takes us closer ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	225,571
0804.4466	Free Distance Bounds for Protograph-Based Regular LDPC Convolutional Codes	In this paper asymptotic methods are used to form lower bounds on the free distance to constraint length ratio of several ensembles of regular, asymptotically good, protograph-based LDPC convolutional codes. In particular, we show that the free distance to constraint length ratio of the regular LDPC convolutional codes...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,658
1507.08847	A novel multivariate performance optimization method based on sparse coding and hyper-predictor learning	In this paper, we investigate the problem of optimization multivariate performance measures, and propose a novel algorithm for it. Different from traditional machine learning methods which optimize simple loss functions to learn prediction function, the problem studied in this paper is how to learn effective hyper-pred...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	45,606
2305.09122	Power Grid Transient Analysis via Open-Source Circuit Simulator: A Case Study of HVDC	This paper proposes an electronic circuit simulator-based method to accelerate the power system transient simulation, where the modeling of a generic HVDC (High Voltage Direct Current) system is focused. The electronic circuit simulation equations and the backward differentiation formula for numerical solving are descr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	364,523
2401.11420	Embedded Hyperspectral Band Selection with Adaptive Optimization for Image Semantic Segmentation	The selection of hyperspectral bands plays a pivotal role in remote sensing and image analysis, with the aim of identifying the most informative spectral bands while minimizing computational overhead. This paper introduces a pioneering approach for hyperspectral band selection that offers an embedded solution, making i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	422,994
1907.13216	Deep Learning Training on the Edge with Low-Precision Posits	Recently, the posit numerical format has shown promise for DNN data representation and compute with ultra-low precision ([5..8]-bit). However, majority of studies focus only on DNN inference. In this work, we propose DNN training using posits and compare with the floating point training. We evaluate on both MNIST and F...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	140,313
2411.10929	Wildfire Risk Metric Impact on Public Safety Power Shut-off Cost Savings	Public Safety Power Shutoffs (PSPS) are a proactive strategy to mitigate fire hazards from power system infrastructure failures. System operators employ PSPS to deactivate portions of the electric grid with heightened wildfire risks to prevent wildfire ignition and redispatch generators to minimize load shedding. A mea...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	508,853
2202.10236	Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation	The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated b...	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	281,466
2203.12274	Pre-training to Match for Unified Low-shot Relation Extraction	Low-shot relation extraction~(RE) aims to recognize novel relations with very few or even no samples, which is critical in real scenario application. Few-shot and zero-shot RE are two representative low-shot RE tasks, which seem to be with similar target but require totally different underlying abilities. In this paper...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	287,207
2406.12433	LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations	Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms. Traditional reranking models have focused predominantly on accuracy, but modern applications demand consideration of additional criteria such as diversity and fairness. Existing reran...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	465,406
1211.1788	An Adaptive parameter free data mining approach for healthcare application	In today's world, healthcare is the most important factor affecting human life. Due to heavy work load it is not possible for personal healthcare. The proposed system acts as a preventive measure for determining whether a person is fit or unfit based on person's historical and real time data by applying clustering algo...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	19,629
1908.04297	Super-resolution of Omnidirectional Images Using Adversarial Learning	An omnidirectional image (ODI) enables viewers to look in every direction from a fixed point through a head-mounted display providing an immersive experience compared to that of a standard image. Designing immersive virtual reality systems with ODIs is challenging as they require high resolution content. In this paper,...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	141,439
2204.13372	Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods	Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks. To fully exploit the advantages of RISs in wireless systems, the phases of the reflecting elements must be jointly designed with conventional communication resources, such as ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	293,797
2205.06331	Collaborative Multi-agent Stochastic Linear Bandits	We study a collaborative multi-agent stochastic linear bandit setting, where $N$ agents that form a network communicate locally to minimize their overall regret. In this setting, each agent has its own linear bandit problem (its own reward parameter) and the goal is to select the best global action w.r.t. the average o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	296,206
1910.03854	Multimodal representation models for prediction and control from partial information	Similar to humans, robots benefit from interacting with their environment through a number of different sensor modalities, such as vision, touch, sound. However, learning from different sensor modalities is difficult, because the learning model must be able to handle diverse types of signals, and learn a coherent repre...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	148,608
1809.01772	Multi-view Factorization AutoEncoder with Network Constraints for Multi-omic Integrative Analysis	Multi-omic data provides multiple views of the same patients. Integrative analysis of multi-omic data is crucial to elucidate the molecular underpinning of disease etiology. However, multi-omic data has the "big p, small N" problem (the number of features is large, but the number of samples is small), it is challenging...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	106,887
2411.17608	Mixed-State Quantum Denoising Diffusion Probabilistic Model	Generative quantum machine learning has gained significant attention for its ability to produce quantum states with desired distributions. Among various quantum generative models, quantum denoising diffusion probabilistic models (QuDDPMs) [Phys. Rev. Lett. 132, 100602 (2024)] provide a promising approach with stepwise ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	511,517
1804.02722	Lazy Abstraction-Based Controller Synthesis	We present lazy abstraction-based controller synthesis (ABCS) for continuous-time nonlinear dynamical systems against reach-avoid and safety specifications. State-of-the-art multi-layered ABCS pre-computes multiple finite-state abstractions of varying granularity and applies reactive synthesis to the coarsest abstracti...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	94,472
1308.4506	A study of retrieval algorithms of sparse messages in networks of neural cliques	Associative memories are data structures addressed using part of the content rather than an index. They offer good fault reliability and biological plausibility. Among different families of associative memories, sparse ones are known to offer the best efficiency (ratio of the amount of bits stored to that of bits used ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	26,547
2209.14708	TruEyes: Utilizing Microtasks in Mobile Apps for Crowdsourced Labeling of Machine Learning Datasets	The growing use of supervised machine learning in research and industry has increased the need for labeled datasets. Crowdsourcing has emerged as a popular method to create data labels. However, working on large batches of tasks leads to worker fatigue, negatively impacting labeling quality. To address this, we present...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	320,330
2006.11674	Langevin Dynamics for Adaptive Inverse Reinforcement Learning of Stochastic Gradient Algorithms	Inverse reinforcement learning (IRL) aims to estimate the reward function of optimizing agents by observing their response (estimates or actions). This paper considers IRL when noisy estimates of the gradient of a reward function generated by multiple stochastic gradient agents are observed. We present a generalized La...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	183,328
1612.04039	Construction of Full-Diversity LDPC Lattices for Block-Fading Channels	LDPC lattices were the first family of lattices which have an efficient decoding algorithm in high dimensions over an AWGN channel. Considering Construction D' of lattices with one binary LDPC code as underlying code gives the well known Construction A LDPC lattices or 1-level LDPC lattices. Block-fading channel (BF) i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	65,470
2002.12455	Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?	Substantial efforts have been made on improving the generalization abilities of deep neural networks (DNNs) in order to obtain better performances without introducing more parameters. On the other hand, meta-learning approaches exhibit powerful generalization on new tasks in few-shot learning. Intuitively, few-shot lea...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	166,033
2502.06787	Visual Agentic AI for Spatial Reasoning with a Dynamic API	Visual reasoning -- the ability to interpret the visual world -- is crucial for embodied agents that operate within three-dimensional scenes. Progress in AI has led to vision and language models capable of answering questions from images. However, their performance declines when tasked with 3D spatial reasoning. To tac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	532,244
2406.03233	Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN	In High Energy Physics simulations play a crucial role in unraveling the complexities of particle collision experiments within CERN's Large Hadron Collider. Machine learning simulation methods have garnered attention as promising alternatives to traditional approaches. While existing methods mainly employ Variational A...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	461,154
2410.13597	Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model	Molecular optimization (MO) is a crucial stage in drug discovery in which task-oriented generated molecules are optimized to meet practical industrial requirements. Existing mainstream MO approaches primarily utilize external property predictors to guide iterative property optimization. However, learning all molecular ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	499,600
1112.2816	Phase transition to two-peaks phase in an information cascade voting experiment	Observational learning is an important information aggregation mechanism. However, it occasionally leads to a state in which an entire population chooses a sub-optimal option. When it occurs and whether it is a phase transition remain unanswered. To address these questions, we performed a voting experiment in which sub...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	13,448
2312.05028	Cluster images with AntClust: a clustering algorithm based on the chemical recognition system of ants	We implement AntClust, a clustering algorithm based on the chemical recognition system of ants and use it to cluster images of cars. We will give a short recap summary of the main working principles of the algorithm as devised by the original paper [1]. Further, we will describe how to define a similarity function for ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	413,926
1904.00172	EE-AE: An Exclusivity Enhanced Unsupervised Feature Learning Approach	Unsupervised learning is becoming more and more important recently. As one of its key components, the autoencoder (AE) aims to learn a latent feature representation of data which is more robust and discriminative. However, most AE based methods only focus on the reconstruction within the encoder-decoder phase, which ig...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	125,812
1111.0379	Fast reconstruction of phylogenetic trees using locality-sensitive hashing	We present the first sub-quadratic time algorithm that with high probability correctly reconstructs phylogenetic trees for short sequences generated by a Markov model of evolution. Due to rapid expansion in sequence databases, such very fast algorithms are becoming necessary. Other fast heuristics have been developed f...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,873
2104.00163	DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation	In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	227,894
2412.08021	Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning	Self-supervised learning has the potential of lifting several of the key challenges in reinforcement learning today, such as exploration, representation learning, and reward design. Recent work (METRA) has effectively argued that moving away from mutual information and instead optimizing a certain Wasserstein distance ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	515,904
1305.1112	json2run: a tool for experiment design & analysis	json2run is a tool to automate the running, storage and analysis of experiments. The main advantage of json2run is that it allows to describe a set of experiments concisely as a JSON-formatted parameter tree. It also supports parallel execution of experiments, automatic parameter tuning through the F-Race framework and...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	24,406
2310.07338	From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language Models	Tabular data is foundational to predictive modeling in various crucial industries, including healthcare, finance, retail, sustainability, etc. Despite the progress made in specialized models, there is an increasing demand for universal models that can transfer knowledge, generalize from limited data, and follow human i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	398,933
2407.14570	Are handcrafted filters helpful for attributing AI-generated images?	Recently, a vast number of image generation models have been proposed, which raises concerns regarding the misuse of these artificial intelligence (AI) techniques for generating fake images. To attribute the AI-generated images, existing schemes usually design and train deep neural networks (DNNs) to learn the model fi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	474,833
2006.03963	Combinatorial Black-Box Optimization with Expert Advice	We consider the problem of black-box function optimization over the boolean hypercube. Despite the vast literature on black-box function optimization over continuous domains, not much attention has been paid to learning models for optimization over combinatorial domains until recently. However, the computational comple...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	180,499
2111.02249	Learned Image Compression for Machine Perception	Recent work has shown that learned image compression strategies can outperform standard hand-crafted compression algorithms that have been developed over decades of intensive research on the rate-distortion trade-off. With growing applications of computer vision, high quality image reconstruction from a compressible re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	264,813
2308.11249	Video BagNet: short temporal receptive fields increase robustness in long-term action recognition	Previous work on long-term video action recognition relies on deep 3D-convolutional models that have a large temporal receptive field (RF). We argue that these models are not always the best choice for temporal modeling in videos. A large temporal receptive field allows the model to encode the exact sub-action order of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	387,069
1606.05593	Introspective Agents: Confidence Measures for General Value Functions	Agents of general intelligence deployed in real-world scenarios must adapt to ever-changing environmental conditions. While such adaptive agents may leverage engineered knowledge, they will require the capacity to construct and evaluate knowledge themselves from their own experience in a bottom-up, constructivist fashi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	57,429
1811.10396	Learning to Skip Ineffectual Recurrent Computations in LSTMs	Long Short-Term Memory (LSTM) is a special class of recurrent neural network, which has shown remarkable successes in processing sequential data. The typical architecture of an LSTM involves a set of states and gates: the states retain information over arbitrary time intervals and the gates regulate the flow of informa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	114,479
0911.0183	A Gibbs Sampling Based MAP Detection Algorithm for OFDM Over Rapidly Varying Mobile Radio Channels	In orthogonal frequency-division multiplexing (OFDM) systems operating over rapidly time-varying channels, the orthogonality between subcarriers is destroyed leading to inter-carrier interference (ICI) and resulting in an irreducible error floor. In this paper, a new and low-complexity maximum {\em a posteriori} probab...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,838
2204.00824	Graph-based Approximate NN Search: A Revisit	Nearest neighbor search plays a fundamental role in many disciplines such as multimedia information retrieval, data-mining, and machine learning. The graph-based search approaches show superior performance over other types of approaches in recent studies. In this paper, the graph-based NN search is revisited. We optimi...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	289,400
1805.06201	Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations	We propose a novel data augmentation for labeled sentences called contextual augmentation. We assume an invariance that sentences are natural even if the words in the sentences are replaced with other words with paradigmatic relations. We stochastically replace words with other words that are predicted by a bi-directio...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	97,556
1711.08565	Person Transfer GAN to Bridge Domain Gap for Person Re-Identification	Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e.g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network. To facilitate the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,227
2401.03205	The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models	In the era of large language models (LLMs), hallucination (i.e., the tendency to generate factually incorrect content) poses great challenge to trustworthy and reliable deployment of LLMs in real-world applications. To tackle the LLM hallucination, three key questions should be well studied: how to detect hallucination...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	420,020
1703.09026	Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video	Manual annotations of temporal bounds for object interactions (i.e. start and end times) are typical training input to recognition, localization and detection algorithms. For three publicly available egocentric datasets, we uncover inconsistencies in ground truth temporal bounds within and across annotators and dataset...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	70,687
2407.01065	Improve ROI with Causal Learning and Conformal Prediction	In the commercial sphere, such as operations and maintenance, advertising, and marketing recommendations, intelligent decision-making utilizing data mining and neural network technologies is crucial, especially in resource allocation to optimize ROI. This study delves into the Cost-aware Binary Treatment Assignment Pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	469,134
2211.02643	A Transformer Architecture for Online Gesture Recognition of Mathematical Expressions	The Transformer architecture is shown to provide a powerful framework as an end-to-end model for building expression trees from online handwritten gestures corresponding to glyph strokes. In particular, the attention mechanism was successfully used to encode, learn and enforce the underlying syntax of expressions creat...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	328,648
2311.14094	Robust Decision Aggregation with Second-order Information	We consider a decision aggregation problem with two experts who each make a binary recommendation after observing a private signal about an unknown binary world state. An agent, who does not know the joint information structure between signals and states, sees the experts' recommendations and aims to match the action w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	409,991
2206.04733	On Low-Complexity Quickest Intervention of Mutated Diffusion Processes Through Local Approximation	We consider the problem of controlling a mutated diffusion process with an unknown mutation time. The problem is formulated as the quickest intervention problem with the mutation modeled by a change-point, which is a generalization of the quickest change-point detection (QCD). Our goal is to intervene in the mutated pr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	301,743
2011.10704	Neural Group Testing to Accelerate Deep Learning	Recent advances in deep learning have made the use of large, deep neural networks with tens of millions of parameters. The sheer size of these networks imposes a challenging computational burden during inference. Existing work focuses primarily on accelerating each forward pass of a neural network. Inspired by the grou...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	207,593
2012.12060	Information Leakage Games: Exploring Information as a Utility Function	A common goal in the areas of secure information flow and privacy is to build effective defenses against unwanted leakage of information. To this end, one must be able to reason about potential attacks and their interplay with possible defenses. In this paper, we propose a game-theoretic framework to formalize strategi...	false	false	false	false	true	false	false	false	false	true	false	false	true	false	false	false	false	true	212,810
2207.04789	bloomRF: On Performing Range-Queries in Bloom-Filters with Piecewise-Monotone Hash Functions and Prefix Hashing	We introduce bloomRF as a unified method for approximate membership testing that supports both point- and range-queries. As a first core idea, bloomRF introduces novel prefix hashing to efficiently encode range information in the hash-code of the key itself. As a second key concept, bloomRF proposes novel piecewise-mon...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	307,311
2201.10249	Diversity in the Music Listening Experience: Insights from Focus Group Interviews	Music listening in today's digital spaces is highly characterized by the availability of huge music catalogues, accessible by people all over the world. In this scenario, recommender systems are designed to guide listeners in finding tracks and artists that best fit their requests, having therefore the power to influen...	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	276,925
2302.09301	Exploring the Representation Manifolds of Stable Diffusion Through the Lens of Intrinsic Dimension	Prompting has become an important mechanism by which users can more effectively interact with many flavors of foundation model. Indeed, the last several years have shown that well-honed prompts can sometimes unlock emergent capabilities within such models. While there has been a substantial amount of empirical explorat...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	346,361
2211.15242	Ising Model on Locally Tree-like Graphs: Uniqueness of Solutions to Cavity Equations	In the study of Ising models on large locally tree-like graphs, in both rigorous and non-rigorous methods one is often led to understanding the so-called belief propagation distributional recursions and its fixed points. We prove that there is at most one non-trivial fixed point for Ising models with zero or certain ra...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	333,175
1809.05127	IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks	Deep neural networks (DNN) excel at extracting patterns. Through representation learning and automated feature engineering on large datasets, such models have been highly successful in computer vision and natural language applications. Designing optimal network architectures from a principled or rational approach howev...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	107,721
2411.02236	3D Audio-Visual Segmentation	Recognizing the sounding objects in scenes is a longstanding objective in embodied AI, with diverse applications in robotics and AR/VR/MR. To that end, Audio-Visual Segmentation (AVS), taking as condition an audio signal to identify the masks of the target sounding objects in an input image with synchronous camera and ...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	505,410
2304.04158	Does Continual Learning Equally Forget All Parameters?	Distribution shift (e.g., task or domain shift) in continual learning (CL) usually results in catastrophic forgetting of neural networks. Although it can be alleviated by repeatedly replaying buffered data, the every-step replay is time-consuming. In this paper, we study which modules in neural networks are more prone ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	357,105
2104.10378	Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model	Human motion recognition (HMR) based on wireless sensing is a low-cost technique for scene understanding. Current HMR systems adopt support vector machines (SVMs) and convolutional neural networks (CNNs) to classify radar signals. However, whether a deeper learning model could improve the system performance is currentl...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	231,560
0904.0814	Stability Analysis and Learning Bounds for Transductive Regression Algorithms	This paper uses the notion of algorithmic stability to derive novel generalization bounds for several families of transductive regression algorithms, both by using convexity and closed-form solutions. Our analysis helps compare the stability of these algorithms. It also shows that a number of widely used transductive r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	3,487
2209.05917	SpaDE: Improving Sparse Representations using a Dual Document Encoder for First-stage Retrieval	Sparse document representations have been widely used to retrieve relevant documents via exact lexical matching. Owing to the pre-computed inverted index, it supports fast ad-hoc search but incurs the vocabulary mismatch problem. Although recent neural ranking models using pre-trained language models can address this p...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	317,241
1006.1699	Multidimensional Datawarehouse with Combination Formula	Multidimensional in data warehouse is a compulsion and become the most important for information delivery, without multidimensional Multidimensional in data warehouse is a compulsion and become the most important for information delivery, without multidimensional datawarehouse is incomplete. Multidimensional give abili...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	6,732
2311.12233	Unifying Corroborative and Contributive Attributions in Large Language Models	As businesses, products, and services spring up around large language models, the trustworthiness of these models hinges on the verifiability of their outputs. However, methods for explaining language model outputs largely fall across two distinct fields of study which both use the term "attribution" to refer to entire...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	409,251
2002.04017	Provable Self-Play Algorithms for Competitive Reinforcement Learning	Self-play, where the algorithm learns by playing against itself without requiring any direct supervision, has become the new weapon in modern Reinforcement Learning (RL) for achieving superhuman performance in practice. However, the majority of exisiting theory in reinforcement learning only applies to the setting wher...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	163,479
2003.12756	Harmonic Decompositions of Convolutional Networks	We present a description of the function space and the smoothness class associated with a convolutional network using the machinery of reproducing kernel Hilbert spaces. We show that the mapping associated with a convolutional network expands into a sum involving elementary functions akin to spherical harmonics. This f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	170,002
2011.14917	Comparative Analysis of Extreme Verification Latency Learning Algorithms	One of the more challenging real-world problems in computational intelligence is to learn from non-stationary streaming data, also known as concept drift. Perhaps even a more challenging version of this scenario is when -- following a small set of initial labeled data -- the data stream consists of unlabeled data only....	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	208,929
2312.15561	README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP	The advancement in healthcare has shifted focus toward patient-centric approaches, particularly in self-care and patient education, facilitated by access to Electronic Health Records (EHR). However, medical jargon in EHRs poses significant challenges in patient comprehension. To address this, we introduce a new task of...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	418,048
1806.00589	Efficient Entropy for Policy Gradient with Multidimensional Action Space	In recent years, deep reinforcement learning has been shown to be adept at solving sequential decision processes with high-dimensional state spaces such as in the Atari games. Many reinforcement learning problems, however, involve high-dimensional discrete action spaces as well as high-dimensional state spaces. This pa...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	99,341
2107.12939	Optimal Frequency Regulation using Packetized Energy Management	Packetized energy management (PEM) is a demand dispatch scheme that can be used to provide ancillary services such as frequency regulation. In PEM, distributed energy resources (DERs) are granted uninterruptible access to the grid for a pre-specified time interval called the packet length. This results in a down ramp-l...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	248,053
2312.11513	Maatphor: Automated Variant Analysis for Prompt Injection Attacks	Prompt injection has emerged as a serious security threat to large language models (LLMs). At present, the current best-practice for defending against newly-discovered prompt injection techniques is to add additional guardrails to the system (e.g., by updating the system prompt or using classifiers on the input and/or ...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	416,602
1003.0691	Statistical and Computational Tradeoffs in Stochastic Composite Likelihood	Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stochastic variation of the composite likelihood function. Each of the estimators resolve the computation-accuracy tradeoff differently, and taken...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	5,833
2402.12212	Polarization of Autonomous Generative AI Agents Under Echo Chambers	Online social networks often create echo chambers where people only hear opinions reinforcing their beliefs. An echo chamber often generates polarization, leading to conflicts caused by people with radical opinions, such as the January 6, 2021, attack on the US Capitol. The echo chamber has been viewed as a human-speci...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	430,754
2501.02278	An experimental comparison of tree-data structures for connectivity queries on fully-dynamic undirected graphs (Extended Version)	During the past decades significant efforts have been made to propose data structures for answering connectivity queries on fully dynamic graphs, i.e., graphs with frequent insertions and deletions of edges. However, a comprehensive understanding of how these data structures perform in practice is missing, since not al...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	522,419
cmp-lg/9406033	Verb Semantics and Lexical Selection	This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentence as well as selection restriction...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,112
1809.06098	Policy Optimization via Importance Sampling	Policy optimization is an effective reinforcement learning approach to solve continuous control tasks. Recent achievements have shown that alternating online and offline optimization is a successful choice for efficient trajectory reuse. However, deciding when to stop optimizing and collect new trajectories is non-triv...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	107,960
2410.16212	Comprehensive benchmarking of large language models for RNA secondary structure prediction	Inspired by the success of large language models (LLM) for DNA and proteins, several LLM for RNA have been developed recently. RNA-LLM uses large datasets of RNA sequences to learn, in a self-supervised way, how to represent each RNA base with a semantically rich numerical vector. This is done under the hypothesis that...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	500,929
2102.09680	Fixing Errors of the Google Voice Recognizer through Phonetic Distance Metrics	Speech recognition systems for the Spanish language, such as Google's, produce errors quite frequently when used in applications of a specific domain. These errors mostly occur when recognizing words new to the recognizer's language model or ad hoc to the domain. This article presents an algorithm that uses Levenshtein...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	220,851
2203.09281	Ranking of Communities in Multiplex Spatiotemporal Models of Brain Dynamics	As a relatively new field, network neuroscience has tended to focus on aggregate behaviours of the brain averaged over many successive experiments or over long recordings in order to construct robust brain models. These models are limited in their ability to explain dynamic state changes in the brain which occurs spont...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	286,102
1602.07337	Sparse Estimation of Multivariate Poisson Log-Normal Models from Count Data	Modeling data with multivariate count responses is a challenging problem due to the discrete nature of the responses. Existing methods for univariate count responses cannot be easily extended to the multivariate case since the dependency among multiple responses needs to be properly accommodated. In this paper, we prop...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	52,494
1907.06417	Quick, Stat!: A Statistical Analysis of the Quick, Draw! Dataset	The Quick, Draw! Dataset is a Google dataset with a collection of 50 million drawings, divided in 345 categories, collected from the users of the game Quick, Draw!. In contrast with most of the existing image datasets, in the Quick, Draw! Dataset, drawings are stored as time series of pencil positions instead of a bitm...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	138,621
2210.00169	Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition	The smaller memory bandwidth in smart devices prompts development of smaller Automatic Speech Recognition (ASR) models. To obtain a smaller model, one can employ the model compression techniques. Knowledge distillation (KD) is a popular model compression approach that has shown to achieve smaller model size with relati...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	320,762
2305.04288	Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion	Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulfill the requirements in preserving \textit{privacy} and maintaining high model \textit{utility}. The...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	362,710
1602.05350	Relative Error Embeddings for the Gaussian Kernel Distance	A reproducing kernel can define an embedding of a data point into an infinite dimensional reproducing kernel Hilbert space (RKHS). The norm in this space describes a distance, which we call the kernel distance. The random Fourier features (of Rahimi and Recht) describe an oblivious approximate mapping into finite dimen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	52,243
2402.12326	PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents	Psychological measurement is essential for mental health, self-understanding, and personal development. Traditional methods, such as self-report scales and psychologist interviews, often face challenges with engagement and accessibility. While game-based and LLM-based tools have been explored to improve user interest a...	true	false	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	430,802

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