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
2407.18516	Integrating Posture Control in Speech Motor Models: A Parallel-Structured Simulation Approach	Posture is an essential aspect of motor behavior, necessitating continuous muscle activation to counteract gravity. It remains stable under perturbation, aiding in maintaining bodily balance and enabling movement execution. Similarities have been observed between gross body postures and speech postures, such as those i...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	476,402
2310.13913	Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models	Protein-ligand structure prediction is an essential task in drug discovery, predicting the binding interactions between small molecules (ligands) and target proteins (receptors). Recent advances have incorporated deep learning techniques to improve the accuracy of protein-ligand structure prediction. Nevertheless, the ...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	401,634
1506.04573	A New PAC-Bayesian Perspective on Domain Adaptation	We study the issue of PAC-Bayesian domain adaptation: We want to learn, from a source domain, a majority vote model dedicated to a target one. Our theoretical contribution brings a new perspective by deriving an upper-bound on the target risk where the distributions' divergence---expressed as a ratio---controls the tra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	44,183
1401.3860	Planning with Noisy Probabilistic Relational Rules	Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually interpretable and they can be learned effectively from the action experiences in complex worlds. We investigate reasoning with such rules in gr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	29,974
2412.16521	Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations	The accuracy of deep neural networks is significantly influenced by the effectiveness of mini-batch construction during training. In single-label scenarios, such as binary and multi-class classification tasks, it has been demonstrated that batch selection algorithms preferring samples with higher uncertainty achieve be...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	519,565
2412.15877	Approximate State Abstraction for Markov Games	This paper introduces state abstraction for two-player zero-sum Markov games (TZMGs), where the payoffs for the two players are determined by the state representing the environment and their respective actions, with state transitions following Markov decision processes. For example, in games like soccer, the value of a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	519,298
2307.12540	UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection	Visual anomaly detection aims to learn normality from normal images, but existing approaches are fragmented across various tasks: defect detection, semantic anomaly detection, multi-class anomaly detection, and anomaly clustering. This one-task-one-model approach is resource-intensive and incurs high maintenance costs ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	381,291
2009.00092	Data and Image Prior Integration for Image Reconstruction Using Consensus Equilibrium	Image domain prior models have been shown to improve the quality of reconstructed images, especially when data are limited. Pre-processing of raw data, through the implicit or explicit inclusion of data domain priors have separately also shown utility in improving reconstructions. In this work, a principled approach is...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	193,948
2104.03000	Universal Adversarial Training with Class-Wise Perturbations	Despite their overwhelming success on a wide range of applications, convolutional neural networks (CNNs) are widely recognized to be vulnerable to adversarial examples. This intriguing phenomenon led to a competition between adversarial attacks and defense techniques. So far, adversarial training is the most widely use...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	228,934
2406.15279	Safe Inputs but Unsafe Output: Benchmarking Cross-modality Safety Alignment of Large Vision-Language Model	As Artificial General Intelligence (AGI) becomes increasingly integrated into various facets of human life, ensuring the safety and ethical alignment of such systems is paramount. Previous studies primarily focus on single-modality threats, which may not suffice given the integrated and complex nature of cross-modality...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	466,681
1611.08696	Optimizing Expectation with Guarantees in POMDPs (Technical Report)	A standard objective in partially-observable Markov decision processes (POMDPs) is to find a policy that maximizes the expected discounted-sum payoff. However, such policies may still permit unlikely but highly undesirable outcomes, which is problematic especially in safety-critical applications. Recently, there has be...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	64,546
2109.00534	The Minimax Complexity of Distributed Optimization	In this thesis, I study the minimax oracle complexity of distributed stochastic optimization. First, I present the "graph oracle model", an extension of the classic oracle complexity framework that can be applied to study distributed optimization algorithms. Next, I describe a general approach to proving optimization l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	253,134
2111.01662	OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression	Explicit deep generative models (DGMs), e.g., VAEs and Normalizing Flows, have shown to offer an effective data modelling alternative for lossless compression. However, DGMs themselves normally require large storage space and thus contaminate the advantage brought by accurate data density estimation. To eliminate the r...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	264,622
1907.02884	Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model	Intent Detection and Slot Filling are two pillar tasks in Spoken Natural Language Understanding. Common approaches adopt joint Deep Learning architectures in attention-based recurrent frameworks. In this work, we aim at exploiting the success of "recurrence-less" models for these tasks. We introduce Bert-Joint, i.e., a...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	137,711
1905.01000	Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach	Evolving Internet-of-Things (IoT) applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by identifying the type of the surrounding indoor environment. This identification is of high importance since it leads to higher localization accurac...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	129,613
1606.00822	Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis	Previous approaches to model and analyze facial expression analysis use three different techniques: facial action units, geometric features and graph based modelling. However, previous approaches have treated these technique separately. There is an interrelationship between these techniques. The facial expression analy...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	56,714
2010.12379	A Simulation-based Education Approach for the Electromagnetic and Electromechanical Transient Waves in Power Systems	Power systems usually go through electromagnetic and electromechanical transient processes after different disturbances. Learning the characteristics and the differences between them are important but not easy for students majoring in power systems. This paper presents a simulation-based approach to comprehensively stu...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	202,672
2407.07478	EA-VTR: Event-Aware Video-Text Retrieval	Understanding the content of events occurring in the video and their inherent temporal logic is crucial for video-text retrieval. However, web-crawled pre-training datasets often lack sufficient event information, and the widely adopted video-level cross-modal contrastive learning also struggles to capture detailed and...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	471,777
1910.06909	OverQ: Opportunistic Outlier Quantization for Neural Network Accelerators	Outliers in weights and activations pose a key challenge for fixed-point quantization of neural networks. While they can be addressed by fine-tuning, this is not practical for ML service providers (e.g., Google or Microsoft) who often receive customer models without training data. Specialized hardware for handling acti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	149,479
2405.04151	Gas Source Localization Using physics Guided Neural Networks	This work discusses a novel method for estimating the location of a gas source based on spatially distributed concentration measurements taken, e.g., by a mobile robot or flying platform that follows a predefined trajectory to collect samples. The proposed approach uses a Physics-Guided Neural Network to approximate th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	452,467
2002.09928	Predictive Sampling with Forecasting Autoregressive Models	Autoregressive models (ARMs) currently hold state-of-the-art performance in likelihood-based modeling of image and audio data. Generally, neural network based ARMs are designed to allow fast inference, but sampling from these models is impractically slow. In this paper, we introduce the predictive sampling algorithm: a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	165,233
1711.03034	Optimal Control of Storage Regeneration with Repair Codes	High availability of containerized applications requires to perform robust storage of applications' state. Since basic replication techniques are extremely costly at scale, storage space requirements can be reduced by means of erasure or repairing codes. In this paper we address storage regeneration using repair codes,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	84,147
1702.05891	Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification	Multi-label image classification is a fundamental but challenging task in computer vision. Great progress has been achieved by exploiting semantic relations between labels in recent years. However, conventional approaches are unable to model the underlying spatial relations between labels in multi-label images, because...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	68,495
2407.00541	Answering real-world clinical questions using large language model based systems	Evidence to guide healthcare decisions is often limited by a lack of relevant and trustworthy literature as well as difficulty in contextualizing existing research for a specific patient. Large language models (LLMs) could potentially address both challenges by either summarizing published literature or generating new ...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	468,906
1505.03235	Optimizing Ad Allocation in Social Advertising	Social advertising (or social promotion) is an effective approach that produces a significant cascade of adoption through influence in the online social networks. The goal of this work is to optimize the ad allocation from the platform's perspective. On the one hand, the platform would like to maximize revenue earned f...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	43,056
2405.07314	Learnable Item Tokenization for Generative Recommendation	Utilizing powerful Large Language Models (LLMs) for generative recommendation has attracted much attention. Nevertheless, a crucial challenge is transforming recommendation data into the language space of LLMs through effective item tokenization. Current approaches, such as ID, textual, and codebook-based identifiers, ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	453,661
2501.05494	Mathematical Modeling and Machine Learning for Predicting Shade-Seeking Behavior in Cows Under Heat Stress	In this paper we develop a mathematical model combined with machine learning techniques to predict shade-seeking behavior in cows exposed to heat stress. The approach integrates advanced mathematical features, such as time-averaged thermal indices and accumulated heat stress metrics, obtained by mathematical analysis o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	523,621
2111.06420	Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities	The past decade has seen significant progress in artificial intelligence (AI), which has resulted in algorithms being adopted for resolving a variety of problems. However, this success has been met by increasing model complexity and employing black-box AI models that lack transparency. In response to this need, Explain...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	266,070
2206.08916	Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks	We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical computer vision tasks, including pose estimation, object detection, depth estimation and image generation, vision-and-language tasks such as region captioning and referring expression, to natural language processing tasks such a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	303,350
2502.03963	AL-PINN: Active Learning-Driven Physics-Informed Neural Networks for Efficient Sample Selection in Solving Partial Differential Equations	Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving Partial Differential Equations (PDEs) by incorporating physical constraints into deep learning models. However, standard PINNs often require a large number of training samples to achieve high accuracy, leading to increased computa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,932
2412.02030	NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic Adversarial Training	We introduce NitroFusion, a fundamentally different approach to single-step diffusion that achieves high-quality generation through a dynamic adversarial framework. While one-step methods offer dramatic speed advantages, they typically suffer from quality degradation compared to their multi-step counterparts. Just as a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	513,348
1708.04013	Pilot Contamination for Wideband Massive MMO: Number of cells Vs Multipath	This paper proposes a novel joint channel estimation and beamforming approach for multicell wideband massive multiple input multiple output (MIMO) systems. With the proposed channel estimation and beamforming approach, we determine the number of cells $N_c$ that can utilize the same time and frequency resource while mi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,871
2106.00358	Towards Efficient Cross-Modal Visual Textual Retrieval using Transformer-Encoder Deep Features	Cross-modal retrieval is an important functionality in modern search engines, as it increases the user experience by allowing queries and retrieved objects to pertain to different modalities. In this paper, we focus on the image-sentence retrieval task, where the objective is to efficiently find relevant images for a g...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	238,092
2104.02532	End-To-End Bias Mitigation: Removing Gender Bias in Deep Learning	Machine Learning models have been deployed across many different aspects of society, often in situations that affect social welfare. Although these models offer streamlined solutions to large problems, they may contain biases and treat groups or individuals unfairly based on protected attributes such as gender. In this...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	228,749
1901.03941	Aggregating Large-Scale Generalized Energy Storages to Participate in Energy Market and Regulation Market	This paper proposes a concept of generalized energy storage (GES) to facilitate the integration of large-scale heterogeneous flexible resources with electric/thermal energy storage capacity to participate in multiple markets. First, a generalized state variable referred to as degree of satisfaction (DoS) is defined, an...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	118,529
1805.03908	Towards a universal neural network encoder for time series	We study the use of a time series encoder to learn representations that are useful on data set types with which it has not been trained on. The encoder is formed of a convolutional neural network whose temporal output is summarized by a convolutional attention mechanism. This way, we obtain a compact, fixed-length repr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	97,143
2011.04184	Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character Augmentation	We propose a new character-based text classification framework for non-alphabetic languages, such as Chinese and Japanese. Our framework consists of a variational character encoder (VCE) and character-level text classifier. The VCE is composed of a $\beta$-variational auto-encoder ($\beta$-VAE) that learns the proposed...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	205,495
2105.03193	Network Pruning That Matters: A Case Study on Retraining Variants	Network pruning is an effective method to reduce the computational expense of over-parameterized neural networks for deployment on low-resource systems. Recent state-of-the-art techniques for retraining pruned networks such as weight rewinding and learning rate rewinding have been shown to outperform the traditional fi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	234,074
1405.0770	Attributes Coupling based Item Enhanced Matrix Factorization Technique for Recommender Systems	Recommender system has attracted lots of attentions since it helps users alleviate the information overload problem. Matrix factorization technique is one of the most widely employed collaborative filtering techniques in the research of recommender systems due to its effectiveness and efficiency in dealing with very la...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	32,800
2003.08628	Foldover Features for Dynamic Object Behavior Description in Microscopic Videos	Behavior description is conducive to the analysis of tiny objects, similar objects, objects with weak visual information and objects with similar visual information, playing a fundamental role in the identification and classification of dynamic objects in microscopic videos. To this end, we propose foldover features to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,801
2403.14668	Predicting Learning Performance with Large Language Models: A Study in Adult Literacy	Intelligent Tutoring Systems (ITSs) have significantly enhanced adult literacy training, a key factor for societal participation, employment opportunities, and lifelong learning. Our study investigates the application of advanced AI models, including Large Language Models (LLMs) like GPT-4, for predicting learning perf...	false	false	false	false	true	false	true	false	true	false	false	false	false	true	false	false	false	false	440,189
1606.05759	Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015	The paper describes the Egyptian Arabic-to-English statistical machine translation (SMT) system that the QCRI-Columbia-NYUAD (QCN) group submitted to the NIST OpenMT'2015 competition. The competition focused on informal dialectal Arabic, as used in SMS, chat, and speech. Thus, our efforts focused on processing and stan...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	57,465
2005.09284	ISeeU2: Visually Interpretable ICU mortality prediction using deep learning and free-text medical notes	Accurate mortality prediction allows Intensive Care Units (ICUs) to adequately benchmark clinical practice and identify patients with unexpected outcomes. Traditionally, simple statistical models have been used to assess patient death risk, many times with sub-optimal performance. On the other hand deep learning holds ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	177,886
2106.08649	Improving the expressiveness of neural vocoding with non-affine Normalizing Flows	This paper proposes a general enhancement to the Normalizing Flows (NF) used in neural vocoding. As a case study, we improve expressive speech vocoding with a revamped Parallel Wavenet (PW). Specifically, we propose to extend the affine transformation of PW to the more expressive invertible non-affine function. The gre...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	241,372
2301.12671	Optimal Decision Trees For Interpretable Clustering with Constraints (Extended Version)	Constrained clustering is a semi-supervised task that employs a limited amount of labelled data, formulated as constraints, to incorporate domain-specific knowledge and to significantly improve clustering accuracy. Previous work has considered exact optimization formulations that can guarantee optimal clustering while ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	342,634
1907.09423	Satellite-Net: Automatic Extraction of Land Cover Indicators from Satellite Imagery by Deep Learning	In this paper we address the challenge of land cover classification for satellite images via Deep Learning (DL). Land Cover aims to detect the physical characteristics of the territory and estimate the percentage of land occupied by a certain category of entities: vegetation, residential buildings, industrial areas, fo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	139,352
2408.06854	LoRA$^2$ : Multi-Scale Low-Rank Approximations for Fine-Tuning Large Language Models	Fine-tuning large language models (LLMs) with high parameter efficiency for downstream tasks has become a new paradigm. Low-Rank Adaptation (LoRA) significantly reduces the number of trainable parameters for fine-tuning. Although it has demonstrated commendable performance, updating parameters within a single scale may...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	480,372
2009.07417	Too Much Information Kills Information: A Clustering Perspective	Clustering is one of the most fundamental tools in the artificial intelligence area, particularly in the pattern recognition and learning theory. In this paper, we propose a simple, but novel approach for variance-based k-clustering tasks, included in which is the widely known k-means clustering. The proposed approach ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	195,915
2004.12040	Deep convolutional neural networks for face and iris presentation attack detection: Survey and case study	Biometric presentation attack detection is gaining increasing attention. Users of mobile devices find it more convenient to unlock their smart applications with finger, face or iris recognition instead of passwords. In this paper, we survey the approaches presented in the recent literature to detect face and iris prese...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	174,106
1511.01854	Trace-distance measure of coherence	We show that trace distance measure of coherence is a strong monotone for all qubit and, so called, $X$ states. An expression for the trace distance coherence for all pure states and a semi definite program for arbitrary states is provided. We also explore the relation between $l_1$-norm and relative entropy based meas...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	48,554
1912.08837	Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Data	Due to the ever-growing diversity of the data source, multi-modality feature learning has attracted more and more attention. However, most of these methods are designed by jointly learning feature representation from multi-modalities that exist in both training and test sets, yet they are less investigated in absence o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	157,922
0911.1691	Vertical partitioning of relational OLTP databases using integer programming	A way to optimize performance of relational row store databases is to reduce the row widths by vertically partitioning tables into table fractions in order to minimize the number of irrelevant columns/attributes read by each transaction. This paper considers vertical partitioning algorithms for relational row-store OLT...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	4,902
2208.13298	Goal-Conditioned Q-Learning as Knowledge Distillation	Many applications of reinforcement learning can be formalized as goal-conditioned environments, where, in each episode, there is a "goal" that affects the rewards obtained during that episode but does not affect the dynamics. Various techniques have been proposed to improve performance in goal-conditioned environments,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	315,016
2106.14476	Adventurer's Treasure Hunt: A Transparent System for Visually Grounded Compositional Visual Question Answering based on Scene Graphs	With the expressed goal of improving system transparency and visual grounding in the reasoning process in VQA, we present a modular system for the task of compositional VQA based on scene graphs. Our system is called "Adventurer's Treasure Hunt" (or ATH), named after an analogy we draw between our model's search proced...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	243,429
2209.09007	Comparative Study of Q-Learning and NeuroEvolution of Augmenting Topologies for Self Driving Agents	Autonomous driving vehicles have been of keen interest ever since automation of various tasks started. Humans are prone to exhaustion and have a slow response time on the road, and on top of that driving is already quite a dangerous task with around 1.35 million road traffic incident deaths each year. It is expected th...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	true	false	false	318,342
1409.4958	Tensity Research Based on the Information of Eye Movement	User's mental state is concerned gradually, during the interaction course of human robot. As the measurement and identification method of psychological state, tension, has certain practical significance role. At presents there is no suitable method of measuring the tension. Firstly, sum up some availability of eye move...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	36,123
2007.15678	Mix Dimension in Poincar\'{e} Geometry for 3D Skeleton-based Action Recognition	Graph Convolutional Networks (GCNs) have already demonstrated their powerful ability to model the irregular data, e.g., skeletal data in human action recognition, providing an exciting new way to fuse rich structural information for nodes residing in different parts of a graph. In human action recognition, current work...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,726
1711.01791	HyperNetworks with statistical filtering for defending adversarial examples	Deep learning algorithms have been known to be vulnerable to adversarial perturbations in various tasks such as image classification. This problem was addressed by employing several defense methods for detection and rejection of particular types of attacks. However, training and manipulating networks according to parti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	83,956
2212.13929	Evaluating Generalizability of Deep Learning Models Using Indian-COVID-19 CT Dataset	Computer tomography (CT) have been routinely used for the diagnosis of lung diseases and recently, during the pandemic, for detecting the infectivity and severity of COVID-19 disease. One of the major concerns in using ma-chine learning (ML) approaches for automatic processing of CT scan images in clinical setting is t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	338,447
2409.20502	COLLAGE: Collaborative Human-Agent Interaction Generation using Hierarchical Latent Diffusion and Language Models	We propose a novel framework COLLAGE for generating collaborative agent-object-agent interactions by leveraging large language models (LLMs) and hierarchical motion-specific vector-quantized variational autoencoders (VQ-VAEs). Our model addresses the lack of rich datasets in this domain by incorporating the knowledge a...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	493,145
1702.02363	Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers	Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC) dataset is a collection of automatically categorized and annotated sentences obtained from Wikipedia. We constructed large-scale gazetteers by using a graph crawler algorithm to extract relevant entity and domain information from a semantic kn...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	67,963
2501.11905	Phase Transitions in Phase-Only Compressed Sensing	The goal of phase-only compressed sensing is to recover a structured signal $\mathbf{x}$ from the phases $\mathbf{z} = {\rm sign}(\mathbf{\Phi}\mathbf{x})$ under some complex-valued sensing matrix $\mathbf{\Phi}$. Exact reconstruction of the signal's direction is possible: we can reformulate it as a linear compressed s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	526,089
2407.07200	Measuring Trust for Exoskeleton Systems	Wearable robotic systems are a class of robots that have a tight coupling between human and robot movements. Similar to non-wearable robots, it is important to measure the trust a person has that the robot can support achieving the desired goals. While some measures of trust may apply to all potential robotic roles, th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	471,671
1905.13021	Robustness to Adversarial Perturbations in Learning from Incomplete Data	What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? To provide a concrete answer to this question, this paper unifies two major learning frameworks: Semi-Supervised Learning (SSL) and Distributionally Robust Learning (DRL). We develop a gener...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	132,970
2306.06393	On the required radio resources for ultra-reliable communication in highly interfered scenarios	Future wireless systems are expected to support mission-critical services demanding higher and higher reliability. In this letter, we dimension the radio resources needed to achieve a given failure probability target for ultra-reliable wireless systems in high interference conditions, assuming a protocol with frequency...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	372,590
2403.19559	Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset	Hate speech detection models are only as good as the data they are trained on. Datasets sourced from social media suffer from systematic gaps and biases, leading to unreliable models with simplistic decision boundaries. Adversarial datasets, collected by exploiting model weaknesses, promise to fix this problem. However...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	442,395
cs/0003074	A Finite State and Data-Oriented Method for Grapheme to Phoneme Conversion	A finite-state method, based on leftmost longest-match replacement, is presented for segmenting words into graphemes, and for converting graphemes into phonemes. A small set of hand-crafted conversion rules for Dutch achieves a phoneme accuracy of over 93%. The accuracy of the system is further improved by using transf...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	537,075
2207.03689	Guiding the retraining of convolutional neural networks against adversarial inputs	Background: When using deep learning models, there are many possible vulnerabilities and some of the most worrying are the adversarial inputs, which can cause wrong decisions with minor perturbations. Therefore, it becomes necessary to retrain these models against adversarial inputs, as part of the software testing pro...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	306,946
2411.04389	Approximate FW Algorithm with a novel DMO method over Graph-structured Support Set	In this project, we reviewed a paper that deals graph-structured convex optimization (GSCO) problem with the approximate Frank-Wolfe (FW) algorithm. We analyzed and implemented the original algorithm and introduced some extensions based on that. Then we conducted experiments to compare the results and concluded that ou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,250
2310.04901	WAIT: Feature Warping for Animation to Illustration video Translation using GANs	In this paper, we explore a new domain for video-to-video translation. Motivated by the availability of animation movies that are adopted from illustrated books for children, we aim to stylize these videos with the style of the original illustrations. Current state-of-the-art video-to-video translation models rely on h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	397,877
1802.00332	Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making	Tactical driving decision making is crucial for autonomous driving systems and has attracted considerable interest in recent years. In this paper, we propose several practical components that can speed up deep reinforcement learning algorithms towards tactical decision making tasks: 1) non-uniform action skipping as a ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	89,399
1708.01566	Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes	The success of deep learning in computer vision is based on availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,405
2104.02276	Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding	The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	228,663
2405.13753	A Dynamic Model of Performative Human-ML Collaboration: Theory and Empirical Evidence	Machine learning (ML) models are increasingly used in various applications, from recommendation systems in e-commerce to diagnosis prediction in healthcare. In this paper, we present a novel dynamic framework for thinking about the deployment of ML models in a performative, human-ML collaborative system. In our framewo...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	456,064
1807.03746	Scalable Sparse Subspace Clustering via Ordered Weighted $\ell_1$ Regression	The main contribution of the paper is a new approach to subspace clustering that is significantly more computationally efficient and scalable than existing state-of-the-art methods. The central idea is to modify the regression technique in sparse subspace clustering (SSC) by replacing the $\ell_1$ minimization with a g...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	102,599
2008.11070	An Economic Perspective on Predictive Maintenance of Filtration Units	This paper provides an economic perspective on the predictive maintenance of filtration units. The rise of predictive maintenance is possible due to the growing trend of industry 4.0 and the availability of inexpensive sensors. However, the adoption rate for predictive maintenance by companies remains low. The majority...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	193,171
2106.09900	A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction	In this paper, we propose a novel edge-editing approach to extract relation information from a document. We treat the relations in a document as a relation graph among entities in this approach. The relation graph is iteratively constructed by editing edges of an initial graph, which might be a graph extracted by anoth...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	241,832
2305.08995	Denoising Diffusion Models for Plug-and-Play Image Restoration	Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and interpretable method for solving various inverse problems by utilizing any off-the-shelf denoiser as the implicit image prior. However, most existing methods focus on discriminative Gaussian denoisers. Although diffusion models have shown...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	364,475
2411.01719	LES-SINDy: Laplace-Enhanced Sparse Identification of Nonlinear Dynamical Systems	Sparse Identification of Nonlinear Dynamical Systems (SINDy) is a powerful tool for the data-driven discovery of governing equations. However, it encounters challenges when modeling complex dynamical systems involving high-order derivatives or discontinuities, particularly in the presence of noise. These limitations re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	505,193
2407.06183	Stepping on the Edge: Curvature Aware Learning Rate Tuners	Curvature information -- particularly, the largest eigenvalue of the loss Hessian, known as the sharpness -- often forms the basis for learning rate tuners. However, recent work has shown that the curvature information undergoes complex dynamics during training, going from a phase of increasing sharpness to eventual st...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	471,300
1502.06134	Learning with Square Loss: Localization through Offset Rademacher Complexity	We consider regression with square loss and general classes of functions without the boundedness assumption. We introduce a notion of offset Rademacher complexity that provides a transparent way to study localization both in expectation and in high probability. For any (possibly non-convex) class, the excess loss of a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	40,459
2305.13193	TEIMMA: The First Content Reuse Annotator for Text, Images, and Math	This demo paper presents the first tool to annotate the reuse of text, images, and mathematical formulae in a document pair -- TEIMMA. Annotating content reuse is particularly useful to develop plagiarism detection algorithms. Real-world content reuse is often obfuscated, which makes it challenging to identify such cas...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	366,393
1707.05911	Recognizing and Curating Photo Albums via Event-Specific Image Importance	Automatic organization of personal photos is a problem with many real world ap- plications, and can be divided into two main tasks: recognizing the event type of the photo collection, and selecting interesting images from the collection. In this paper, we attempt to simultaneously solve both tasks: album-wise event rec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,313
2402.14855	An LLM Maturity Model for Reliable and Transparent Text-to-Query	Recognizing the imperative to address the reliability and transparency issues of Large Language Models (LLM), this work proposes an LLM maturity model tailored for text-to-query applications. This maturity model seeks to fill the existing void in evaluating LLMs in such applications by incorporating dimensions beyond m...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	431,876
1810.10307	Topic representation: finding more representative words in topic models	The top word list, i.e., the top-M words with highest marginal probability in a given topic, is the standard topic representation in topic models. Most of recent automatical topic labeling algorithms and popular topic quality metrics are based on it. However, we find, empirically, words in this type of top word list ar...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	111,248
2310.11848	The Value-Sensitive Conversational Agent Co-Design Framework	Conversational agents (CAs) are gaining traction in both industry and academia, especially with the advent of generative AI and large language models. As these agents are used more broadly by members of the general public and take on a number of critical use cases and social roles, it becomes important to consider the ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	400,807
2209.00959	Echocardiographic Image Quality Assessment Using Deep Neural Networks	Echocardiography image quality assessment is not a trivial issue in transthoracic examination. As the in vivo examination of heart structures gained prominence in cardiac diagnosis, it has been affirmed that accurate diagnosis of the left ventricle functions is hugely dependent on the quality of echo images. Up till no...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	315,735
2310.06603	V2X-AHD:Vehicle-to-Everything Cooperation Perception via Asymmetric Heterogenous Distillation Network	Object detection is the central issue of intelligent traffic systems, and recent advancements in single-vehicle lidar-based 3D detection indicate that it can provide accurate position information for intelligent agents to make decisions and plan. Compared with single-vehicle perception, multi-view vehicle-road cooperat...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	398,645
2405.16833	Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models	While large language models (LLMs) such as Llama-2 or GPT-4 have shown impressive zero-shot performance, fine-tuning is still necessary to enhance their performance for customized datasets, domain-specific tasks, or other private needs. However, fine-tuning all parameters of LLMs requires significant hardware resources...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	457,630
1901.10629	A Convolutional Neural Network model based on Neutrosophy for Noisy Speech Recognition	Convolutional neural networks are sensitive to unknown noisy condition in the test phase and so their performance degrades for the noisy data classification task including noisy speech recognition. In this research, a new convolutional neural network (CNN) model with data uncertainty handling; referred as NCNN (Neutros...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,069
1602.04259	A Minimalistic Approach to Sum-Product Network Learning for Real Applications	Sum-Product Networks (SPNs) are a class of expressive yet tractable hierarchical graphical models. LearnSPN is a structure learning algorithm for SPNs that uses hierarchical co-clustering to simultaneously identifying similar entities and similar features. The original LearnSPN algorithm assumes that all the variables ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	52,097
2405.12742	Multi-Subject Personalization	Creative story illustration requires a consistent interplay of multiple characters or objects. However, conventional text-to-image models face significant challenges while producing images featuring multiple personalized subjects. For example, they distort the subject rendering, or the text descriptions fail to render ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	455,636
cs/9801102	Monotonicity and Persistence in Preferential Logics	An important characteristic of many logics for Artificial Intelligence is their nonmonotonicity. This means that adding a formula to the premises can invalidate some of the consequences. There may, however, exist formulae that can always be safely added to the premises without destroying any of the consequences: we say...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	540,375
2309.06612	Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained Devices	The recent surge of interest surrounding Multimodal Neural Networks (MM-NN) is attributed to their ability to effectively process and integrate multiscale information from diverse data sources. MM-NNs extract and fuse features from multiple modalities using adequate unimodal backbones and specific fusion networks. Alth...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	391,474
0801.3878	Hash Property and Coding Theorems for Sparse Matrices and Maximum-Likelihood Coding	The aim of this paper is to prove the achievability of several coding problems by using sparse matrices (the maximum column weight grows logarithmically in the block length) and maximal-likelihood (ML) coding. These problems are the Slepian-Wolf problem, the Gel'fand-Pinsker problem, the Wyner-Ziv problem, and the One-...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,207
2205.07949	Power and Skew Reduction Using Resonant Energy Recycling in 14-nm FinFET Clocks	As the demand for high-performance microprocessors increases, the circuit complexity and the rate of data transfer increases resulting in higher power consumption. We propose a clocking architecture that uses a series LC resonance and inductor matching technique to address this bottleneck. By employing pulsed resonance...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	296,770
2406.12835	Influence Maximization via Graph Neural Bandits	We consider a ubiquitous scenario in the study of Influence Maximization (IM), in which there is limited knowledge about the topology of the diffusion network. We set the IM problem in a multi-round diffusion campaign, aiming to maximize the number of distinct users that are influenced. Leveraging the capability of ban...	false	false	false	true	true	true	true	false	false	false	false	false	false	false	false	false	false	false	465,594
1907.10360	An Optimal Algorithm to Solve the Combined Task Allocation and Path Finding Problem	We consider multi-agent transport task problems where, e.g. in a factory setting, items have to be delivered from a given start to a goal pose while the delivering robots need to avoid collisions with each other on the floor. We introduce a Task Conflict-Based Search (TCBS) Algorithm to solve the combined delivery task...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	139,598
2302.09378	Modelos Generativos basados en Mecanismos de Difusi\'on	Diffusion-based generative models are a design framework that allows generating new images from processes analogous to those found in non-equilibrium thermodynamics. These models model the reversal of a physical diffusion process in which two miscible liquids of different colors progressively mix until they form a homo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	346,403
1811.01811	Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls	Machine learning has been applied to a broad range of applications and some of them are available online as application programming interfaces (APIs) with either free (trial) or paid subscriptions. In this paper, we study adversarial machine learning in the form of back-box attacks on online classifier APIs. We start w...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	112,451

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