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
2304.02942	InterFormer: Real-time Interactive Image Segmentation	Interactive image segmentation enables annotators to efficiently perform pixel-level annotation for segmentation tasks. However, the existing interactive segmentation pipeline suffers from inefficient computations of interactive models because of the following two issues. First, annotators' later click is based on mode...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	356,618
2012.07630	Decoupled Self Attention for Accurate One Stage Object Detection	As the scale of object detection dataset is smaller than that of image recognition dataset ImageNet, transfer learning has become a basic training method for deep learning object detection models, which will pretrain the backbone network of object detection model on ImageNet dataset to extract features for classificati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,531
2302.11993	xURLLC-Aware Service Provisioning in Vehicular Networks: A Semantic Communication Perspective	Semantic communication (SemCom), as an emerging paradigm focusing on meaning delivery, has recently been considered a promising solution for the inevitable crisis of scarce communication resources. This trend stimulates us to explore the potential of applying SemCom to wireless vehicular networks, which normally consum...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	347,395
1107.5469	A small world of citations? The influence of collaboration networks on citation practices	This paper examines the proximity of authors to those they cite using degrees of separation in a co-author network, essentially using collaboration networks to expand on the notion of self-citations. While the proportion of direct self-citations (including co-authors of both citing and cited papers) is relatively const...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	11,468
2110.07131	Reverse Maximum Inner Product Search: How to efficiently find users who would like to buy my item?	The MIPS (maximum inner product search), which finds the item with the highest inner product with a given query user, is an essential problem in the recommendation field. It is usual that e-commerce companies face situations where they want to promote and sell new or discounted items. In these situations, we have to co...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	260,872
2401.17766	Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospects	Recent zero-shot learning (ZSL) approaches have integrated fine-grained analysis, i.e., fine-grained ZSL, to mitigate the commonly known seen/unseen domain bias and misaligned visual-semantics mapping problems, and have made profound progress. Notably, this paradigm differs from existing close-set fine-grained methods ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	425,315
2003.03676	Towards Solving Large-scale Expensive Optimization Problems Efficiently Using Coordinate Descent Algorithm	Many real-world problems are categorized as large-scale problems, and metaheuristic algorithms as an alternative method to solve large-scale problem; they need the evaluation of many candidate solutions to tackle them prior to their convergence, which is not affordable for practical applications since the most of them ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	167,314
2003.03297	Active Model Estimation in Markov Decision Processes	We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision process (MDP). Efficient exploration in this problem requires the agent to identify the regions in which estimating the model is more difficult and then exploit this knowledge to collect mor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	167,186
2210.02661	Topological Continual Learning with Wasserstein Distance and Barycenter	Continual learning in neural networks suffers from a phenomenon called catastrophic forgetting, in which a network quickly forgets what was learned in a previous task. The human brain, however, is able to continually learn new tasks and accumulate knowledge throughout life. Neuroscience findings suggest that continual ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	321,733
2502.03417	From Features to Transformers: Redefining Ranking for Scalable Impact	We present LiGR, a large-scale ranking framework developed at LinkedIn that brings state-of-the-art transformer-based modeling architectures into production. We introduce a modified transformer architecture that incorporates learned normalization and simultaneous set-wise attention to user history and ranked items. Thi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,716
2109.10442	Selecting Datasets for Evaluating an Enhanced Deep Learning Framework	A framework was developed to address limitations associated with existing techniques for analysing sequences. This work deals with the steps followed to select suitable datasets characterised by discrete irregular sequential patterns. To identify, select, explore and evaluate which datasets from various sources extract...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,605
2410.00337	SyntheOcc: Synthesize Geometric-Controlled Street View Images through 3D Semantic MPIs	The advancement of autonomous driving is increasingly reliant on high-quality annotated datasets, especially in the task of 3D occupancy prediction, where the occupancy labels require dense 3D annotation with significant human effort. In this paper, we propose SyntheOcc, which denotes a diffusion model that Synthesize ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	493,324
1311.7466	Linear Network Error Correction Multicast/Broadcast/Dispersion/Generic Codes	In the practical network communications, many internal nodes in the network are required to not only transmit messages but decode source messages. For different applications, four important classes of linear network codes in network coding theory, i.e., linear multicast, linear broadcast, linear dispersion, and generic...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,738
2304.04597	Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits	Three-dimensional inspection of nanostructures such as integrated circuits is important for security and reliability assurance. Two scanning operations are required: ptychographic to recover the complex transmissivity of the specimen; and rotation of the specimen to acquire multiple projections covering the 3D spatial ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	357,280
2304.11359	Detecting Adversarial Faces Using Only Real Face Self-Perturbations	Adversarial attacks aim to disturb the functionality of a target system by adding specific noise to the input samples, bringing potential threats to security and robustness when applied to facial recognition systems. Although existing defense techniques achieve high accuracy in detecting some specific adversarial faces...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	359,781
2204.05311	Causal Discovery and Causal Learning for Fire Resistance Evaluation: Incorporating Domain Knowledge	Experiments remain the gold standard to establish an understanding of fire-related phenomena. A primary goal in designing tests is to uncover the data generating process (i.e., the how and why the observations we see come to be); or simply what causes such observations. Uncovering such a process not only advances our k...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	290,991
1609.01044	Classifying and sorting cluttered piles of unknown objects with robots: a learning approach	We consider the problem of sorting a densely cluttered pile of unknown objects using a robot. This yet unsolved problem is relevant in the robotic waste sorting business. By extending previous active learning approaches to grasping, we show a system that learns the task autonomously. Instead of predicting just whethe...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	60,551
2004.08549	A Survey of 6G Wireless Communications: Emerging Technologies	While fifth-generation (5G) communications are being rolled out around the world, sixth-generation (6G) communications have attracted much attention from both the industry and academia. Compared with 5G, 6G will have a wider frequency band, higher transmission rate, spectrum efficiency, greater connection capacity, sho...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	173,092
cs/0006003	Exploiting Diversity in Natural Language Processing: Combining Parsers	Three state-of-the-art statistical parsers are combined to produce more accurate parses, as well as new bounds on achievable Treebank parsing accuracy. Two general approaches are presented and two combination techniques are described for each approach. Both parametric and non-parametric models are explored. The resulti...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	537,118
2111.06457	Variability-Aware Training and Self-Tuning of Highly Quantized DNNs for Analog PIM	DNNs deployed on analog processing in memory (PIM) architectures are subject to fabrication-time variability. We developed a new joint variability- and quantization-aware DNN training algorithm for highly quantized analog PIM-based models that is significantly more effective than prior work. It outperforms variability-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	266,079
2404.01037	ARAGOG: Advanced RAG Output Grading	Retrieval-Augmented Generation (RAG) is essential for integrating external knowledge into Large Language Model (LLM) outputs. While the literature on RAG is growing, it primarily focuses on systematic reviews and comparisons of new state-of-the-art (SoTA) techniques against their predecessors, with a gap in extensive e...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	443,223
2006.15454	A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity Rewards	Cross-lingual text summarization aims at generating a document summary in one language given input in another language. It is a practically important but under-explored task, primarily due to the dearth of available data. Existing methods resort to machine translation to synthesize training data, but such pipeline appr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	184,508
1006.5066	Power Allocation Strategies across N Orthogonal Channels at Both Source and Relay	We consider a wireless relay network with one source, one relay and one destination, where communications between nodes are preformed via N orthogonal channels. This, for example, is the case when orthogonal frequency division multiplexing is employed for data communications. Since the power available at the source and...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,897
2412.15607	Short-Term Forecasting of Thermostatic and Residential Loads Using Long Short-Term Memory Recurrent Neural Networks	Internet of Things (IoT) devices in smart grids enable intelligent energy management for grid managers and personalized energy services for consumers. Investigating a smart grid with IoT devices requires a simulation framework with IoT devices modeling. However, there lack comprehensive study on the modeling of IoT dev...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	519,203
2412.15646	CustomTTT: Motion and Appearance Customized Video Generation via Test-Time Training	Benefiting from large-scale pre-training of text-video pairs, current text-to-video (T2V) diffusion models can generate high-quality videos from the text description. Besides, given some reference images or videos, the parameter-efficient fine-tuning method, i.e. LoRA, can generate high-quality customized concepts, e.g...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	519,217
1308.0365	Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes	In this paper we address the problem of multiple camera calibration in the presence of a homogeneous scene, and without the possibility of employing calibration object based methods. The proposed solution exploits salient features present in a larger field of view, but instead of employing active vision we replace the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	26,219
2107.10607	3D Shape Generation with Grid-based Implicit Functions	Previous approaches to generate shapes in a 3D setting train a GAN on the latent space of an autoencoder (AE). Even though this produces convincing results, it has two major shortcomings. As the GAN is limited to reproduce the dataset the AE was trained on, we cannot reuse a trained AE for novel data. Furthermore, it i...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	247,344
2303.12670	Correlational Image Modeling for Self-Supervised Visual Pre-Training	We introduce Correlational Image Modeling (CIM), a novel and surprisingly effective approach to self-supervised visual pre-training. Our CIM performs a simple pretext task: we randomly crop image regions (exemplars) from an input image (context) and predict correlation maps between the exemplars and the context. Three ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,334
2306.01914	On the Sample Complexity of Imitation Learning for Smoothed Model Predictive Control	Recent work in imitation learning has shown that having an expert controller that is both suitably smooth and stable enables stronger guarantees on the performance of the learned controller. However, constructing such smoothed expert controllers for arbitrary systems remains challenging, especially in the presence of i...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	370,668
1502.03296	Statistical laws in linguistics	Zipf's law is just one out of many universal laws proposed to describe statistical regularities in language. Here we review and critically discuss how these laws can be statistically interpreted, fitted, and tested (falsified). The modern availability of large databases of written text allows for tests with an unpreced...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	40,134
2303.08171	Resilient Dynamic Average Consensus based on Trusted agents	In this paper, we address the discrete-time dynamic average consensus (DAC) of a multi-agent system in the presence of adversarial attacks. The adversarial attack is considered to be of Byzantine type, which compromises the computation capabilities of the agent and sends arbitrary false data to its neighbours. We assum...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	351,535
2105.07566	Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification	The usage of smartphone-collected respiratory sound, trained with deep learning models, for detecting and classifying COVID-19 becomes popular recently. It removes the need for in-person testing procedures especially for rural regions where related medical supplies, experienced workers, and equipment are limited. Howev...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	235,474
2211.04134	Consistent Query Answering for Primary Keys and Conjunctive Queries with Counting	The problem of consistent query answering for primary keys and self-join-free conjunctive queries has been intensively studied in recent years and is by now well understood. In this paper, we study an extension of this problem with counting. The queries we consider count how many times each value occurs in a designated...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	329,146
2204.03758	Compositional Generalization and Decomposition in Neural Program Synthesis	When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neural program synthesis methods have similar capabilities, what we can measure is whether they compositionally generalize, that is, whether a m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	290,426
1302.1043	The price of bandit information in multiclass online classification	We consider two scenarios of multiclass online learning of a hypothesis class $H\subseteq Y^X$. In the {\em full information} scenario, the learner is exposed to instances together with their labels. In the {\em bandit} scenario, the true label is not exposed, but rather an indication whether the learner's prediction i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	21,776
2205.10864	Federated Learning Aggregation: New Robust Algorithms with Guarantees	Federated Learning has been recently proposed for distributed model training at the edge. The principle of this approach is to aggregate models learned on distributed clients to obtain a new more general "average" model (FedAvg). The resulting model is then redistributed to clients for further training. To date, the mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	297,905
1712.04046	Character-Based Handwritten Text Transcription with Attention Networks	The paper approaches the task of handwritten text recognition (HTR) with attentional encoder-decoder networks trained on sequences of characters, rather than words. We experiment on lines of text from popular handwriting datasets and compare different activation functions for the attention mechanism used for aligning i...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	86,535
1501.03952	Mind the Gap: Subspace based Hierarchical Domain Adaptation	Domain adaptation techniques aim at adapting a classifier learnt on a source domain to work on the target domain. Exploiting the subspaces spanned by features of the source and target domains respectively is one approach that has been investigated towards solving this problem. These techniques normally assume the exist...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	39,307
1704.04296	FastVentricle: Cardiac Segmentation with ENet	Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function. One disadvantage of CMR is that post-processing of exams is tedious. Without automation, precise assessment of cardiac function via CMR typically requires an annotator to spend tens of minutes per case manually contourin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	71,784
1303.5435	An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation	In a previous paper [Pearl and Verma, 1991] we presented an algorithm for extracting causal influences from independence information, where a causal influence was defined as the existence of a directed arc in all minimal causal models consistent with the data. In this paper we address the question of deciding whether t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,123
2312.06575	EasyVolcap: Accelerating Neural Volumetric Video Research	Volumetric video is a technology that digitally records dynamic events such as artistic performances, sporting events, and remote conversations. When acquired, such volumography can be viewed from any viewpoint and timestamp on flat screens, 3D displays, or VR headsets, enabling immersive viewing experiences and more f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,576
2010.05612	Cardiac Cohort Classification based on Morphologic and Hemodynamic Parameters extracted from 4D PC-MRI Data	An accurate assessment of the cardiovascular system and prediction of cardiovascular diseases (CVDs) are crucial. Measured cardiac blood flow data provide insights about patient-specific hemodynamics, where many specialized techniques have been developed for the visual exploration of such data sets to better understand...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	200,200
0812.1843	Identification of parameters underlying emotions and a classification of emotions	The standard classification of emotions involves categorizing the expression of emotions. In this paper, parameters underlying some emotions are identified and a new classification based on these parameters is suggested.	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	2,773
2005.09997	Learning Semantic Program Embeddings with Graph Interval Neural Network	Learning distributed representations of source code has been a challenging task for machine learning models. Earlier works treated programs as text so that natural language methods can be readily applied. Unfortunately, such approaches do not capitalize on the rich structural information possessed by source code. Of la...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	178,055
2007.02924	INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving	In learning-assisted theorem proving, one of the most critical challenges is to generalize to theorems unlike those seen at training time. In this paper, we introduce INT, an INequality Theorem proving benchmark, specifically designed to test agents' generalization ability. INT is based on a procedure for generating th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	185,903
2401.05215	Pre-trained Large Language Models for Financial Sentiment Analysis	Financial sentiment analysis refers to classifying financial text contents into sentiment categories (e.g. positive, negative, and neutral). In this paper, we focus on the classification of financial news title, which is a challenging task due to a lack of large amount of training samples. To overcome this difficulty, ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	420,682
1911.00361	Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers	Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers. Recent emerged quantization technique has been applied to inference of deep neural networks for fast and efficient execution. However, directly applying quantization in training can cause significant accuracy loss, thus ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	151,801
2410.06376	Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees	The problem of determining the configuration of points from partial distance information, known as the Euclidean Distance Geometry (EDG) problem, is fundamental to many tasks in the applied sciences. In this paper, we propose two algorithms grounded in the Riemannian optimization framework to address the EDG problem. O...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	496,171
2103.00087	CXR-Net: An Artificial Intelligence Pipeline for Quick Covid-19 Screening of Chest X-Rays	CXR-Net is a two-module Artificial Intelligence pipeline for the quick detection of SARS-CoV-2 from chest X-rays (CXRs). Module 1 was trained on a public dataset of 6395 CXRs with radiologist annotated lung contours to generate masks of the lungs that overlap the heart and large vasa. Module 2 is a hybrid convnet in wh...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	222,142
2310.04074	Automatic Aspect Extraction from Scientific Texts	Being able to extract from scientific papers their main points, key insights, and other important information, referred to here as aspects, might facilitate the process of conducting a scientific literature review. Therefore, the aim of our research is to create a tool for automatic aspect extraction from Russian-langu...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	397,528
2011.08449	Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks	Vehicular Edge Computing (VEC) is a promising paradigm to enable huge amount of data and multimedia content to be cached in proximity to vehicles. However, high mobility of vehicles and dynamic wireless channel condition make it challenge to design an optimal content caching policy. Further, with much sensitive persona...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	206,876
2206.07780	A machine learning approach to predicting pore pressure response in liquefiable sands under cyclic loading	Shear stress history controls the pore pressure response in liquefiable soils. The excess pore pressure does not increase under cyclic loading when shear stress amplitude is lower than the peak prior amplitude -- the shielding effect. Many sophisticated constitutive models fail to capture the shielding effect observed ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	302,881
2405.18536	Data-Driven Simulator for Mechanical Circulatory Support with Domain Adversarial Neural Process	Mechanical Circulatory Support (MCS) devices, implemented as a probabilistic deep sequence model. Existing mechanical simulators for MCS rely on oversimplifying assumptions and are insensitive to patient-specific behavior, limiting their applicability to real-world treatment scenarios. To address these shortcomings, ou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	458,463
2403.15780	A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services	As Machine Learning grows in popularity across various fields, equity has become a key focus for the AI community. However, fairness-oriented approaches are still underexplored in smart mobility. Addressing this gap, our study investigates the balance between performance optimization and algorithmic fairness in shared ...	false	false	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	440,744
1304.2340	Summary of A New Normative Theory of Probabilistic Logic	By probabilistic logic I mean a normative theory of belief that explains how a body of evidence affects one's degree of belief in a possible hypothesis. A new axiomatization of such a theory is presented which avoids a finite additivity axiom, yet which retains many useful inference rules. Many of the examples of this ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,648
1511.08913	Sliding-Window Optimization on an Ambiguity-Clearness Graph for Multi-object Tracking	Multi-object tracking remains challenging due to frequent occurrence of occlusions and outliers. In order to handle this problem, we propose an Approximation-Shrink Scheme for sequential optimization. This scheme is realized by introducing an Ambiguity-Clearness Graph to avoid conflicts and maintain sequence independen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	49,595
2206.04359	Learning Non-Vacuous Generalization Bounds from Optimization	One of the fundamental challenges in the deep learning community is to theoretically understand how well a deep neural network generalizes to unseen data. However, current approaches often yield generalization bounds that are either too loose to be informative of the true generalization error or only valid to the compr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	301,596
2207.06883	RF-Photonic Deep Learning Processor with Shannon-Limited Data Movement	Edholm's Law predicts exponential growth in data rate and spectrum bandwidth for communications and is forecasted to remain true for the upcoming deployment of 6G. Compounding this issue is the exponentially increasing demand for deep neural network (DNN) compute, including DNNs for signal processing. However, the slow...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	308,026
0811.1260	The Application of Fuzzy Logic to Collocation Extraction	Collocations are important for many tasks of Natural language processing such as information retrieval, machine translation, computational lexicography etc. So far many statistical methods have been used for collocation extraction. Almost all the methods form a classical crisp set of collocation. We propose a fuzzy log...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	2,649
2107.08622	Provably Efficient Multi-Task Reinforcement Learning with Model Transfer	We study multi-task reinforcement learning (RL) in tabular episodic Markov decision processes (MDPs). We formulate a heterogeneous multi-player RL problem, in which a group of players concurrently face similar but not necessarily identical MDPs, with a goal of improving their collective performance through inter-player...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	246,787
1903.12287	PyTorch-BigGraph: A Large-scale Graph Embedding System	Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks. Modern graphs, particularly in industrial applications, contain billions of nodes and trillions of edges, which exceeds the capability of existing embedding systems. We present PyTorch-B...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	true	125,681
2001.09186	A tutorial on the range variant of asymmetric numeral systems	This paper is intended to be a brief and accessible introduction to the range variant of asymmetric numeral systems (ANS), a system for lossless compression of sequences which can be used as a drop in replacement for arithmetic coding (AC). Because of the relative simplicity of ANS, we are able to provide enough mathem...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	161,493
1906.07328	Losing Confidence in Quality: Unspoken Evolution of Computer Vision Services	Recent advances in artificial intelligence (AI) and machine learning (ML), such as computer vision, are now available as intelligent services and their accessibility and simplicity is compelling. Multiple vendors now offer this technology as cloud services and developers want to leverage these advances to provide value...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	135,566
2501.15014	On Accelerating Edge AI: Optimizing Resource-Constrained Environments	Resource-constrained edge deployments demand AI solutions that balance high performance with stringent compute, memory, and energy limitations. In this survey, we present a comprehensive overview of the primary strategies for accelerating deep learning models under such constraints. First, we examine model compression ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	527,360
2002.05259	Learning to Generate Levels From Nothing	Machine learning for procedural content generation has recently become an active area of research. Levels vary in both form and function and are mostly unrelated to each other across games. This has made it difficult to assemble suitably large datasets to bring machine learning to level design in the same way as it's b...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	163,840
2204.10792	Equivalence of Decentralized Observation, Diagnosis, and Control Problems in Discrete-event Systems	This paper demonstrates an equivalence between observation problems, control problems (with partial observation), and diagnosis problems of decentralized discrete-event systems, namely, the three classes of problems are Turing equivalent, as one class Turing reduces to another. The equivalence allows decomposition of...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	292,917
1606.03391	Simple Question Answering by Attentive Convolutional Neural Network	This work focuses on answering single-relation factoid questions over Freebase. Each question can acquire the answer from a single fact of form (subject, predicate, object) in Freebase. This task, simple question answering (SimpleQA), can be addressed via a two-step pipeline: entity linking and fact selection. In fact ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	57,092
2305.09559	Robust and lightweight audio fingerprint for Automatic Content Recognition	This research paper presents a novel audio fingerprinting system for Automatic Content Recognition (ACR). By using signal processing techniques and statistical transformations, our proposed method generates compact fingerprints of audio segments that are robust to noise degradations present in real-world audio. The sys...	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	364,685
2109.06798	Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction	Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of pretrained multilingual encoders suggests an easy optimism of "train on English, run on any langu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	255,282
1904.01869	Securing State Estimation Under Sensor and Actuator Attacks: Theory and Design	This paper discusses the problem of estimating the state of a linear time-invariant system when some of its sensors and actuators are compromised by an adversarial agent. In the model considered in this paper, the malicious agent attacks an input (output) by manipulating its value arbitrarily, i.e., we impose no constr...	false	false	false	false	false	false	false	false	false	true	true	false	true	false	false	false	false	false	126,271
1810.01943	AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias	Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and prison sentencing. This paper introduces a new open source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an A...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	109,497
2405.04435	Fast Exact Retrieval for Nearest-neighbor Lookup (FERN)	Exact nearest neighbor search is a computationally intensive process, and even its simpler sibling -- vector retrieval -- can be computationally complex. This is exacerbated when retrieving vectors which have high-dimension $d$ relative to the number of vectors, $N$, in the database. Exact nearest neighbor retrieval ha...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	452,571
2107.00797	Mitigating deep double descent by concatenating inputs	The double descent curve is one of the most intriguing properties of deep neural networks. It contrasts the classical bias-variance curve with the behavior of modern neural networks, occurring where the number of samples nears the number of parameters. In this work, we explore the connection between the double descent ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,272
1004.1997	An optimized recursive learning algorithm for three-layer feedforward neural networks for mimo nonlinear system identifications	Back-propagation with gradient method is the most popular learning algorithm for feed-forward neural networks. However, it is critical to determine a proper fixed learning rate for the algorithm. In this paper, an optimized recursive algorithm is presented for online learning based on matrix operation and optimization ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	6,146
2005.01928	Modal features for image texture classification	Feature extraction is a key step in image processing for pattern recognition and machine learning processes. Its purpose lies in reducing the dimensionality of the input data through the computing of features which accurately describe the original information. In this article, a new feature extraction method based on D...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	175,712
2412.09149	Student-Informed Teacher Training	Imitation learning with a privileged teacher has proven effective for learning complex control behaviors from high-dimensional inputs, such as images. In this framework, a teacher is trained with privileged task information, while a student tries to predict the actions of the teacher with more limited observations, e.g...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	516,378
2303.02242	TrojText: Test-time Invisible Textual Trojan Insertion	In Natural Language Processing (NLP), intelligent neuron models can be susceptible to textual Trojan attacks. Such attacks occur when Trojan models behave normally for standard inputs but generate malicious output for inputs that contain a specific trigger. Syntactic-structure triggers, which are invisible, are becomin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	349,268
1811.09930	A multi-dimensional extension of the Lightweight Temporal Compression method	Lightweight Temporal Compression (LTC) is among the lossy stream compression methods that provide the highest compression rate for the lowest CPU and memory consumption. As such, it is well suited to compress data streams in energy-constrained systems such as connected objects. The current formulation of LTC, however, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	114,365
2110.01518	Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics	Much of recent progress in NLU was shown to be due to models' learning dataset-specific heuristics. We conduct a case study of generalization in NLI (from MNLI to the adversarially constructed HANS dataset) in a range of BERT-based architectures (adapters, Siamese Transformers, HEX debiasing), as well as with subsampli...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	258,796
2410.23862	$\psi$DAG: Projected Stochastic Approximation Iteration for DAG Structure Learning	Learning the structure of Directed Acyclic Graphs (DAGs) presents a significant challenge due to the vast combinatorial search space of possible graphs, which scales exponentially with the number of nodes. Recent advancements have redefined this problem as a continuous optimization task by incorporating differentiable ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	504,222
2412.19878	YOLO-MST: Multiscale deep learning method for infrared small target detection based on super-resolution and YOLO	With the advancement of aerospace technology and the increasing demands of military applications, the development of low false-alarm and high-precision infrared small target detection algorithms has emerged as a key focus of research globally. However, the traditional model-driven method is not robust enough when deali...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	521,012
2006.07521	A Blockchain-based Decentralized Data Sharing Infrastructure for Off-grid Networking	Off-grid networks are recently emerging as a solution to connect the unconnected or provide alternative services to networks of possibly untrusted participants. The systems currently used, however, exhibit limitations due to their centralized nature and thus prove inadequate to secure trust. Blockchain technology can b...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	181,831
1906.09486	Protecting shared information in networks: a network security game with strategic attacks	A digital security breach, by which confidential information is leaked, does not only affect the agent whose system is infiltrated, but is also detrimental to other agents socially connected to the infiltrated system. Although it has been argued that these externalities create incentives to under-invest in security, th...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	136,174
2408.16650	Towards Efficient Modelling of String Dynamics: A Comparison of State Space and Koopman based Deep Learning Methods	This paper presents an examination of State Space Models (SSM) and Koopman-based deep learning methods for modelling the dynamics of both linear and non-linear stiff strings. Through experiments with datasets generated under different initial conditions and sample rates, we assess the capacity of these models to accura...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	484,406
2107.07642	Improving application performance with biased distributions of quantum states	We consider the properties of a specific distribution of mixed quantum states of arbitrary dimension that can be biased towards a specific mean purity. In particular, we analyze mixtures of Haar-random pure states with Dirichlet-distributed coefficients. We analytically derive the concentration parameters required to m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	246,478
2308.16906	Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator	In this paper, we introduce a novel approach to fine-grained cross-view geo-localization. Our method aligns a warped ground image with a corresponding GPS-tagged satellite image covering the same area using homography estimation. We first employ a differentiable spherical transform, adhering to geometric principles, to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	389,161
2006.16840	Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization	This paper presents a framework of successive functional gradient optimization for training nonconvex models such as neural networks, where training is driven by mirror descent in a function space. We provide a theoretical analysis and empirical study of the training method derived from this framework. It is shown that...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	184,924
2001.07194	Recommending Themes for Ad Creative Design via Visual-Linguistic Representations	There is a perennial need in the online advertising industry to refresh ad creatives, i.e., images and text used for enticing online users towards a brand. Such refreshes are required to reduce the likelihood of ad fatigue among online users, and to incorporate insights from other successful campaigns in related produc...	false	false	false	false	false	true	true	false	true	false	false	true	false	false	false	false	false	true	160,986
2302.09920	Two-Tier Multi-Rate Slotted ALOHA for OWC/RF-Based IoT Networks	We consider a massive Internet of Things (IoT) scenario where indoor IoT devices access the network via optical wireless communication (OWC) IoT systems that relay data via a backhaul radio frequency (RF) low-power wide-area network (LP WAN). We propose a novel two-tier multi-rate Slotted ALOHA (SA) system model to des...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	346,622
2307.01940	An Adaptive Overcurrent Protection for Solar-based DC Microgrids Using IEC 61850	Over-Current (OC) protection is one of the pervasive protections in solar-based DC microgrids. Fast operation is a key advantage of its popularity. On the other hand, utilizing OC in DC microgrids has some challenges that are not in AC grids. Some of these challenges are related to the grounding approach of the DC micr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	377,520
1906.00158	Patch Learning	There have been different strategies to improve the performance of a machine learning model, e.g., increasing the depth, width, and/or nonlinearity of the model, and using ensemble learning to aggregate multiple base/weak learners in parallel or in series. This paper proposes a novel strategy called patch learning (PL)...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,287
1604.04377	DARI: Distance metric And Representation Integration for Person Verification	The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately. To explore their interaction, this work proposes an end-to-end learning framework called DARI, i.e. Distance metric And Representation Integration, a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	54,636
0805.3217	Statistical region-based active contours with exponential family observations	In this paper, we focus on statistical region-based active contour models where image features (e.g. intensity) are random variables whose distribution belongs to some parametric family (e.g. exponential) rather than confining ourselves to the special Gaussian case. Using shape derivation tools, our effort focuses on c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	1,800
2201.12632	Towards Robust Deep Active Learning for Scientific Computing	Deep learning (DL) is revolutionizing the scientific computing community. To reduce the data gap, active learning has been identified as a promising solution for DL in the scientific computing community. However, the deep active learning (DAL) literature is dominated by image classification problems and pool-based meth...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	277,716
2412.11185	Transliterated Zero-Shot Domain Adaptation for Automatic Speech Recognition	The performance of automatic speech recognition models often degenerates on domains not covered by the training data. Domain adaptation can address this issue, assuming the availability of the target domain data in the target language. However, such assumption does not stand in many real-world applications. To make dom...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	517,300
2010.13900	Incorporating Symbolic Domain Knowledge into Graph Neural Networks	Our interest is in scientific problems with the following characteristics: (1) Data are naturally represented as graphs; (2) The amount of data available is typically small; and (3) There is significant domain-knowledge, usually expressed in some symbolic form. These kinds of problems have been addressed effectively in...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	203,279
2308.01053	Boundary integrated neural networks (BINNs) for 2D elastostatic and piezoelectric problems: Theory and MATLAB code	In this paper, we make the first attempt to apply the boundary integrated neural networks (BINNs) for the numerical solution of two-dimensional (2D) elastostatic and piezoelectric problems. BINNs combine artificial neural networks with the well-established boundary integral equations (BIEs) to effectively solve partial...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	383,125
1603.06668	Learning Representations for Automatic Colorization	We develop a fully automatic image colorization system. Our approach leverages recent advances in deep networks, exploiting both low-level and semantic representations. As many scene elements naturally appear according to multimodal color distributions, we train our model to predict per-pixel color histograms. This int...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	53,524
2501.13993	CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation	The introduction of new features and services in the banking sector often overwhelms customers, creating an opportunity for banks to enhance user experience through financial chatbots powered by large language models (LLMs). We initiated an AI agent designed to provide customers with relevant information about banking ...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	526,935
1707.05534	Latent Gaussian Process Regression	We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs. The approach is built on extending the input space of a regression problem with a latent variable that is used to modulate the covariance function over the training ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	77,253

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