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
2007.10712	On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic	The COVID-19 pandemic has developed to be more than a bio-crisis as global news has reported a sharp rise in xenophobia and discrimination in both online and offline communities. Such toxic behaviors take a heavy toll on society, especially during these daunting times. Despite the gravity of the issue, very few studies...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	188,349
2012.02998	Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes	The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weath...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,944
1905.02463	Adaptive Generation of Unrestricted Adversarial Inputs	Neural networks are vulnerable to adversarially-constructed perturbations of their inputs. Most research so far has considered perturbations of a fixed magnitude under some $l_p$ norm. Although studying these attacks is valuable, there has been increasing interest in the construction of (and robustness to) unrestricted...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	129,980
1410.7922	Extended Dynamic Programming and Fast Multidimensional Search Algorithm for Energy Minization in Stereo and Motion	This paper presents a novel extended dynamic programming approach for energy minimization (EDP) to solve the correspondence problem for stereo and motion. A significant speedup is achieved using a recursive minimum search strategy (RMS). The mentioned speedup is particularly important if the disparity space is 2D as we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	37,117
2107.06217	What classifiers know what they don't?	Being uncertain when facing the unknown is key to intelligent decision making. However, machine learning algorithms lack reliable estimates about their predictive uncertainty. This leads to wrong and overly-confident decisions when encountering classes unseen during training. Despite the importance of equipping classif...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	246,027
1908.07448	Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing	We present an extensive evaluation of three recently proposed methods for contextualized embeddings on 89 corpora in 54 languages of the Universal Dependencies 2.3 in three tasks: POS tagging, lemmatization, and dependency parsing. Employing the BERT, Flair and ELMo as pretrained embedding inputs in a strong baseline o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	142,298
2101.00784	WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19	The COVID-19 epidemic has been a significant healthcare challenge in the United States. According to the Centers for Disease Control and Prevention (CDC), COVID-19 infection is transmitted predominately by respiratory droplets generated when people breathe, talk, cough, or sneeze. Wearing a mask is the primary, effecti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	214,202
2104.07551	HIVE-COTE 2.0: a new meta ensemble for time series classification	The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from classifiers of multiple domains, including phase-independent shapelets, bag-of-words based dictionaries and phase-dependent intervals. Since i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	230,461
2404.00798	On Difficulties of Attention Factorization through Shared Memory	Transformers have revolutionized deep learning in numerous fields, including natural language processing, computer vision, and audio processing. Their strength lies in their attention mechanism, which allows for the discovering of complex input relationships. However, this mechanism's quadratic time and memory complexi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	443,108
1903.05994	Can Adversarial Network Attack be Defended?	Machine learning has been successfully applied to complex network analysis in various areas, and graph neural networks (GNNs) based methods outperform others. Recently, adversarial attack on networks has attracted special attention since carefully crafted adversarial networks with slight perturbations on clean network ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	124,283
1912.02292	Deep Double Descent: Where Bigger Models and More Data Hurt	We show that a variety of modern deep learning tasks exhibit a "double-descent" phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs. We u...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	156,308
2007.05113	FC2RN: A Fully Convolutional Corner Refinement Network for Accurate Multi-Oriented Scene Text Detection	Recent scene text detection works mainly focus on curve text detection. However, in real applications, the curve texts are more scarce than the multi-oriented ones. Accurate detection of multi-oriented text with large variations of scales, orientations, and aspect ratios is of great significance. Among the multi-orient...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	186,571
2006.00070	Refined Reliability Combining for Binary Message Passing Decoding of Product Codes	We propose a novel soft-aided iterative decoding algorithm for product codes (PCs). The proposed algorithm, named iterative bounded distance decoding with combined reliability (iBDD-CR), enhances the conventional iterative bounded distance decoding (iBDD) of PCs by exploiting some level of soft information. In particul...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	179,357
2304.14094	Categorical Foundations of Explainable AI: A Unifying Theory	Explainable AI (XAI) aims to address the human need for safe and reliable AI systems. However, numerous surveys emphasize the absence of a sound mathematical formalization of key XAI notions -- remarkably including the term "explanation" which still lacks a precise definition. To bridge this gap, this paper presents th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	360,823
2310.19914	Efficient entanglement purification based on noise guessing decoding	In this paper, we propose a novel bipartite entanglement purification protocol built upon hashing and upon the guessing random additive noise decoding (GRAND) approach recently devised for classical error correction codes. Our protocol offers substantial advantages over existing hashing protocols, requiring fewer qubit...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	404,181
1904.01814	Deep Neural Networks for Rotation-Invariance Approximation and Learning	Based on the tree architecture, the objective of this paper is to design deep neural networks with two or more hidden layers (called deep nets) for realization of radial functions so as to enable rotational invariance for near-optimal function approximation in an arbitrarily high dimensional Euclidian space. It is show...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	126,259
2005.10019	Every Colour You Are: Stance Prediction and Turnaround in Controversial Issues	Web platforms have allowed political manifestation and debate for decades. Technology changes have brought new opportunities for expression, and the availability of longitudinal data of these debates entice new questions regarding who participates, and who updates their opinion. The aim of this work is to provide a met...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	178,066
2206.11939	Measuring Representational Robustness of Neural Networks Through Shared Invariances	A major challenge in studying robustness in deep learning is defining the set of ``meaningless'' perturbations to which a given Neural Network (NN) should be invariant. Most work on robustness implicitly uses a human as the reference model to define such perturbations. Our work offers a new view on robustness by using ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	304,422
1410.0507	Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules	Generating functionals may guide the evolution of a dynamical system and constitute a possible route for handling the complexity of neural networks as relevant for computational intelligence. We propose and explore a new objective function, which allows to obtain plasticity rules for the afferent synaptic weights. The ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	36,476
1712.05927	SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution	Single image super resolution (SISR) is to reconstruct a high resolution image from a single low resolution image. The SISR task has been a very attractive research topic over the last two decades. In recent years, convolutional neural network (CNN) based models have achieved great performance on SISR task. Despite the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	86,796
1709.09723	Estimating a Separably-Markov Random Field (SMuRF) from Binary Observations	A fundamental problem in neuroscience is to characterize the dynamics of spiking from the neurons in a circuit that is involved in learning about a stimulus or a contingency. A key limitation of current methods to analyze neural spiking data is the need to collapse neural activity over time or trials, which may cause t...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	81,673
2404.15328	Time topological analysis of EEG using signature theory	Anomaly detection in multivariate signals is a task of paramount importance in many disciplines (epidemiology, finance, cognitive sciences and neurosciences, oncology, etc.). In this perspective, Topological Data Analysis (TDA) offers a battery of "shape" invariants that can be exploited for the implementation of an ef...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	449,047
1911.11552	Network Intrusion Detection based on LSTM and Feature Embedding	Growing number of network devices and services have led to increasing demand for protective measures as hackers launch attacks to paralyze or steal information from victim systems. Intrusion Detection System (IDS) is one of the essential elements of network perimeter security which detects the attacks by inspecting net...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	155,161
1304.3098	Evidential Reasoning in Parallel Hierarchical Vision Programs	This paper presents an efficient adaptation and application of the Dempster-Shafer theory of evidence, one that can be used effectively in a massively parallel hierarchical system for visual pattern perception. It describes the techniques used, and shows in an extended example how they serve to improve the system's per...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	23,814
2212.04606	The R-algebra of Quasiknowledge and Convex Optimization	This article develops a convex description of a classical or quantum learner's or agent's state of knowledge about its environment, presented as a convex subset of a commutative R-algebra. With caveats, this leads to a generalization of certain semidefinite programs in quantum information (such as those describing the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	335,500
2302.11705	ACE: Zero-Shot Image to Image Translation via Pretrained Auto-Contrastive-Encoder	Image-to-image translation is a fundamental task in computer vision. It transforms images from one domain to images in another domain so that they have particular domain-specific characteristics. Most prior works train a generative model to learn the mapping from a source domain to a target domain. However, learning su...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	347,284
2102.00668	Graphs of Joint Types, Noninteractive Simulation, and Stronger Hypercontractivity	In this paper, we study the type graph, namely, a bipartite graph induced by a joint type. We investigate the maximum edge density of induced bipartite subgraphs of this graph having a number of vertices on each side on an exponential scale in the length $n$ of the type. This can be seen as an isoperimetric problem. We...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	217,872
2008.07524	Reinforcement Learning with Quantum Variational Circuits	The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potenti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	192,136
2409.19471	SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models	Despite significant advancements in large language models (LLMs) that enhance robot agents' understanding and execution of natural language (NL) commands, ensuring the agents adhere to user-specified constraints remains challenging, particularly for complex commands and long-horizon tasks. To address this challenge, we...	false	false	false	false	true	false	false	true	true	false	false	false	false	false	false	false	false	true	492,696
2409.06727	Data-driven methods for computational mechanics: A fair comparison between neural networks based and model-free approaches	We present a comparison between two approaches to modelling hyperelastic material behaviour using data. The first approach is a novel approach based on Data-driven Computational Mechanics (DDCM) that completely bypasses the definition of a material model by using only data from simulations or real-life experiments to p...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	487,252
2311.06602	BizBench: A Quantitative Reasoning Benchmark for Business and Finance	Answering questions within business and finance requires reasoning, precision, and a wide-breadth of technical knowledge. Together, these requirements make this domain difficult for large language models (LLMs). We introduce BizBench, a benchmark for evaluating models' ability to reason about realistic financial proble...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	407,002
1407.3068	Deep Networks with Internal Selective Attention through Feedback Connections	Traditional convolutional neural networks (CNN) are stationary and feedforward. They neither change their parameters during evaluation nor use feedback from higher to lower layers. Real brains, however, do. So does our Deep Attention Selective Network (dasNet) architecture. DasNets feedback structure can dynamically al...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	34,593
2211.10265	Context Variance Evaluation of Pretrained Language Models for Prompt-based Biomedical Knowledge Probing	Pretrained language models (PLMs) have motivated research on what kinds of knowledge these models learn. Fill-in-the-blanks problem (e.g., cloze tests) is a natural approach for gauging such knowledge. BioLAMA generates prompts for biomedical factual knowledge triples and uses the Top-k accuracy metric to evaluate diff...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	331,253
2205.13124	PixelGame: Infrared small target segmentation as a Nash equilibrium	A key challenge of infrared small target segmentation (ISTS) is to balance false negative pixels (FNs) and false positive pixels (FPs). Traditional methods combine FNs and FPs into a single objective by weighted sum, and the optimization process is decided by one actor. Minimizing FNs and FPs with the same strategy lea...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	298,816
1808.10379	Asymptotic analysis of the Friedkin-Johnsen model when the matrix of the susceptibility weights approaches the identity matrix	In this paper we analyze the Friedkin-Johnsen model of opinions when the coefficients weighting the agent susceptibilities to interpersonal influence approach 1. We will show that in this case, under suitable assumptions, the model converges to a quasi-consensus condition among the agents. In general the achieved conse...	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	false	false	false	106,371
1610.00017	On Optimal Latency of Communications	In this paper we investigate the optimal latency of communications. Focusing on fixed rate communication without any feedback channel, this paper encompasses low-latency strategies with which one hop and multi-hop communication issues are treated from an information theoretic perspective. By defining the latency as the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	61,779
1606.01314	Optimal Storage Allocation for Wireless Cloud Caching Systems with a Limited Sum Storage Capacity	In wireless cloud storage systems, the recovery failure probability depends on not only wireless channel conditions but also storage size of each distributed storage node. For an efficient utilization of limited storage capacity and the performance characterization of allocation strategies, we asymptotically analyze th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	56,790
2104.11549	Antenna Efficiency in Massive MIMO Detection	In this paper, we consider the multi-user detection problem in a multiple-input multiple-output (MIMO) system, where the number of receive antennas at the base station (BS) grows infinitely large. We propose a new performance metric, called antenna efficiency, to characterize how fast the vector error probability (VEP)...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	231,944
2409.01382	Automatic Detection of LLM-generated Code: A Case Study of Claude 3 Haiku	Using Large Language Models (LLMs) has gained popularity among software developers for generating source code. However, the use of LLM-generated code can introduce risks of adding suboptimal, defective, and vulnerable code. This makes it necessary to devise methods for the accurate detection of LLM-generated code. Towa...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	485,315
2009.08633	fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP	We present fastHan, an open-source toolkit for four basic tasks in Chinese natural language processing: Chinese word segmentation (CWS), Part-of-Speech (POS) tagging, named entity recognition (NER), and dependency parsing. The backbone of fastHan is a multi-task model based on a pruned BERT, which uses the first 8 laye...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	196,300
2002.12507	Decentralized Federated Learning via SGD over Wireless D2D Networks	Federated Learning (FL), an emerging paradigm for fast intelligent acquisition at the network edge, enables joint training of a machine learning model over distributed data sets and computing resources with limited disclosure of local data. Communication is a critical enabler of large-scale FL due to significant amount...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	166,056
2401.07543	Must: Maximizing Latent Capacity of Spatial Transcriptomics Data	Spatial transcriptomics (ST) technologies have revolutionized the study of gene expression patterns in tissues by providing multimodality data in transcriptomic, spatial, and morphological, offering opportunities for understanding tissue biology beyond transcriptomics. However, we identify the modality bias phenomenon ...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	421,584
1611.05181	Graph Learning from Data under Structural and Laplacian Constraints	Graphs are fundamental mathematical structures used in various fields to represent data, signals and processes. In this paper, we propose a novel framework for learning/estimating graphs from data. The proposed framework includes (i) formulation of various graph learning problems, (ii) their probabilistic interpretatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	63,969
2203.14085	Near-Infrared Depth-Independent Image Dehazing using Haar Wavelets	We propose a fusion algorithm for haze removal that combines color information from an RGB image and edge information extracted from its corresponding NIR image using Haar wavelets. The proposed algorithm is based on the key observation that NIR edge features are more prominent in the hazy regions of the image than the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	287,871
1805.10702	Study of Unique-Word Based GFDM Transmission Systems	In this paper, we propose the use of a deterministic sequence, known as unique word (UW), instead of the cyclic prefix (CP) in generalized frequency division multiplexing (GFDM) systems. The UW consists of known sequences that, if not null, can be used advantageously for synchronization and channel estimation purposes....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	98,746
1309.7430	Pilot Beam Pattern Design for Channel Estimation in Massive MIMO Systems	In this paper, the problem of pilot beam pattern design for channel estimation in massive multiple-input multiple-output systems with a large number of transmit antennas at the base station is considered, and a new algorithm for pilot beam pattern design for optimal channel estimation is proposed under the assumption t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	27,371
2401.11881	Modelling the Dynamics of Identity and Fairness in Ultimatum Game	Allocation games are zero-sum games that model the distribution of resources among multiple agents. In this paper, we explore the interplay between an \textit{subjective identity} and its impact on notions of fairness in allocation. The sense of identity in agents is known to lead to responsible decision-making in non-...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	423,190
2102.10457	Development of the first Portuguese radar tracking sensor for Space Debris	Currently, space debris represents a threat for satellites and space-based operations, both in-orbit and during the launching process. The yearly increase in space debris represents a serious concern to major space agencies leading to the development of dedicated space programs to deal with this issue. Ground-based rad...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	221,099
2308.02226	Learning to Paraphrase Sentences to Different Complexity Levels	While sentence simplification is an active research topic in NLP, its adjacent tasks of sentence complexification and same-level paraphrasing are not. To train models on all three tasks, we present two new unsupervised datasets. We compare these datasets, one labeled by a weak classifier and the other by a rule-based a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	383,545
2411.18369	G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation	Recent advances in imitation learning for 3D robotic manipulation have shown promising results with diffusion-based policies. However, achieving human-level dexterity requires seamless integration of geometric precision and semantic understanding. We present G3Flow, a novel framework that constructs real-time semantic ...	false	false	false	false	true	false	false	true	false	false	true	true	false	false	false	false	false	false	511,844
2003.05438	Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning	The recently advanced unsupervised learning approaches use the siamese-like framework to compare two "views" from the same image for learning representations. Making the two views distinctive is a core to guarantee that unsupervised methods can learn meaningful information. However, such frameworks are sometimes fragil...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	167,856
2310.18382	From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks	Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making. By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society, e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	403,522
2201.04387	Maximizing Self-supervision from Thermal Image for Effective Self-supervised Learning of Depth and Ego-motion	Recently, self-supervised learning of depth and ego-motion from thermal images shows strong robustness and reliability under challenging scenarios. However, the inherent thermal image properties such as weak contrast, blurry edges, and noise hinder to generate effective self-supervision from thermal images. Therefore, ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	275,087
2312.15246	A Theory of Non-Acyclic Generative Flow Networks	GFlowNets is a novel flow-based method for learning a stochastic policy to generate objects via a sequence of actions and with probability proportional to a given positive reward. We contribute to relaxing hypotheses limiting the application range of GFlowNets, in particular: acyclicity (or lack thereof). To this end, ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	417,939
2110.03900	Neural Strokes: Stylized Line Drawing of 3D Shapes	This paper introduces a model for producing stylized line drawings from 3D shapes. The model takes a 3D shape and a viewpoint as input, and outputs a drawing with textured strokes, with variations in stroke thickness, deformation, and color learned from an artist's style. The model is fully differentiable. We train its...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	259,673
1909.08297	Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition	Recently, infrared human action recognition has attracted increasing attention for it has many advantages over visible light, that is, being robust to illumination change and shadows. However, the infrared action data is limited until now, which degrades the performance of infrared action recognition. Motivated by the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	145,949
2209.14937	NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer	Classical machine learning models such as deep neural networks are usually trained by using Stochastic Gradient Descent-based (SGD) algorithms. The classical SGD can be interpreted as a discretization of the stochastic gradient flow. In this paper we propose a novel, robust and accelerated stochastic optimizer that rel...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	320,404
2304.03843	Why think step by step? Reasoning emerges from the locality of experience	Humans have a powerful and mysterious capacity to reason. Working through a set of mental steps enables us to make inferences we would not be capable of making directly even though we get no additional data from the world. Similarly, when large language models generate intermediate steps (a chain of thought) before ans...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	356,961
2006.06730	Is deep learning necessary for simple classification tasks?	Automated machine learning (AutoML) and deep learning (DL) are two cutting-edge paradigms used to solve a myriad of inductive learning tasks. In spite of their successes, little guidance exists for when to choose one approach over the other in the context of specific real-world problems. Furthermore, relatively few too...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	181,532
2310.11486	End-to-End real time tracking of children's reading with pointer network	In this work, we explore how a real time reading tracker can be built efficiently for children's voices. While previously proposed reading trackers focused on ASR-based cascaded approaches, we propose a fully end-to-end model making it less prone to lags in voice tracking. We employ a pointer network that directly lear...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	400,662
2204.02446	Detecting Cloud-Based Phishing Attacks by Combining Deep Learning Models	Web-based phishing attacks nowadays exploit popular cloud web hosting services and apps such as Google Sites and Typeform for hosting their attacks. Since these attacks originate from reputable domains and IP addresses of the cloud services, traditional phishing detection methods such as IP reputation monitoring and bl...	false	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	false	false	289,943
1802.07954	The State of the Art in Integrating Machine Learning into Visual Analytics	Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning a...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	91,005
2104.05196	StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer	Text style transfer aims to controllably generate text with targeted stylistic changes while maintaining core meaning from the source sentence constant. Many of the existing style transfer benchmarks primarily focus on individual high-level semantic changes (e.g. positive to negative), which enable controllability at a...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	229,632
2208.03083	Neural Network Verification using Residual Reasoning	With the increasing integration of neural networks as components in mission-critical systems, there is an increasing need to ensure that they satisfy various safety and liveness requirements. In recent years, numerous sound and complete verification methods have been proposed towards that end, but these typically suffe...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	311,671
2112.01830	Table2Vec: Automated Universal Representation Learning to Encode All-round Data DNA for Benchmarkable and Explainable Enterprise Data Science	Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, mark...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	269,629
2405.19019	Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media	We propose Physics-Aware Neural Implicit Solvers (PANIS), a novel, data-driven framework for learning surrogates for parametrized Partial Differential Equations (PDEs). It consists of a probabilistic, learning objective in which weighted residuals are used to probe the PDE and provide a source of {\em virtual} data i.e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	458,715
2405.00824	Efficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations	Conventional recommendation systems (RSs) are typically optimized to enhance performance metrics uniformly across all training samples. This makes it hard for data-driven RSs to cater to a diverse set of users due to the varying properties of these users. The performance disparity among various populations can harm t...	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	451,095
1104.1389	Generalizing the Markov and covariance interpolation problem using input-to-state filters	In the Markov and covariance interpolation problem a transfer function $W$ is sought that match the first coefficients in the expansion of $W$ around zero and the first coefficients of the Laurent expansion of the corresponding spectral density $WW^\star$. Here we solve an interpolation problem where the matched parame...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	9,907
2308.01752	Quantifying Retrospective Human Responsibility in Intelligent Systems	Intelligent systems have become a major part of our lives. Human responsibility for outcomes becomes unclear in the interaction with these systems, as parts of information acquisition, decision-making, and action implementation may be carried out jointly by humans and systems. Determining human causal responsibility wi...	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	383,353
1903.07837	Turing-Completeness of Dynamics in Abstract Persuasion Argumentation	Abstract Persuasion Argumentation (APA) is a dynamic argumentation formalism that extends Dung argumentation with persuasion relations. In this work, we show through two-counter Minsky machine encoding that APA dynamics is Turing-complete.	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	124,716
1606.02433	Training Design and Two-stage Channel Estimation for Correlated Two-way MIMO Relay Systems	This paper addresses the training signal design for the channel estimation in two-way multiple-input-and-multipleoutput (MIMO) relay systems, where the channels are correlated. We first derive the backward channel estimator with the optimal training signal sent by the relay node. Given the estimated backward channels a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	56,959
2312.00660	Resource-constrained knowledge diffusion processes inspired by human peer learning	We consider a setting where a population of artificial learners is given, and the objective is to optimize aggregate measures of performance, under constraints on training resources. The problem is motivated by the study of peer learning in human educational systems. In this context, we study natural knowledge diffusio...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	412,126
1907.10931	Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks	Nonlinear image registration continues to be a fundamentally important tool in medical image analysis. Diagnostic tasks, image-guided surgery and radiotherapy as well as motion analysis all rely heavily on accurate intra-patient alignment. Furthermore, inter-patient registration enables atlas-based segmentation or land...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	139,741
2404.03434	Learning From Simplicial Data Based on Random Walks and 1D Convolutions	Triggered by limitations of graph-based deep learning methods in terms of computational expressivity and model flexibility, recent years have seen a surge of interest in computational models that operate on higher-order topological domains such as hypergraphs and simplicial complexes. While the increased expressivity o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	444,258
physics/0308041	Ensembles of Protein Molecules as Statistical Analog Computers	A class of analog computers built from large numbers of microscopic probabilistic machines is discussed. It is postulated that such computers are implemented in biological systems as ensembles of protein molecules. The formalism is based on an abstract computational model referred to as Protein Molecule Machine (PMM). ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	540,811
2304.02163	GINA-3D: Learning to Generate Implicit Neural Assets in the Wild	Modeling the 3D world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving. However, manually creating or re-creating real-world-like environments is difficult, expensive, and not scalable. Recent generative model te...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	true	356,338
2009.03665	GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning	Few-shot classification is a challenge in machine learning where the goal is to train a classifier using a very limited number of labeled examples. This scenario is likely to occur frequently in real life, for example when data acquisition or labeling is expensive. In this work, we consider the problem of post-labeled ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	194,855
2105.03123	AI in (and for) Games	This chapter outlines the relation between artificial intelligence (AI) / machine learning (ML) algorithms and digital games. This relation is two-fold: on one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour (i.e. actions within the game world), commercial ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	234,046
2405.16386	Variational Offline Multi-agent Skill Discovery	Skills are effective temporal abstractions established for sequential decision making, which enable efficient hierarchical learning for long-horizon tasks and facilitate multi-task learning through their transferability. Despite extensive research, research gaps remain in multi-agent scenarios, particularly for automat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	457,391
1412.6913	The weak core and the structure of elites in social multiplex networks	Recent approaches on elite identification highlighted the important role of {\em intermediaries}, by means of a new definition of the core of a multiplex network, the {\em generalised} $K$-core. This newly introduced core subgraph crucially incorporates those individuals who, in spite of not being very connected, maint...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	38,734
1701.06659	DSSD : Deconvolutional Single Shot Detector	The main contribution of this paper is an approach for introducing additional context into state-of-the-art general object detection. To achieve this we first combine a state-of-the-art classifier (Residual-101[14]) with a fast detection framework (SSD[18]). We then augment SSD+Residual-101 with deconvolution layers to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	67,173
2412.07592	Complexity of inversion of functions on the reals	We study the complexity of deterministic and probabilistic inversions of partial computable functions on the reals.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	515,728
2304.09797	Progressive-Hint Prompting Improves Reasoning in Large Language Models	The performance of Large Language Models (LLMs) in reasoning tasks depends heavily on prompt design, with Chain-of-Thought (CoT) and self-consistency being critical methods that enhance this ability. However, these methods do not fully exploit the answers generated by the LLM to guide subsequent responses. This paper p...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	359,175
2010.09919	Optimal Decision Lists using SAT	Decision lists are one of the most easily explainable machine learning models. Given the renewed emphasis on explainable machine learning decisions, this machine learning model is increasingly attractive, combining small size and clear explainability. In this paper, we show for the first time how to construct optimal "...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	201,703
2101.02595	A Clinical Evaluation of a Low-Cost Strain Gauge Respiration Belt and Machine Learning to Detect Sleep Apnea	Sleep apnea is a serious and severely under-diagnosed sleep-related respiration disorder characterized by repeated disrupted breathing events during sleep. It is diagnosed via polysomnography which is an expensive test conducted in a sleep lab requiring sleep experts to manually score the recorded data. Since the sympt...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	214,680
2203.06692	Towards Semantic Communications: A Paradigm Shift	The last seventy years have witnessed the transition of communication from Shannon's theoretical concept to current high-efficient practical systems. Classical communication systems address the capability-deficiency issue mainly by module-stacking and technique-densification with ever-increasing complexity. In such a t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	285,201
2210.06682	Application-Driven AI Paradigm for Hand-Held Action Detection	In practical applications especially with safety requirement, some hand-held actions need to be monitored closely, including smoking cigarettes, dialing, eating, etc. Taking smoking cigarettes as example, existing smoke detection algorithms usually detect the cigarette or cigarette with hand as the target object only, ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	323,392
2203.00843	X-Trans2Cap: Cross-Modal Knowledge Transfer using Transformer for 3D Dense Captioning	3D dense captioning aims to describe individual objects by natural language in 3D scenes, where 3D scenes are usually represented as RGB-D scans or point clouds. However, only exploiting single modal information, e.g., point cloud, previous approaches fail to produce faithful descriptions. Though aggregating 2D feature...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	283,142
2412.15208	OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving	Since the advent of Multimodal Large Language Models (MLLMs), they have made a significant impact across a wide range of real-world applications, particularly in Autonomous Driving (AD). Their ability to process complex visual data and reason about intricate driving scenarios has paved the way for a new paradigm in end...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	518,982
2312.11861	A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization	Decentralized optimization methods with local updates have recently gained attention for their provable ability to communication acceleration. In these methods, nodes perform several iterations of local computations between the communication rounds. Nevertheless, this capability is effective only when the loss function...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,749
2204.00179	GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature	Although supervised deep stereo matching networks have made impressive achievements, the poor generalization ability caused by the domain gap prevents them from being applied to real-life scenarios. In this paper, we propose to leverage the feature of a model trained on large-scale datasets to deal with the domain shif...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	289,169
2311.14532	Digital Twin-Native AI-Driven Service Architecture for Industrial Networks	The dramatic increase in the connectivity demand results in an excessive amount of Internet of Things (IoT) sensors. To meet the management needs of these large-scale networks, such as accurate monitoring and learning capabilities, Digital Twin (DT) is the key enabler. However, current attempts regarding DT implementat...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	410,141
2007.00210	Review of Learning-Assisted Power System Optimization	With dramatic breakthroughs in recent years, machine learning is showing great potential to upgrade the toolbox for power system optimization. Understanding the strength and limitation of machine learning approaches is crucial to decide when and how to deploy them to boost the optimization performance. This paper pays ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	185,046
2002.00325	Polar decreasing monomial-Cartesian codes	We prove that families of polar codes with multiple kernels over certain symmetric channels can be viewed as polar decreasing monomial-Cartesian codes, offering a unified treatment for such codes, over any finite field. We define decreasing monomial-Cartesian codes as the evaluation of a set of monomials closed under d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	162,325
2011.08298	Facebook AI's WMT20 News Translation Task Submission	This paper describes Facebook AI's submission to WMT20 shared news translation task. We focus on the low resource setting and participate in two language pairs, Tamil <-> English and Inuktitut <-> English, where there are limited out-of-domain bitext and monolingual data. We approach the low resource problem using two ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	206,823
2410.01212	Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfaction	Enforcing state-wise safety constraints is critical for the application of reinforcement learning (RL) in real-world problems, such as autonomous driving and robot manipulation. However, existing safe RL methods only enforce state-wise constraints in expectation or enforce hard state-wise constraints with strong assump...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	493,657
1501.07648	Improved Adaptive Sparse Channel Estimation Using Re-Weighted L1-norm Normalized Least Mean Fourth Algorithm	In next-generation wireless communications systems, accurate sparse channel estimation (SCE) is required for coherent detection. This paper studies SCE in terms of adaptive filtering theory, which is often termed as adaptive channel estimation (ACE). Theoretically, estimation accuracy could be improved by either exploi...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	39,726
2008.07960	Dataset Bias in Few-shot Image Recognition	The goal of few-shot image recognition (FSIR) is to identify novel categories with a small number of annotated samples by exploiting transferable knowledge from training data (base categories). Most current studies assume that the transferable knowledge can be well used to identify novel categories. However, such trans...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,268
2007.06279	Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation	Medical image annotations are prohibitively time-consuming and expensive to obtain. To alleviate annotation scarcity, many approaches have been developed to efficiently utilize extra information, e.g.,semi-supervised learning further exploring plentiful unlabeled data, domain adaptation including multi-modality learnin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	186,964
1511.06841	Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification	Connectionist temporal classification (CTC) based supervised sequence training of recurrent neural networks (RNNs) has shown great success in many machine learning areas including end-to-end speech and handwritten character recognition. For the CTC training, however, it is required to unroll (or unfold) the RNN by the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	49,334

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