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
2012.01394	A Gaussian Process-based Price-Amount Curve Construction for Demand Response Provided by Internet Data Centers	For a Demand Response (DR) program with internet data centers (IDC), the Price-Amount curve that estimates how the potential DR amount depends on the DR price determined by power systems is crucial. Constructing this curve is challenging mainly due to the uncertainty in IDCs' operation. A novel Gaussian Process Regress...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	209,406
1605.06778	openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit	We introduce openXBOW, an open-source toolkit for the generation of bag-of-words (BoW) representations from multimodal input. In the BoW principle, word histograms were first used as features in document classification, but the idea was and can easily be adapted to, e.g., acoustic or visual low-level descriptors, intro...	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	56,189
2006.16637	OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning	Occlusion is an inevitable and critical problem in unsupervised optical flow learning. Existing methods either treat occlusions equally as non-occluded regions or simply remove them to avoid incorrectness. However, the occlusion regions can provide effective information for optical flow learning. In this paper, we pres...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	184,866
2409.18896	S2O: Static to Openable Enhancement for Articulated 3D Objects	Despite much progress in large 3D datasets there are currently few interactive 3D object datasets, and their scale is limited due to the manual effort required in their construction. We introduce the static to openable (S2O) task which creates interactive articulated 3D objects from static counterparts through openable...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	492,450
2009.13207	A thermodynamically consistent chemical spiking neuron capable of autonomous Hebbian learning	We propose a fully autonomous, thermodynamically consistent set of chemical reactions that implements a spiking neuron. This chemical neuron is able to learn input patterns in a Hebbian fashion. The system is scalable to arbitrarily many input channels. We demonstrate its performance in learning frequency biases in the...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	197,671
1302.4840	Joint Physical Network Coding and LDPC decoding for Two Way Wireless Relaying	In this paper, we investigate the joint design of channel and network coding in bi-directional relaying systems and propose a combined low complexity physical network coding and LDPC decoding scheme. For the same LDPC codes employed at both source nodes, we show that the relay can decodes the network coded codewords fr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	22,194
2304.05366	The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning	No free lunch theorems for supervised learning state that no learner can solve all problems or that all learners achieve exactly the same accuracy on average over a uniform distribution on learning problems. Accordingly, these theorems are often referenced in support of the notion that individual problems require speci...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	357,597
1709.09360	Learning of Colors from Color Names: Distribution and Point Estimation	Color names are often made up of multiple words. As a task in natural language understanding we investigate in depth the capacity of neural networks based on sums of word embeddings (SOWE), recurrence (LSTM and GRU based RNNs) and convolution (CNN), to estimate colors from sequences of terms. We consider both point and...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	81,616
2206.13035	A General Recipe for Likelihood-free Bayesian Optimization	The acquisition function, a critical component in Bayesian optimization (BO), can often be written as the expectation of a utility function under a surrogate model. However, to ensure that acquisition functions are tractable to optimize, restrictions must be placed on the surrogate model and utility function. To extend...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	304,815
1611.02419	Lightweight Interactions for Reciprocal Cooperation in a Social Network Game	The construction of reciprocal relationships requires cooperative interactions during the initial meetings. However, cooperative behavior with strangers is risky because the strangers may be exploiters. In this study, we show that people increase the likelihood of cooperativeness of strangers by using lightweight non-r...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	63,563
1005.4032	Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition	In this paper we present an OCR for Handwritten Devnagari Characters. Basic symbols are recognized by neural classifier. We have used four feature extraction techniques namely, intersection, shadow feature, chain code histogram and straight line fitting features. Shadow features are computed globally for character imag...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	6,537
1708.01608	A criterion for bubble merging in liquid metal: computational and experimental study	An innovative model is presented for merging of bubbles inside a liquid metal. The proposed model is based on forming a thin film (narrow channel) between merging bubbles during growth. Rupturing of the film occurs when an oscillation in velocity and pressure arises inside the channel followed by merging of the bubbles...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	78,412
2203.17241	Bayesian optimization with known experimental and design constraints for chemistry applications	Optimization strategies driven by machine learning, such as Bayesian optimization, are being explored across experimental sciences as an efficient alternative to traditional design of experiment. When combined with automated laboratory hardware and high-performance computing, these strategies enable next-generation pla...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	289,078
2203.01746	SaPHyRa: A Learning Theory Approach to Ranking Nodes in Large Networks	Ranking nodes based on their centrality stands a fundamental, yet, challenging problem in large-scale networks. Approximate methods can quickly estimate nodes' centrality and identify the most central nodes, but the ranking for the majority of remaining nodes may be meaningless. For example, ranking for less-known webs...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	283,501
2311.10129	Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art	Procedural content generation (PCG) can be applied to a wide variety of tasks in games, from narratives, levels and sounds, to trees and weapons. A large amount of game content is comprised of graphical assets, such as clouds, buildings or vegetation, that do not require gameplay function considerations. There is also ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	408,430
2312.14985	UniHuman: A Unified Model for Editing Human Images in the Wild	Human image editing includes tasks like changing a person's pose, their clothing, or editing the image according to a text prompt. However, prior work often tackles these tasks separately, overlooking the benefit of mutual reinforcement from learning them jointly. In this paper, we propose UniHuman, a unified model tha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	417,827
1507.05936	The Cumulative Distribution Transform and Linear Pattern Classification	Discriminating data classes emanating from sensors is an important problem with many applications in science and technology. We describe a new transform for pattern identification that interprets patterns as probability density functions, and has special properties with regards to classification. The transform, which w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	45,339
1805.00216	Privately Learning High-Dimensional Distributions	We present novel, computationally efficient, and differentially private algorithms for two fundamental high-dimensional learning problems: learning a multivariate Gaussian and learning a product distribution over the Boolean hypercube in total variation distance. The sample complexity of our algorithms nearly matches t...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	96,383
2002.03996	Deep Gated Networks: A framework to understand training and generalisation in deep learning	Understanding the role of (stochastic) gradient descent (SGD) in the training and generalisation of deep neural networks (DNNs) with ReLU activation has been the object study in the recent past. In this paper, we make use of deep gated networks (DGNs) as a framework to obtain insights about DNNs with ReLU activation. I...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	163,472
2205.09159	Stochastic uncertainty analysis of gravity gradient tensor components and their combinations	Full tensor gravity (FTG) devices provide up to five independent components of the gravity gradient tensor. However, we do not yet have a quantitative understanding of which tensor components or combinations of components are more important to recover a subsurface density model by gravity inversion. This is mainly beca...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	297,169
2101.05499	ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information	Social media platforms are vulnerable to fake news dissemination, which causes negative consequences such as panic and wrong medication in the healthcare domain. Therefore, it is important to automatically detect fake news in an early stage before they get widely spread. This paper analyzes the impact of incorporating ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	215,447
1905.08417	Offloading Deadline-Constrained Cellular Traffic	In this work we study the problem of hard-deadline constrained data offloading in cellular networks. A single-Base-Station (BS) single-frequency-channel downlink system is studied where users request the same packet from the BS at the beginning of each time slot. Packets have a hard deadline of one time slot. The slot ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	131,466
1903.10635	Federated Learning Of Out-Of-Vocabulary Words	We demonstrate that a character-level recurrent neural network is able to learn out-of-vocabulary (OOV) words under federated learning settings, for the purpose of expanding the vocabulary of a virtual keyboard for smartphones without exporting sensitive text to servers. High-frequency words can be sampled from the tra...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	125,321
2210.10759	On Representing Mixed-Integer Linear Programs by Graph Neural Networks	While Mixed-integer linear programming (MILP) is NP-hard in general, practical MILP has received roughly 100--fold speedup in the past twenty years. Still, many classes of MILPs quickly become unsolvable as their sizes increase, motivating researchers to seek new acceleration techniques for MILPs. With deep learning, t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	325,050
2104.02391	Weakly Supervised Video Salient Object Detection	Significant performance improvement has been achieved for fully-supervised video salient object detection with the pixel-wise labeled training datasets, which are time-consuming and expensive to obtain. To relieve the burden of data annotation, we present the first weakly supervised video salient object detection model...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	228,705
2310.14258	Constructive barrier feedback for collision avoidance in leader-follower formation control	This paper proposes a novel constructive barrier feedback for reactive collision avoidance between two agents. It incorporates this feature in a formation tracking control strategy for a group of 2nd-order dynamic robots defined in three-dimensional space. Using only relative measurements between neighboring agents, we...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	401,780
1606.00002	Uncertain programming model for multi-item solid transportation problem	In this paper, an uncertain Multi-objective Multi-item Solid Transportation Problem (MMSTP) based on uncertainty theory is presented. In the model, transportation costs, supplies, demands and conveyances parameters are taken to be uncertain parameters. There are restrictions on some items and conveyances of the model. ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	56,616
1909.11414	Inequality is rising where social network segregation interacts with urban topology	Social networks amplify inequalities due to fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation reflected in network fragmentation. Yet, little is known about what structural factors facilitate fragmentation. In this paper we use big data from a ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	146,806
2502.04393	UniCP: A Unified Caching and Pruning Framework for Efficient Video Generation	Diffusion Transformers (DiT) excel in video generation but encounter significant computational challenges due to the quadratic complexity of attention. Notably, attention differences between adjacent diffusion steps follow a U-shaped pattern. Current methods leverage this property by caching attention blocks, however, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	531,133
1311.0942	Resource Allocation for Cost Minimization in Limited Feedback MU-MIMO Systems with Delay Guarantee	In this paper, we design a resource allocation framework for the delay-sensitive Multi-User MIMO (MU-MIMO) broadcast system with limited feedback. Considering the scarcity and interrelation of the transmit power and feedback bandwidth, it is imperative to optimize the two resources in a joint and efficient manner while...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,194
2501.08518	Easing Seasickness through Attention Redirection with a Mindfulness-Based Brain--Computer Interface	Seasickness is a prevalent issue that adversely impacts both passenger experiences and the operational efficiency of maritime crews. While techniques that redirect attention have proven effective in alleviating motion sickness symptoms in terrestrial environments, applying similar strategies to manage seasickness poses...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	524,796
1810.11049	Towards a Ranking Model for Semantic Layers over Digital Archives	Archived collections of documents (like newspaper archives) serve as important information sources for historians, journalists, sociologists and other interested parties. Semantic Layers over such digital archives allow describing and publishing metadata and semantic information about the archived documents in a standa...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	111,421
2010.13674	A Corpus for Argumentative Writing Support in German	In this paper, we present a novel annotation approach to capture claims and premises of arguments and their relations in student-written persuasive peer reviews on business models in German language. We propose an annotation scheme based on annotation guidelines that allows to model claims and premises as well as suppo...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	203,212
2412.18977	CGCOD: Class-Guided Camouflaged Object Detection	Camouflaged Object Detection (COD) aims to identify objects that blend seamlessly into their surroundings. The inherent visual complexity of camouflaged objects, including their low contrast with the background, diverse textures, and subtle appearance variations, often obscures semantic cues, making accurate segmentati...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	520,660
2004.09936	DIET: Lightweight Language Understanding for Dialogue Systems	Large-scale pre-trained language models have shown impressive results on language understanding benchmarks like GLUE and SuperGLUE, improving considerably over other pre-training methods like distributed representations (GloVe) and purely supervised approaches. We introduce the Dual Intent and Entity Transformer (DIET)...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	173,496
2312.11666	HAAR: Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles	We present HAAR, a new strand-based generative model for 3D human hairstyles. Specifically, based on textual inputs, HAAR produces 3D hairstyles that could be used as production-level assets in modern computer graphics engines. Current AI-based generative models take advantage of powerful 2D priors to reconstruct 3D co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	416,672
2412.03190	Node Classification With Integrated Reject Option	One of the key tasks in graph learning is node classification. While Graph neural networks have been used for various applications, their adaptivity to reject option setting is not previously explored. In this paper, we propose NCwR, a novel approach to node classification in Graph Neural Networks (GNNs) with an integr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	513,858
2501.01115	Co-Design of a Robot Controller Board and Indoor Positioning System for IoT-Enabled Applications	This paper describes the development of a cost-effective yet precise indoor robot navigation system composed of a custom robot controller board and an indoor positioning system. First, the proposed robot controller board has been specially designed for emerging IoT-based robot applications and is capable of driving two...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	521,933
2204.07032	Farmer-Bot: An Interactive Bot for Farmers	The Indian Agricultural sector generates huge employment accounting for over 54% of countrys workforce. Its overall stand in GDP is close to 14%. However, this sector has been plagued by knowledge and infrastructure deficit, especially in the rural sectors. Like other sectors, the Indian Agricultural sector has seen ra...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	291,533
2202.11474	Residual Bootstrap Exploration for Stochastic Linear Bandit	We propose a new bootstrap-based online algorithm for stochastic linear bandit problems. The key idea is to adopt residual bootstrap exploration, in which the agent estimates the next step reward by re-sampling the residuals of mean reward estimate. Our algorithm, residual bootstrap exploration for stochastic linear ba...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	281,903
1010.5377	Estimating Network Parameters for Selecting Community Detection Algorithms	This paper considers the problem of algorithm selection for community detection. The aim of community detection is to identify sets of nodes in a network which are more interconnected relative to their connectivity to the rest of the network. A large number of algorithms have been developed to tackle this problem, but ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	8,021
2106.16032	Robust Inertial-aided Underwater Localization based on Imaging Sonar Keyframes	This article focuses on feature-based underwater localization and navigation for autonomous underwater vehicles (AUVs) using 2D imaging sonar measurements. The sparsity of underwater acoustic features and the loss of elevation angle in sonar images may introduce wrong feature matches or insufficient features for optimi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	243,947
2405.02659	R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models	Retrieval-augmented large language models (LLMs) leverage relevant content retrieved by information retrieval systems to generate correct responses, aiming to alleviate the hallucination problem. However, existing retriever-responder methods typically append relevant documents to the prompt of LLMs to perform text gene...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	451,856
2209.04421	Private Read Update Write (PRUW) in Federated Submodel Learning (FSL): Communication Efficient Schemes With and Without Sparsification	We investigate the problem of private read update write (PRUW) in relation to private federated submodel learning (FSL), where a machine learning model is divided into multiple submodels based on the different types of data used to train the model. In PRUW, each user downloads the required submodel without revealing it...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	true	316,779
2207.09634	HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection	The fast development of self-supervised learning lowers the bar learning feature representation from massive unlabeled data and has triggered a series of research on change detection of remote sensing images. Challenges in adapting self-supervised learning from natural images classification to remote sensing images cha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,954
2304.10295	Decouple Non-parametric Knowledge Distillation For End-to-end Speech Translation	Existing techniques often attempt to make knowledge transfer from a powerful machine translation (MT) to speech translation (ST) model with some elaborate techniques, which often requires transcription as extra input during training. However, transcriptions are not always available, and how to improve the ST model perf...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	359,363
2308.07134	Language is All a Graph Needs	The emergence of large-scale pre-trained language models has revolutionized various AI research domains. Transformers-based Large Language Models (LLMs) have gradually replaced CNNs and RNNs to unify fields of computer vision and natural language processing. Compared with independent data samples such as images, videos...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	385,404
2404.04809	Low-Resource Machine Translation through Retrieval-Augmented LLM Prompting: A Study on the Mambai Language	This study explores the use of large language models (LLMs) for translating English into Mambai, a low-resource Austronesian language spoken in Timor-Leste, with approximately 200,000 native speakers. Leveraging a novel corpus derived from a Mambai language manual and additional sentences translated by a native speaker...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	444,797
2303.09095	SLOPER4D: A Scene-Aware Dataset for Global 4D Human Pose Estimation in Urban Environments	We present SLOPER4D, a novel scene-aware dataset collected in large urban environments to facilitate the research of global human pose estimation (GHPE) with human-scene interaction in the wild. Employing a head-mounted device integrated with a LiDAR and camera, we record 12 human subjects' activities over 10 diverse u...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	351,909
2201.11198	Implementation of Advanced Wind Turbine Controllers for Scaled Turbine Testing in a Wind Tunnel	Based on a series of two experimental campaigns testing advanced controllers on a scaled wind turbine operating in a wind tunnel, this contribution describes the overall experimental method, challenges faced, lessons learned, and opportunities for future work. The two campaigns, run in Fall 2018 and Fall 2019, tested u...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	277,217
2402.07334	Differentially Private Training of Mixture of Experts Models	This position paper investigates the integration of Differential Privacy (DP) in the training of Mixture of Experts (MoE) models within the field of natural language processing. As Large Language Models (LLMs) scale to billions of parameters, leveraging expansive datasets, they exhibit enhanced linguistic capabilities ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	428,663
2207.11161	Lagrangian Method for Q-Function Learning (with Applications to Machine Translation)	This paper discusses a new approach to the fundamental problem of learning optimal Q-functions. In this approach, optimal Q-functions are formulated as saddle points of a nonlinear Lagrangian function derived from the classic Bellman optimality equation. The paper shows that the Lagrangian enjoys strong duality, in spi...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	309,523
1408.6125	The proposal of improved component selection framework	Component selection is considered one of hard tasks in Component Based Software Engineering (CBSE). It is difficult to find the optimal component selection. CBSE is an approach that is used to develop a software system from pre-existing software components. Appropriate software component selection plays an important ro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	35,604
2403.07849	Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations	We formulate an XAI-based model improvement approach for Graph Neural Networks (GNNs) for node classification, called Explanation Enhanced Graph Learning (EEGL). The goal is to improve predictive performance of GNN using explanations. EEGL is an iterative self-improving algorithm, which starts with a learned "vanilla" ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	437,049
2208.04446	Safe Dual Gradient Method for Network Utility Maximization Problems	In this paper, we introduce a novel first-order dual gradient algorithm for solving network utility maximization problems that arise in resource allocation schemes over networks with safety-critical constraints. Inspired by applications where customers' demand can only be affected through posted prices and real-time tw...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	312,107
2303.15579	Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning	We propose an adjusted Wasserstein distributionally robust estimator -- based on a nonlinear transformation of the Wasserstein distributionally robust (WDRO) estimator in statistical learning. The classic WDRO estimator is asymptotically biased, while our adjusted WDRO estimator is asymptotically unbiased, resulting in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	354,541
2103.12016	Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature	Algorithmic decision-making (ADM) increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires taking people's fairness perceptions into account ...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	226,032
2402.11739	A Transition System Abstraction Framework for Neural Network Dynamical System Models	This paper proposes a transition system abstraction framework for neural network dynamical system models to enhance the model interpretability, with applications to complex dynamical systems such as human behavior learning and verification. To begin with, the localized working zone will be segmented into multiple local...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	430,539
2111.04613	Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems	We introduce a curriculum learning algorithm, Variational Automatic Curriculum Learning (VACL), for solving challenging goal-conditioned cooperative multi-agent reinforcement learning problems. We motivate our paradigm through a variational perspective, where the learning objective can be decomposed into two terms: tas...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	265,534
2307.06960	On stepwise advancement of fractures and pressure oscillations in saturated porous media	Comments to K.M. Pervaiz Fathima, Ren\'e de Borst, Implications of single or multiple pressure degrees of freedom at fracture in fluid saturated porous media, Engineering Fracture Mechanics, 213 (2019), 1-20.	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	379,235
2306.01076	Quantization-Aware and Tensor-Compressed Training of Transformers for Natural Language Understanding	Fine-tuned transformer models have shown superior performances in many natural language tasks. However, the large model size prohibits deploying high-performance transformer models on resource-constrained devices. This paper proposes a quantization-aware tensor-compressed training approach to reduce the model size, ari...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	370,282
1405.6052	Limited Feedback Massive MISO Systems with Trellis Coded Quantization for Correlated Channels	In this paper, we propose trellis coded quantization (TCQ) based limited feedback techniques for massive multiple-input single-output (MISO) frequency division duplexing (FDD) systems in temporally and spatially correlated channels. We exploit the correlation present in the channel to effectively quantize channel direc...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	33,331
2301.02979	CameraPose: Weakly-Supervised Monocular 3D Human Pose Estimation by Leveraging In-the-wild 2D Annotations	To improve the generalization of 3D human pose estimators, many existing deep learning based models focus on adding different augmentations to training poses. However, data augmentation techniques are limited to the "seen" pose combinations and hard to infer poses with rare "unseen" joint positions. To address this pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	339,650
1910.01911	Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detector	Automatic post-disaster damage detection using aerial imagery is crucial for quick assessment of damage caused by disaster and development of a recovery plan. The main problem preventing us from creating an applicable model in practice is that damaged (positive) examples we are trying to detect are much harder to obtai...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	148,080
2205.09342	Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel	Recent research in the theory of overparametrized learning has sought to establish generalization guarantees in the interpolating regime. Such results have been established for a few common classes of methods, but so far not for ensemble methods. We devise an ensemble classification method that simultaneously interpola...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	297,233
2203.16095	OLxPBench: Real-time, Semantically Consistent, and Domain-specific are Essential in Benchmarking, Designing, and Implementing HTAP Systems	As real-time analysis of the new data become increasingly compelling, more organizations deploy Hybrid Transactional/Analytical Processing (HTAP) systems to support real-time queries on data recently generated by online transaction processing. This paper argues that real-time queries, semantically consistent schema, an...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	288,654
2008.12025	Feature Selection from High-Dimensional Data with Very Low Sample Size: A Cautionary Tale	In classification problems, the purpose of feature selection is to identify a small, highly discriminative subset of the original feature set. In many applications, the dataset may have thousands of features and only a few dozens of samples (sometimes termed `wide'). This study is a cautionary tale demonstrating why fe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	193,469
2004.03133	Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation	Recent research demonstrates that word embeddings, trained on the human-generated corpus, have strong gender biases in embedding spaces, and these biases can result in the discriminative results from the various downstream tasks. Whereas the previous methods project word embeddings into a linear subspace for debiasing,...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	171,458
1802.04427	Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E Stained Histology Sections	Nuclear segmentation is an important step for profiling aberrant regions of histology sections. However, segmentation is a complex problem as a result of variations in nuclear geometry (e.g., size, shape), nuclear type (e.g., epithelial, fibroblast), and nuclear phenotypes (e.g., vesicular, aneuploidy). The problem is ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	90,218
2003.09600	A level set representation method for N-dimensional convex shape and applications	In this work, we present a new efficient method for convex shape representation, which is regardless of the dimension of the concerned objects, using level-set approaches. Convexity prior is very useful for object completion in computer vision. It is a very challenging task to design an efficient method for high dimens...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	169,097
2207.00961	Digital-twin-enhanced metal tube bending forming real-time prediction method based on Multi-source-input MTL	As one of the most widely used metal tube bending methods, the rotary draw bending (RDB) process enables reliable and high-precision metal tube bending forming (MTBF). The forming accuracy is seriously affected by the springback and other potential forming defects, of which the mechanism analysis is difficult to deal w...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	305,974
2403.15143	Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project	Image annotation is one of the most essential tasks for guaranteeing proper treatment for patients and tracking progress over the course of therapy in the field of medical imaging and disease diagnosis. However, manually annotating a lot of 2D and 3D imaging data can be extremely tedious. Deep Learning (DL) based segme...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	440,419
1210.6142	Cooperating epidemics of foodborne diseases with diverse trade networks	The frequent outbreak of severe foodborne diseases warns of a potential threat that the global trade networks could spread fatal pathogens. The global trade network is a typical overlay network, which compounds multiple standalone trade networks representing the transmission of a single product and connecting the same ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	19,340
2008.07347	HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition	Summary: Named Entity Recognition (NER) is an important step in biomedical information extraction pipelines. Tools for NER should be easy to use, cover multiple entity types, highly accurate, and robust towards variations in text genre and style. To this end, we propose HunFlair, an NER tagger covering multiple entity ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	192,080
2112.03259	Novel Local Radiomic Bayesian Classifiers for Non-Invasive Prediction of MGMT Methylation Status in Glioblastoma	Glioblastoma, an aggressive brain cancer, is amongst the most lethal of all cancers. Expression of the O6-methylguanine-DNA-methyltransferase (MGMT) gene in glioblastoma tumor tissue is of clinical importance as it has a significant effect on the efficacy of Temozolomide, the primary chemotherapy treatment administered...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	270,138
2401.16441	FaKnow: A Unified Library for Fake News Detection	Over the past years, a large number of fake news detection algorithms based on deep learning have emerged. However, they are often developed under different frameworks, each mandating distinct utilization methodologies, consequently hindering reproducibility. Additionally, a substantial amount of redundancy characteriz...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	424,832
2502.06707	FinMamba: Market-Aware Graph Enhanced Multi-Level Mamba for Stock Movement Prediction	Recently, combining stock features with inter-stock correlations has become a common and effective approach for stock movement prediction. However, financial data presents significant challenges due to its low signal-to-noise ratio and the dynamic complexity of the market, which give rise to two key limitations in exis...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	532,196
1909.10278	Detection of Classifier Inconsistencies in Image Steganalysis	In this paper, a methodology to detect inconsistencies in classification-based image steganalysis is presented. The proposed approach uses two classifiers: the usual one, trained with a set formed by cover and stego images, and a second classifier trained with the set obtained after embedding additional random messages...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	146,500
1703.06370	Weakly-supervised DCNN for RGB-D Object Recognition in Real-World Applications Which Lack Large-scale Annotated Training Data	This paper addresses the problem of RGBD object recognition in real-world applications, where large amounts of annotated training data are typically unavailable. To overcome this problem, we propose a novel, weakly-supervised learning architecture (DCNN-GPC) which combines parametric models (a pair of Deep Convolutiona...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	70,220
2303.17172	Lengths of divisible codes with restricted column multiplicities	We determine the minimum possible column multiplicity of even, doubly-, and triply-even codes given their length. This refines a classification result for the possible lengths of $q^r$-divisible codes over $\mathbb{F}_q$. We also give a few computational results for field sizes $q>2$. Non-existence results of divisible...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	355,131
1704.07068	Diffusion geometry unravels the emergence of functional clusters in collective phenomena	Collective phenomena emerge from the interaction of natural or artificial units with a complex organization. The interplay between structural patterns and dynamics might induce functional clusters that, in general, are different from topological ones. In biological systems, like the human brain, the overall functionali...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	72,286
1812.10889	InstaGAN: Instance-aware Image-to-Image Translation	Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs). However, previous methods often fail in challenging cases, in particular, when an image has multiple target instances and a translation task involves significa...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	117,457
1912.05086	Learning from Noisy Anchors for One-stage Object Detection	State-of-the-art object detectors rely on regressing and classifying an extensive list of possible anchors, which are divided into positive and negative samples based on their intersection-over-union (IoU) with corresponding groundtruth objects. Such a harsh split conditioned on IoU results in binary labels that are po...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	157,009
2109.06677	Specified Certainty Classification, with Application to Read Classification for Reference-Guided Metagenomic Assembly	Specified Certainty Classification (SCC) is a new paradigm for employing classifiers whose outputs carry uncertainties, typically in the form of Bayesian posterior probabilities. By allowing the classifier output to be less precise than one of a set of atomic decisions, SCC allows all decisions to achieve a specified l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	255,234
2501.07701	Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim	Surrogate models are effective tools for accelerated design of complex systems. The result of a design optimization procedure using surrogate models can be used to initialize an optimization routine using the full order system. High accuracy of the surrogate model can be advantageous for fast convergence. In this work,...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	524,472
2311.05144	Counter-Empirical Attacking based on Adversarial Reinforcement Learning for Time-Relevant Scoring System	Scoring systems are commonly seen for platforms in the era of big data. From credit scoring systems in financial services to membership scores in E-commerce shopping platforms, platform managers use such systems to guide users towards the encouraged activity pattern, and manage resources more effectively and more effic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	406,489
2312.00048	Tokenized Model: A Blockchain-Empowered Decentralized Model Ownership Verification Platform	With the development of practical deep learning models like generative AI, their excellent performance has brought huge economic value. For instance, ChatGPT has attracted more than 100 million users in three months. Since the model training requires a lot of data and computing power, a well-performing deep learning mo...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	411,875
2203.05071	On the influence of over-parameterization in manifold based surrogates and deep neural operators	Constructing accurate and generalizable approximators for complex physico-chemical processes exhibiting highly non-smooth dynamics is challenging. In this work, we propose new developments and perform comparisons for two promising approaches: manifold-based polynomial chaos expansion (m-PCE) and the deep neural operato...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	284,688
2004.02379	Reinforcement Learning for Accident Risk-Adaptive V2X Networking	The significance of vehicle-to-everything (V2X) communications has been ever increased as connected and autonomous vehicles get more emergent in practice. The key challenge is the dynamicity: each vehicle needs to recognize the frequent changes of the surroundings and apply them to its networking behavior. This is the ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	171,211
1801.06611	Multiple Description Convolutional Neural Networks for Image Compression	Multiple description coding (MDC) is able to stably transmit the signal in the un-reliable and non-prioritized networks, which has been broadly studied for several decades. However, the traditional MDC doesn't well leverage image's context features to generate multiple descriptions. In this paper, we propose a novel st...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	88,636
2203.00727	Repeated Robot-Assisted Unilateral Stiffness Perturbations Result in Significant Aftereffects Relevant to Post-Stroke Gait Rehabilitation	Due to hemiparesis, stroke survivors frequently develop a dysfunctional gait that is often characterized by an overall decrease in walking speed and a unilateral decrease in step length. With millions currently affected by this dysfunctional gait, robust and effective rehabilitation protocols are needed. Although robot...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	283,093
1905.04564	PrivateJobMatch: A Privacy-Oriented Deferred Multi-Match Recommender System for Stable Employment	Coordination failure reduces match quality among employers and candidates in the job market, resulting in a large number of unfilled positions and/or unstable, short-term employment. Centralized job search engines provide a platform that connects directly employers with job-seekers. However, they require users to discl...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	130,505
2501.14090	Making Reliable and Flexible Decisions in Long-tailed Classification	Long-tailed classification is challenging due to its heavy imbalance in class probabilities. While existing methods often focus on overall accuracy or accuracy for tail classes, they overlook a critical aspect: certain types of errors can carry greater risks than others in real-world long-tailed problems. For example, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	526,971
1911.05767	Maximizing the Partial Decode-and-Forward Rate in the Gaussian MIMO Relay Channel	It is known that circularly symmetric Gaussian signals are the optimal input signals for the partial decode-and-forward (PDF) coding scheme in the Gaussian multiple-input multiple-output (MIMO) relay channel, but there is currently no method to find the optimal covariance matrices nor to compute the optimal achievable ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	153,360
2305.13652	Cross-lingual Knowledge Transfer and Iterative Pseudo-labeling for Low-Resource Speech Recognition with Transducers	Voice technology has become ubiquitous recently. However, the accuracy, and hence experience, in different languages varies significantly, which makes the technology not equally inclusive. The availability of data for different languages is one of the key factors affecting accuracy, especially in training of all-neural...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,605
1903.03305	Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods	Typical attempts to improve the capability of visual place recognition techniques include the use of multi-sensor fusion and integration of information over time from image sequences. These approaches can improve performance but have disadvantages including the need for multiple physical sensors and calibration process...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	123,703
2410.16736	Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration	Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data, leading to impressive performance across a range of downstream applications. Current methods often rely on human-annotated data or predefined task templates to direct powerful LLMs in synthesizing task-r...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	501,169
2301.03116	Unsupervised Learning for Combinatorial Optimization Needs Meta-Learning	A general framework of unsupervised learning for combinatorial optimization (CO) is to train a neural network (NN) whose output gives a problem solution by directly optimizing the CO objective. Albeit with some advantages over traditional solvers, the current framework optimizes an averaged performance over the distrib...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	339,699
2403.12401	VQ-NeRV: A Vector Quantized Neural Representation for Videos	Implicit neural representations (INR) excel in encoding videos within neural networks, showcasing promise in computer vision tasks like video compression and denoising. INR-based approaches reconstruct video frames from content-agnostic embeddings, which hampers their efficacy in video frame regression and restricts th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	439,157
2208.04840	Risk-averse Stochastic Optimization for Farm Management Practices and Cultivar Selection Under Uncertainty	Optimizing management practices and selecting the best cultivar for planting play a significant role in increasing agricultural food production and decreasing environmental footprint. In this study, we develop optimization frameworks under uncertainty using conditional value-at-risk in the stochastic programming object...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	312,240

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