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
1209.6419	Partial Gaussian Graphical Model Estimation	This paper studies the partial estimation of Gaussian graphical models from high-dimensional empirical observations. We derive a convex formulation for this problem using $\ell_1$-regularized maximum-likelihood estimation, which can be solved via a block coordinate descent algorithm. Statistical estimation performance ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	18,815
1905.00944	Capacity Limits of Full-Duplex Cellular Network	This paper aims to characterize the capacity limits of a wireless cellular network with a full-duplex (FD) base-station (BS) and half-duplex user terminals, in which three independent messages are communicated: the uplink message $m_1$ from the uplink user to the BS, the downlink message $m_2$ from the BS to the downli...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	129,593
2210.00620	Does Wikidata Support Analogical Reasoning?	Analogical reasoning methods have been built over various resources, including commonsense knowledge bases, lexical resources, language models, or their combination. While the wide coverage of knowledge about entities and events make Wikidata a promising resource for analogical reasoning across situations and domains, ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	320,933
2206.04992	Artificial Intelligence Enabled NOMA Towards Next Generation Multiple Access	This article focuses on the application of artificial intelligence (AI) in non-orthogonal multiple-access (NOMA), which aims to achieve automated, adaptive, and high-efficiency multi-user communications towards next generation multiple access (NGMA). First, the limitations of current scenario-specific multiple-antenna ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	301,851
2402.15584	State Space Models for Event Cameras	Today, state-of-the-art deep neural networks that process event-camera data first convert a temporal window of events into dense, grid-like input representations. As such, they exhibit poor generalizability when deployed at higher inference frequencies (i.e., smaller temporal windows) than the ones they were trained on...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	432,197
1201.3901	On the Dispersions of Three Network Information Theory Problems	We analyze the dispersions of distributed lossless source coding (the Slepian-Wolf problem), the multiple-access channel and the asymmetric broadcast channel. For the two-encoder Slepian-Wolf problem, we introduce a quantity known as the entropy dispersion matrix, which is analogous to the scalar dispersions that have ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	13,883
1810.03233	Towards Gradient Free and Projection Free Stochastic Optimization	This paper focuses on the problem of \emph{constrained} \emph{stochastic} optimization. A zeroth order Frank-Wolfe algorithm is proposed, which in addition to the projection-free nature of the vanilla Frank-Wolfe algorithm makes it gradient free. Under convexity and smoothness assumption, we show that the proposed algo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	109,764
2103.10461	Robot Manipulator Control with Inverse Kinematics PD-Pseudoinverse Jacobian and Forward Kinematics Denavit Hartenberg	This paper presents the development of vision-based robotic arm manipulator control by applying Proportional Derivative-Pseudoinverse Jacobian (PD-PIJ) kinematics and Denavit Hartenberg forward kinematics. The task of sorting objects based on color is carried out to observe error propagation in the implementation of ma...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	225,458
2501.10431	Quantum Annealing for Robust Principal Component Analysis	Principal component analysis is commonly used for dimensionality reduction, feature extraction, denoising, and visualization. The most commonly used principal component analysis method is based upon optimization of the L2-norm, however, the L2-norm is known to exaggerate the contribution of errors and outliers. When op...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	525,516
2305.10927	Causal Document-Grounded Dialogue Pre-training	The goal of document-grounded dialogue (DocGD) is to generate a response by grounding the evidence in a supporting document in accordance with the dialogue context. This process involves four variables that are causally connected. Recently, task-specific pre-training has greatly boosted performances on many downstream ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	365,295
1004.1794	Probabilistic Semantic Web Mining Using Artificial Neural Analysis	Most of the web user's requirements are search or navigation time and getting correctly matched result. These constrains can be satisfied with some additional modules attached to the existing search engines and web servers. This paper proposes that powerful architecture for search engines with the title of Probabilisti...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	6,137
2403.04258	Depth-aware Test-Time Training for Zero-shot Video Object Segmentation	Zero-shot Video Object Segmentation (ZSVOS) aims at segmenting the primary moving object without any human annotations. Mainstream solutions mainly focus on learning a single model on large-scale video datasets, which struggle to generalize to unseen videos. In this work, we introduce a test-time training (TTT) strateg...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	435,528
2405.15167	ProDAG: Projection-Induced Variational Inference for Directed Acyclic Graphs	Directed acyclic graph (DAG) learning is a rapidly expanding field of research. Though the field has witnessed remarkable advances over the past few years, it remains statistically and computationally challenging to learn a single (point estimate) DAG from data, let alone provide uncertainty quantification. Our paper a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	456,782
2108.04051	A Streamwise GAN Vocoder for Wideband Speech Coding at Very Low Bit Rate	Recently, GAN vocoders have seen rapid progress in speech synthesis, starting to outperform autoregressive models in perceptual quality with much higher generation speed. However, autoregressive vocoders are still the common choice for neural generation of speech signals coded at very low bit rates. In this paper, we p...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	249,874
2212.02376	DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization	Decentralized bilevel optimization has received increasing attention recently due to its foundational role in many emerging multi-agent learning paradigms (e.g., multi-agent meta-learning and multi-agent reinforcement learning) over peer-to-peer edge networks. However, to work with the limited computation and communica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	334,768
1511.09174	A class of $q$-ary linear codes derived from irreducible cyclic codes	Recently, linear codes with a few weights were widely investigated due to their applications in secret sharing schemes and authentication schemes. In this letter, we present a class of $q$-ary linear codes derived from irreducible cyclic codes with $q$ a prime power. We use Gauss sums to represent its Hamming weights a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	49,643
2302.04464	Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models	Federated learning (FL) is an emerging technique that trains massive and geographically distributed edge data while maintaining privacy. However, FL has inherent challenges in terms of fairness and computational efficiency due to the rising heterogeneity of edges, and thus usually results in sub-optimal performance in ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	344,717
2005.07186	Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors	Bayesian neural networks (BNNs) demonstrate promising success in improving the robustness and uncertainty quantification of modern deep learning. However, they generally struggle with underfitting at scale and parameter efficiency. On the other hand, deep ensembles have emerged as alternatives for uncertainty quantific...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	177,220
2104.13530	Extreme Rotation Estimation using Dense Correlation Volumes	We present a technique for estimating the relative 3D rotation of an RGB image pair in an extreme setting, where the images have little or no overlap. We observe that, even when images do not overlap, there may be rich hidden cues as to their geometric relationship, such as light source directions, vanishing points, an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	232,529
2409.16949	DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling	In this paper, we present an effective data augmentation framework leveraging the Large Language Model (LLM) and Diffusion Model (DM) to tackle the challenges inherent in data-scarce scenarios. Recently, DMs have opened up the possibility of generating synthetic images to complement a few training images. However, incr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	491,590
2005.07902	The Power of Triply Complementary Priors for Image Compressive Sensing	Recent works that utilized deep models have achieved superior results in various image restoration applications. Such approach is typically supervised which requires a corpus of training images with distribution similar to the images to be recovered. On the other hand, the shallow methods which are usually unsupervised...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	177,434
2205.05515	Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting	Counterfactual explanations (CFEs) highlight what changes to a model's input would have changed its prediction in a particular way. CFEs have gained considerable traction as a psychologically grounded solution for explainable artificial intelligence (XAI). Recent innovations introduce the notion of computational plausi...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	295,957
2210.05728	DeepMend: Learning Occupancy Functions to Represent Shape for Repair	We present DeepMend, a novel approach to reconstruct restorations to fractured shapes using learned occupancy functions. Existing shape repair approaches predict low-resolution voxelized restorations, or require symmetries or access to a pre-existing complete oracle. We represent the occupancy of a fractured shape as t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	322,974
1710.10370	Topology Adaptive Graph Convolutional Networks	Spectral graph convolutional neural networks (CNNs) require approximation to the convolution to alleviate the computational complexity, resulting in performance loss. This paper proposes the topology adaptive graph convolutional network (TAGCN), a novel graph convolutional network defined in the vertex domain. We provi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	83,364
2004.08301	Belief Propagation for Maximum Coverage on Weighted Bipartite Graph and Application to Text Summarization	We study text summarization from the viewpoint of maximum coverage problem. In graph theory, the task of text summarization is regarded as maximum coverage problem on bipartite graph with weighted nodes. In recent study, belief-propagation based algorithm for maximum coverage on unweighted graph was proposed using the ...	false	false	false	true	false	true	true	false	true	false	false	false	false	false	false	false	false	false	173,031
2309.09206	Differentiable SLAM Helps Deep Learning-based LiDAR Perception Tasks	We investigate a new paradigm that uses differentiable SLAM architectures in a self-supervised manner to train end-to-end deep learning models in various LiDAR based applications. To the best of our knowledge there does not exist any work that leverages SLAM as a training signal for deep learning based models. We explo...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	392,512
1912.07133	Digital filters with vanishing moments for shape analysis	Shape- and scale-selective digital-filters, with steerable finite/infinite impulse responses (FIR/IIRs) and non-recursive/recursive realizations, that are separable in both spatial dimensions and adequately isotropic, are derived. The filters are conveniently designed in the frequency domain via derivative constraints ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	157,522
1712.09550	Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling	We consider the problem of Active Search, where a maximum of relevant objects - ideally all relevant objects - should be retrieved with the minimum effort or minimum time. Typically, there are two main challenges to face when tackling this problem: first, the class of relevant objects has often low prevalence and, seco...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	87,372
1605.04466	Generalized Linear Models for Aggregated Data	Databases in domains such as healthcare are routinely released to the public in aggregated form. Unfortunately, naive modeling with aggregated data may significantly diminish the accuracy of inferences at the individual level. This paper addresses the scenario where features are provided at the individual level, but th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	55,868
2401.04545	Evaluating Gesture Recognition in Virtual Reality	Human-Robot Interaction (HRI) has become increasingly important as robots are being integrated into various aspects of daily life. One key aspect of HRI is gesture recognition, which allows robots to interpret and respond to human gestures in real-time. Gesture recognition plays an important role in non-verbal communic...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	420,465
cs/0701003	Magnification Laws of Winner-Relaxing and Winner-Enhancing Kohonen Feature Maps	Self-Organizing Maps are models for unsupervised representation formation of cortical receptor fields by stimuli-driven self-organization in laterally coupled winner-take-all feedforward structures. This paper discusses modifications of the original Kohonen model that were motivated by a potential function, in their ab...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	540,004
1404.6560	Content Caching and Delivery over Heterogeneous Wireless Networks	Emerging heterogeneous wireless architectures consist of a dense deployment of local-coverage wireless access points (APs) with high data rates, along with sparsely-distributed, large-coverage macro-cell base stations (BS). We design a coded caching-and-delivery scheme for such architectures that equips APs with storag...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,600
2111.15496	Bayesian Modelling of Multivalued Power Curves from an Operational Wind Farm	Power curves capture the relationship between wind speed and output power for a specific wind turbine. Accurate regression models of this function prove useful in monitoring, maintenance, design, and planning. In practice, however, the measurements do not always correspond to the ideal curve: power curtailments will ap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	268,950
2408.06732	Exploring the anatomy of articulation rate in spontaneous English speech: relationships between utterance length effects and social factors	Speech rate has been shown to vary across social categories such as gender, age, and dialect, while also being conditioned by properties of speech planning. The effect of utterance length, where speech rate is faster and less variable for longer utterances, has also been shown to reduce the role of social factors once ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	480,322
2405.08101	Can machine learning unlock new insights into high-frequency trading?	We design and train machine learning models to capture the nonlinear interactions between financial market dynamics and high-frequency trading (HFT) activity. In doing so, we introduce new metrics to identify liquidity-demanding and -supplying HFT strategies. Both types of HFT strategies increase activity in response t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	453,986
2303.03260	On the Use of Neural Networks for Full Waveform Inversion	Neural networks have recently gained attention in solving inverse problems. One prominent methodology are Physics-Informed Neural Networks (PINNs) which can solve both forward and inverse problems. In the paper at hand, full waveform inversion is the considered inverse problem. The performance of PINNs is compared agai...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	349,661
2303.05479	Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning	A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interaction. However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we devise an approach for learning an effec...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	350,480
2003.10664	On Localizing a Camera from a Single Image	Public cameras often have limited metadata describing their attributes. A key missing attribute is the precise location of the camera, using which it is possible to precisely pinpoint the location of events seen in the camera. In this paper, we explore the following question: under what conditions is it possible to est...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	169,406
2003.09280	Deep Reinforcement Learning with Weighted Q-Learning	Reinforcement learning algorithms based on Q-learning are driving Deep Reinforcement Learning (DRL) research towards solving complex problems and achieving super-human performance on many of them. Nevertheless, Q-Learning is known to be positively biased since it learns by using the maximum over noisy estimates of expe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	169,004
2304.12461	TensoIR: Tensorial Inverse Rendering	We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields. Unlike previous works that use purely MLP-based neural fields, thus suffering from low capacity and high computation costs, we extend TensoRF, a state-of-the-art approach for radiance field modeling, to estimate scen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	360,219
1903.11107	Energy Storage Management via Deep Q-Networks	Energy storage devices represent environmentally friendly candidates to cope with volatile renewable energy generation. Motivated by the increase in privately owned storage systems, this paper studies the problem of real-time control of a storage unit co-located with a renewable energy generator and an inelastic load. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	125,431
2209.13511	Phy-Taylor: Physics-Model-Based Deep Neural Networks	Purely data-driven deep neural networks (DNNs) applied to physical engineering systems can infer relations that violate physics laws, thus leading to unexpected consequences. To address this challenge, we propose a physics-model-based DNN framework, called Phy-Taylor, that accelerates learning compliant representations...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	319,922
2210.03230	NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies	Zero-cost proxies (ZC proxies) are a recent architecture performance prediction technique aiming to significantly speed up algorithms for neural architecture search (NAS). Recent work has shown that these techniques show great promise, but certain aspects, such as evaluating and exploiting their complementary strengths...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	321,943
2202.11199	Differentially Private Regression with Unbounded Covariates	We provide computationally efficient, differentially private algorithms for the classical regression settings of Least Squares Fitting, Binary Regression and Linear Regression with unbounded covariates. Prior to our work, privacy constraints in such regression settings were studied under strong a priori bounds on covar...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	281,797
1904.07053	Decentralised Cooperative Collision Avoidance with Reference-Free Model Predictive Control and Desired Versus Planned Trajectories	Connected and automated vehicles provide a new opportunity for highly advanced collision avoidance, in which several cars cooperate to reach an optimal overall outcome, that no single car acting in isolation could achieve. For example, one car may automatically swerve to allow another to avoid an obstacle. However, thi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	127,698
2012.10880	Where, What, Whether: Multi-modal Learning Meets Pedestrian Detection	Pedestrian detection benefits greatly from deep convolutional neural networks (CNNs). However, it is inherently hard for CNNs to handle situations in the presence of occlusion and scale variation. In this paper, we propose W$^3$Net, which attempts to address above challenges by decomposing the pedestrian detection task...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,469
1802.03522	Enhanced version of AdaBoostM1 with J48 Tree learning method	Machine Learning focuses on the construction and study of systems that can learn from data. This is connected with the classification problem, which usually is what Machine Learning algorithms are designed to solve. When a machine learning method is used by people with no special expertise in machine learning, it is im...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	89,995
1301.2268	Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables	Global variational approximation methods in graphical models allow efficient approximate inference of complex posterior distributions by using a simpler model. The choice of the approximating model determines a tradeoff between the complexity of the approximation procedure and the quality of the approximation. In this ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	20,944
1606.09076	Decentralized Caching Schemes and Performance Limits in Two-layer Networks	We study the decentralized caching scheme in a two-layer network, which includes a sever, multiple helpers, and multiple users. Basically, the proposed caching scheme consists of two phases, i.e, placement phase and delivery phase. In the placement phase, each helper/user randomly and independently selects contents fro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	57,944
1906.10770	Deep Modular Co-Attention Networks for Visual Question Answering	Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective `co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	136,509
1602.06529	Robust Resource Allocation for Full-Duplex Cognitive Radio Systems	In this paper, we investigate resource allocation algorithm design for full-duplex (FD) cognitive radio systems. The secondary network employs a FD base station for serving multiple half-duplex downlink and uplink users simultaneously. We study the resource allocation design for minimizing the maximum interference leak...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	52,383
2004.08530	Decoder Error Propagation Mitigation for Spatially Coupled LDPC Codes	In this paper, we introduce two new methods of mitigating decoder error propagation for low-latency sliding window decoding (SWD) of spatially coupled low density parity check (SC-LDPC) codes. Building on the recently introduced idea of \emph{check node (CN) doping} of regular SC-LDPC codes, here we employ variable nod...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	173,085
1904.12732	Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss	Deep learning techniques are recently being used in fundus image analysis and diabetic retinopathy detection. Microaneurysms are an important indicator of diabetic retinopathy progression. We introduce a two-stage deep learning approach for microaneurysms segmentation using multiple scales of the input with selective s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	129,208
2202.00677	An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation	The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks. In this paper, we introduce a novel regularization strategy involving interpolation-based mixing for semi-supervised medical image segmentation. The proposed method is a new consistency regularization strategy that encour...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	278,230
2305.00889	The Impact of the Geometric Properties of the Constraint Set in Safe Optimization with Bandit Feedback	We consider a safe optimization problem with bandit feedback in which an agent sequentially chooses actions and observes responses from the environment, with the goal of maximizing an arbitrary function of the response while respecting stage-wise constraints. We propose an algorithm for this problem, and study how the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	361,479
2010.14454	An isogeometric finite element formulation for geometrically exact Timoshenko beams with extensible directors	An isogeometric finite element formulation for geometrically and materially nonlinear Timoshenko beams is presented, which incorporates in-plane deformation of the cross-section described by two extensible director vectors. Since those directors belong to the space ${\Bbb R}^3$, a configuration can be additively update...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	203,448
2402.17133	SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution	Diffusion-based super-resolution (SR) models have recently garnered significant attention due to their potent restoration capabilities. But conventional diffusion models perform noise sampling from a single distribution, constraining their ability to handle real-world scenes and complex textures across semantic regions...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	432,852
0905.3771	Memory Retrieved from Single Neurons	The paper examines the problem of accessing a vector memory from a single neuron in a Hebbian neural network. It begins with the review of the author's earlier method, which is different from the Hopfield model in that it recruits neighboring neurons by spreading activity, making it possible for single or group of neur...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	3,755
2411.01035	Provable Length Generalization in Sequence Prediction via Spectral Filtering	We consider the problem of length generalization in sequence prediction. We define a new metric of performance in this setting -- the Asymmetric-Regret -- which measures regret against a benchmark predictor with longer context length than available to the learner. We continue by studying this concept through the lens o...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	504,884
2209.05155	Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach	Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calcu...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	317,012
2309.06239	Risk-Aware Reinforcement Learning through Optimal Transport Theory	In the dynamic and uncertain environments where reinforcement learning (RL) operates, risk management becomes a crucial factor in ensuring reliable decision-making. Traditional RL approaches, while effective in reward optimization, often overlook the landscape of potential risks. In response, this paper pioneers the in...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	391,358
2207.06058	Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras	This paper presents a visual SLAM system that uses both points and lines for robust camera localization, and simultaneously performs a piece-wise planar reconstruction (PPR) of the environment to provide a structural map in real-time. One of the biggest challenges in parallel tracking and mapping with a monocular camer...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	307,752
2409.14016	Enhancing Multivariate Time Series-based Solar Flare Prediction with Multifaceted Preprocessing and Contrastive Learning	Accurate solar flare prediction is crucial due to the significant risks that intense solar flares pose to astronauts, space equipment, and satellite communication systems. Our research enhances solar flare prediction by utilizing advanced data preprocessing and classification methods on a multivariate time series-based...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	490,286
1707.09350	Centrality measures for graphons: Accounting for uncertainty in networks	As relational datasets modeled as graphs keep increasing in size and their data-acquisition is permeated by uncertainty, graph-based analysis techniques can become computationally and conceptually challenging. In particular, node centrality measures rely on the assumption that the graph is perfectly known -- a premise ...	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	77,985
1708.00980	CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images	With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,311
1203.2024	A Greedy Link Scheduler for Wireless Networks with Fading Channels	We consider the problem of link scheduling for wireless networks with fading channels, where the link rates are varying with time. Due to the high computational complexity of the throughput optimal scheduler, we provide a low complexity greedy link scheduler GFS, with provable performance guarantees. We show that the p...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	14,804
2409.14197	Advancing Employee Behavior Analysis through Synthetic Data: Leveraging ABMs, GANs, and Statistical Models for Enhanced Organizational Efficiency	Success in todays data-driven corporate climate requires a deep understanding of employee behavior. Companies aim to improve employee satisfaction, boost output, and optimize workflow. This research study delves into creating synthetic data, a powerful tool that allows us to comprehensively understand employee performa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	490,368
2405.18952	Are You Sure? Rank Them Again: Repeated Ranking For Better Preference Datasets	Training Large Language Models (LLMs) with Reinforcement Learning from AI Feedback (RLAIF) aligns model outputs more closely with human preferences. This involves an evaluator model ranking multiple candidate responses to user prompts. However, the rankings from popular evaluator models such as GPT-4 can be inconsisten...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	458,683
2009.10616	Mosques Smart Domes System using Machine Learning Algorithms	Millions of mosques around the world are suffering some problems such as ventilation and difficulty getting rid of bacteria, especially in rush hours where congestion in mosques leads to air pollution and spread of bacteria, in addition to unpleasant odors and to a state of discomfort during the pray times, where in mo...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	196,934
2402.19059	VEnvision3D: A Synthetic Perception Dataset for 3D Multi-Task Model Research	Developing a unified multi-task foundation model has become a critical challenge in computer vision research. In the current field of 3D computer vision, most datasets only focus on single task, which complicates the concurrent training requirements of various downstream tasks. In this paper, we introduce VEnvision3D, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,667
2403.15616	Balancing Fairness and Efficiency in Energy Resource Allocations	Bringing fairness to energy resource allocation remains a challenge, due to the complexity of system structures and economic interdependencies among users and system operators' decision-making. The rise of distributed energy resources has introduced more diverse heterogeneous user groups, surpassing the capabilities of...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	true	440,662
1609.03815	A Unified Gender-Aware Age Estimation	Human age estimation has attracted increasing researches due to its wide applicability in such as security monitoring and advertisement recommendation. Although a variety of methods have been proposed, most of them focus only on the age-specific facial appearance. However, biological researches have shown that not only...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	60,933
2407.12758	Mutual Information Guided Optimal Transport for Unsupervised Visible-Infrared Person Re-identification	Unsupervised visible infrared person re-identification (USVI-ReID) is a challenging retrieval task that aims to retrieve cross-modality pedestrian images without using any label information. In this task, the large cross-modality variance makes it difficult to generate reliable cross-modality labels, and the lack of an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	474,060
2210.15923	DELFI: Deep Mixture Models for Long-term Air Quality Forecasting in the Delhi National Capital Region	The identification and control of human factors in climate change is a rapidly growing concern and robust, real-time air-quality monitoring and forecasting plays a critical role in allowing effective policy formulation and implementation. This paper presents DELFI, a novel deep learning-based mixture model to make effe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,141
2412.10916	Distributed Shape Learning of Complex Objects Using Gaussian Kernel	This paper addresses distributed learning of a complex object for multiple networked robots based on distributed optimization and kernel-based support vector machine. In order to overcome a fundamental limitation of polynomial kernels assumed in our antecessor, we employ Gaussian kernel as a kernel function for classif...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	517,167
2308.07269	EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models	Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. To this end, many knowledge editing approaches for LLMs have emerged -- aiming to subtly inject/edit updated knowledge or...	false	false	false	false	true	true	true	false	true	false	false	true	false	false	false	false	false	false	385,449
2201.11391	Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages	Nowadays, the interest in code-mixing has become ubiquitous in Natural Language Processing (NLP); however, not much attention has been given to address this phenomenon for Speech Translation (ST) task. This can be solely attributed to the lack of code-mixed ST task labelled data. Thus, we introduce Prabhupadavani, whic...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	277,287
2104.02063	Robust Tube-Based Decentralized Nonlinear Model Predictive Control of an Autonomous Tractor-Trailer System	This paper addresses the trajectory tracking problem of an autonomous tractor-trailer system by using a decentralized control approach. A fully decentralized model predictive controller is designed in which interactions between subsystems are neglected and assumed to be perturbations to each other. In order to have a r...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	228,581
2102.04034	The Autonomous Siemens Tram	This paper presents the Autonomous Siemens Tram that was publicly demonstrated in Potsdam, Germany during the InnoTrans 2018 exhibition. The system was built on a Siemens Combino tram and used a multi-modal sensor suite to localize the vehicle, and to detect and respond to traffic signals and obstacles. An overview of ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	218,970
1907.05653	VarGNet: Variable Group Convolutional Neural Network for Efficient Embedded Computing	In this paper, we propose a novel network design mechanism for efficient embedded computing. Inspired by the limited computing patterns, we propose to fix the number of channels in a group convolution, instead of the existing practice that fixing the total group numbers. Our solution based network, named Variable Group...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	138,424
2312.06562	On Meta-Prompting	Modern generative language models are capable of interpreting input strings as instructions, or prompts, and carry out tasks based on them. Many approaches to prompting and pre-training these models involve the automated generation of these prompts: meta-prompting, or prompting to obtain prompts. We propose a theoretic...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	414,570
1804.04230	Herdable Systems Over Signed, Directed Graphs	This paper considers the notion of herdability, a set-based reachability condition, which asks whether the state of a system can be controlled to be element-wise larger than a non-negative threshold. The basic theory of herdable systems is presented, including a necessary and sufficient condition for herdability. This ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	94,789
2412.10706	SHIFT Planner: Speedy Hybrid Iterative Field and Segmented Trajectory Optimization with IKD-tree for Uniform Lightweight Coverage	This paper introduces a comprehensive planning and navigation framework that address these limitations by integrating semantic mapping, adaptive coverage planning, dynamic obstacle avoidance and precise trajectory tracking. Our framework begins by generating panoptic occupancy local semantic maps and accurate localizat...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	517,064
2411.07654	Spike Talk in Power Electronic Grids -- Leveraging Post Moore's Computing Laws	Emerging distributed generation demands highly reliable and resilient coordinating control in microgrids. To improve on these aspects, spiking neural network is leveraged, as a grid-edge intelligence tool to establish a talkative infrastructure, Spike Talk, expediting coordination in next-generation microgrids without ...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	false	true	507,633
2205.15714	Attribute Exploration with Multiple Contradicting Partial Experts	Attribute exploration is a method from Formal Concept Analysis (FCA) that helps a domain expert discover structural dependencies in knowledge domains which can be represented as formal contexts (cross tables of objects and attributes). In this paper we present an extension of attribute exploration that allows for a gro...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	299,842
2204.12477	Digital Twins for Dynamic Management of Blockchain Systems	Blockchain systems are challenged by the so-called Trilemma tradeoff: decentralization, scalability and security. Infrastructure and node configuration, choice of the Consensus Protocol and complexity of the application transactions are cited amongst the factors that affect the tradeoffs balance. Given that Blockchains...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	293,487
2005.01656	Categorized Bandits	We introduce a new stochastic multi-armed bandit setting where arms are grouped inside ``ordered'' categories. The motivating example comes from e-commerce, where a customer typically has a greater appetence for items of a specific well-identified but unknown category than any other one. We introduce three concepts of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	175,646
2011.14475	An Artificial Consciousness Model and its relations with Philosophy of Mind	This work seeks to study the beneficial properties that an autonomous agent can obtain by implementing a cognitive architecture similar to the one of conscious beings. Along this document, a conscious model of autonomous agent based in a global workspace architecture is presented. We describe how this agent is viewed f...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	208,787
2406.18817	Correspondence-Free Non-Rigid Point Set Registration Using Unsupervised Clustering Analysis	This paper presents a novel non-rigid point set registration method that is inspired by unsupervised clustering analysis. Unlike previous approaches that treat the source and target point sets as separate entities, we develop a holistic framework where they are formulated as clustering centroids and clustering members,...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	468,184
2402.14750	Testing Spacecraft Formation Flying with Crazyflie Drones as Satellite Surrogates	As the space domain becomes increasingly congested, autonomy is proposed as one approach to enable small numbers of human ground operators to manage large constellations of satellites and tackle more complex missions such as on-orbit or in-space servicing, assembly, and manufacturing. One of the biggest challenges in d...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	431,819
2302.04001	Leveraging Summary Guidance on Medical Report Summarization	This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50K, 16K and 378K pairs of report and summary that are derived from MIMIC-III, respectively. We implement convincing baselines of automated abstractive summarization on the proposed datasets with pre-t...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	344,551
2111.15300	TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain Augmentation	Due to the difficulty of obtaining ground-truth labels, learning from virtual-world datasets is of great interest for real-world applications like semantic segmentation. From domain adaptation perspective, the key challenge is to learn domain-agnostic representation of the inputs in order to benefit from virtual data. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,884
2204.10636	Ontology-based system to support industrial system design for aircraft assembly	The development of an aircraft industrial system is a complex process which faces the challenge of digital discontinuity in multidisciplinary engineering due to various interfaces between different digital tools, leading to extra development time and costs. This paper proposes an ontology-based system, aiming at functi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	292,862
2211.13815	Using Selective Masking as a Bridge between Pre-training and Fine-tuning	Pre-training a language model and then fine-tuning it for downstream tasks has demonstrated state-of-the-art results for various NLP tasks. Pre-training is usually independent of the downstream task, and previous works have shown that this pre-training alone might not be sufficient to capture the task-specific nuances....	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	332,609
2406.01961	Exploring Real World Map Change Generalization of Prior-Informed HD Map Prediction Models	Building and maintaining High-Definition (HD) maps represents a large barrier to autonomous vehicle deployment. This, along with advances in modern online map detection models, has sparked renewed interest in the online mapping problem. However, effectively predicting online maps at a high enough quality to enable safe...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	460,548
1906.06110	Towards Compact and Robust Deep Neural Networks	Deep neural networks have achieved impressive performance in many applications but their large number of parameters lead to significant computational and storage overheads. Several recent works attempt to mitigate these overheads by designing compact networks using pruning of connections. However, we observe that most ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	135,216
0902.4250	Fundamental limit of sample generalized eigenvalue based detection of signals in noise using relatively few signal-bearing and noise-only samples	The detection problem in statistical signal processing can be succinctly formulated: Given m (possibly) signal bearing, n-dimensional signal-plus-noise snapshot vectors (samples) and N statistically independent n-dimensional noise-only snapshot vectors, can one reliably infer the presence of a signal? This problem aris...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,228
1904.04406	Context-Aware Query Selection for Active Learning in Event Recognition	Activity recognition is a challenging problem with many practical applications. In addition to the visual features, recent approaches have benefited from the use of context, e.g., inter-relationships among the activities and objects. However, these approaches require data to be labeled, entirely available beforehand, a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	127,027
2112.02021	Low-Resolution Massive MIMO Under Hardware Power Consumption Constraints	We consider a fully digital massive multiple-input multiple-output architecture with low-resolution analog-to-digital/digital-to-analog converters (ADCs/DACs) at the base station (BS) and analyze the performance trade-off between the number of BS antennas, the resolution of the ADCs/DACs, and the bandwidth. Assuming a ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	269,705
2208.03552	Inpainting at Modern Camera Resolution by Guided PatchMatch with Auto-Curation	Recently, deep models have established SOTA performance for low-resolution image inpainting, but they lack fidelity at resolutions associated with modern cameras such as 4K or more, and for large holes. We contribute an inpainting benchmark dataset of photos at 4K and above representative of modern sensors. We demonstr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	311,818

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