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
1812.06210	A General Approach to Adding Differential Privacy to Iterative Training Procedures	In this work we address the practical challenges of training machine learning models on privacy-sensitive datasets by introducing a modular approach that minimizes changes to training algorithms, provides a variety of configuration strategies for the privacy mechanism, and then isolates and simplifies the critical logi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	116,563
1906.11613	Mind2Mind : transfer learning for GANs	Training generative adversarial networks (GANs) on high quality (HQ) images involves important computing resources. This requirement represents a bottleneck for the development of applications of GANs. We propose a transfer learning technique for GANs that significantly reduces training time. Our approach consists of f...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	136,707
2201.06153	Reconstruction of Incomplete Wildfire Data using Deep Generative Models	We present our submission to the Extreme Value Analysis 2021 Data Challenge in which teams were asked to accurately predict distributions of wildfire frequency and size within spatio-temporal regions of missing data. For the purpose of this competition we developed a variant of the powerful variational autoencoder mode...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	275,628
2307.04593	DWA: Differential Wavelet Amplifier for Image Super-Resolution	This work introduces Differential Wavelet Amplifier (DWA), a drop-in module for wavelet-based image Super-Resolution (SR). DWA invigorates an approach recently receiving less attention, namely Discrete Wavelet Transformation (DWT). DWT enables an efficient image representation for SR and reduces the spatial area of its...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	378,456
1711.08621	Counterfactual Learning for Machine Translation: Degeneracies and Solutions	Counterfactual learning is a natural scenario to improve web-based machine translation services by offline learning from feedback logged during user interactions. In order to avoid the risk of showing inferior translations to users, in such scenarios mostly exploration-free deterministic logging policies are in place. ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	85,243
1309.0302	Unmixing Incoherent Structures of Big Data by Randomized or Greedy Decomposition	Learning big data by matrix decomposition always suffers from expensive computation, mixing of complicated structures and noise. In this paper, we study more adaptive models and efficient algorithms that decompose a data matrix as the sum of semantic components with incoherent structures. We firstly introduce "GO decom...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	26,775
2205.12113	The Curious Case of Control	Children acquiring English make systematic errors on subject control sentences even after they have reached near-adult competence (C. Chomsky, 1969), possibly due to heuristics based on semantic roles (Maratsos, 1974). Given the advanced fluency of large generative language models, we ask whether model outputs are cons...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	298,407
2008.13748	Reinforced Axial Refinement Network for Monocular 3D Object Detection	Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. This is an ill-posed problem with a major difficulty lying in the information loss by depth-agnostic cameras. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	193,926
2202.05465	WAD-CMSN: Wasserstein Distance based Cross-Modal Semantic Network for Zero-Shot Sketch-Based Image Retrieval	Zero-shot sketch-based image retrieval (ZSSBIR), as a popular studied branch of computer vision, attracts wide attention recently. Unlike sketch-based image retrieval (SBIR), the main aim of ZSSBIR is to retrieve natural images given free hand-drawn sketches that may not appear during training. Previous approaches used...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	279,887
1809.05274	Dueling Bandits with Qualitative Feedback	We formulate and study a novel multi-armed bandit problem called the qualitative dueling bandit (QDB) problem, where an agent observes not numeric but qualitative feedback by pulling each arm. We employ the same regret as the dueling bandit (DB) problem where the duel is carried out by comparing the qualitative feedbac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	107,760
2305.12473	Continually Improving Extractive QA via Human Feedback	We study continually improving an extractive question answering (QA) system via human user feedback. We design and deploy an iterative approach, where information-seeking users ask questions, receive model-predicted answers, and provide feedback. We conduct experiments involving thousands of user interactions under div...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	366,018
1906.03826	Network Implosion: Effective Model Compression for ResNets via Static Layer Pruning and Retraining	Residual Networks with convolutional layers are widely used in the field of machine learning. Since they effectively extract features from input data by stacking multiple layers, they can achieve high accuracy in many applications. However, the stacking of many layers raises their computation costs. To address this pro...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	134,514
2006.10731	Spin-Weighted Spherical CNNs	Learning equivariant representations is a promising way to reduce sample and model complexity and improve the generalization performance of deep neural networks. The spherical CNNs are successful examples, producing SO(3)-equivariant representations of spherical inputs. There are two main types of spherical CNNs. The f...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	182,989
1308.4839	Diversification Based Static Index Pruning - Application to Temporal Collections	Nowadays, web archives preserve the history of large portions of the web. As medias are shifting from printed to digital editions, accessing these huge information sources is drawing increasingly more attention from national and international institutions, as well as from the research community. These collections are i...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	26,570
2404.13159	Equivariant Imaging for Self-supervised Hyperspectral Image Inpainting	Hyperspectral imaging (HSI) is a key technology for earth observation, surveillance, medical imaging and diagnostics, astronomy and space exploration. The conventional technology for HSI in remote sensing applications is based on the push-broom scanning approach in which the camera records the spectral image of a strip...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	448,187
1812.05333	Computation Over NOMA: Improved Achievable Rate Through Sub-Function Superposition	Massive numbers of nodes will be connected in future wireless networks. This brings great difficulty to collect a large amount of data. Instead of collecting the data individually, computation over multi-access channel (CoMAC) provides an intelligent solution by computing a desired function over the air based on the si...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	116,398
1811.00111	On finite-time and fixed-time consensus algorithms for dynamic networks switching among disconnected digraphs	The aim of this paper is to analyze a class of consensus algorithms with finite-time or fixed-time convergence for dynamic networks formed by agents with first-order dynamics. In particular, in the analyzed class a single evaluation of a nonlinear function of the consensus error is performed per each node. The classica...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	111,997
1906.06425	Principled Frameworks for Evaluating Ethics in NLP Systems	We critique recent work on ethics in natural language processing. Those discussions have focused on data collection, experimental design, and interventions in modeling. But we argue that we ought to first understand the frameworks of ethics that are being used to evaluate the fairness and justice of algorithmic systems...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	135,292
2005.02431	Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System	We investigate how automated, data-driven, personalized feedback in a large-scale intelligent tutoring system (ITS) improves student learning outcomes. We propose a machine learning approach to generate personalized feedback, which takes individual needs of students into account. We utilize state-of-the-art machine lea...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	175,864
2307.05784	EgoAdapt: A multi-stream evaluation study of adaptation to real-world egocentric user video	In egocentric action recognition a single population model is typically trained and subsequently embodied on a head-mounted device, such as an augmented reality headset. While this model remains static for new users and environments, we introduce an adaptive paradigm of two phases, where after pretraining a population ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	378,852
2306.03733	A Novel Approach To User Agent String Parsing For Vulnerability Analysis Using Mutli-Headed Attention	The increasing reliance on the internet has led to the proliferation of a diverse set of web-browsers and operating systems (OSs) capable of browsing the web. User agent strings (UASs) are a component of web browsing that are transmitted with every Hypertext Transfer Protocol (HTTP) request. They contain information ab...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	371,456
1212.6216	Generating Motion Patterns Using Evolutionary Computation in Digital Soccer	Dribbling an opponent player in digital soccer environment is an important practical problem in motion planning. It has special complexities which can be generalized to most important problems in other similar Multi Agent Systems. In this paper, we propose a hybrid computational geometry and evolutionary computation ap...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	20,629
1209.5698	Sampling Error Analysis and Properties of Non-bandlimited Signals That Are Reconstructed by Generalized Sinc Functions	Recently efforts have been made to use generalized sinc functions to perfectly reconstruct various kinds of non-bandlimited signals. As a consequence, perfect reconstruction sampling formulas have been established using such generalized sinc functions. This article studies the error of the reconstructed non-bandlimited...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,758
2403.18827	Bridging Generative Networks with the Common Model of Cognition	This article presents a theoretical framework for adapting the Common Model of Cognition to large generative network models within the field of artificial intelligence. This can be accomplished by restructuring modules within the Common Model into shadow production systems that are peripheral to a central production sy...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	442,096
1106.0041	Assessing the consistency of community structure in complex networks	In recent years, community structure has emerged as a key component of complex network analysis. As more data has been collected, researchers have begun investigating changing community structure across multiple networks. Several methods exist to analyze changing communities, but most of these are limited to evolution ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	10,618
2112.08663	MAVE: A Product Dataset for Multi-source Attribute Value Extraction	Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product ranking, retrieval and recommendations. While in the real world, the attribute values of...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	271,888
2109.03153	An eXtended Finite Element Method Implementation in COMSOL Multiphysics: Solid Mechanics	This paper presents the first time implementation of the eXtended Finite Element Method (XFEM) in the general purpose commercial software COMSOL Multiphysics. An enrichment strategy is proposed, consistent with the structure of the software. To this end, for each set of enrichment functions, an additional Solid Mechani...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	253,969
1208.4080	A Simple Proof of Threshold Saturation for Coupled Vector Recursions	Convolutional low-density parity-check (LDPC) codes (or spatially-coupled codes) have now been shown to achieve capacity on binary-input memoryless symmetric channels. The principle behind this surprising result is the threshold-saturation phenomenon, which is defined by the belief-propagation threshold of the spatiall...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,174
2409.17107	Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networks	In this paper, we provide a non-asymptotic analysis of the convergence of the stochastic gradient Hamiltonian Monte Carlo (SGHMC) algorithm to a target measure in Wasserstein-1 and Wasserstein-2 distance. Crucially, compared to the existing literature on SGHMC, we allow its stochastic gradient to be discontinuous. This...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	491,651
1507.02150	SAR Imaging of Moving Target based on Knowledge-aided Two-dimensional Autofocus	Due to uncertainty on target's motion, the range cell migration (RCM) and azimuth phase error (APE) of moving targets can't be completely compensated in synthetic aperture radar (SAR) processing. Therefore, moving targets often appear two-dimensional (2-D) defocused in SAR images. In this paper, a 2-D autofocus method ...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	44,948
2103.10346	A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning	Recent advances in distributed learning raise environmental concerns due to the large energy needed to train and move data to/from data centers. Novel paradigms, such as federated learning (FL), are suitable for decentralized model training across devices or silos that simultaneously act as both data producers and lear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	225,427
2309.13869	PRiSM: Enhancing Low-Resource Document-Level Relation Extraction with Relation-Aware Score Calibration	Document-level relation extraction (DocRE) aims to extract relations of all entity pairs in a document. A key challenge in DocRE is the cost of annotating such data which requires intensive human effort. Thus, we investigate the case of DocRE in a low-resource setting, and we find that existing models trained on low da...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	394,384
2410.04472	Collapsed Language Models Promote Fairness	To mitigate societal biases implicitly encoded in recent successful pretrained language models, a diverse array of approaches have been proposed to encourage model fairness, focusing on prompting, data augmentation, regularized fine-tuning, and more. Despite the development, it is nontrivial to reach a principled under...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	495,298
2201.05503	Global-threshold and backbone high-resolution weather radar networks are significantly complementary in a watershed	There are several criteria for building up networks from time series related to different points in geographical space. The most used criterion is the Global-Threshold (GT). Using a weather radar dataset, this paper shows that the Backbone (BB) - a local-threshold criterion - generates networks whose geographical confi...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	275,406
1808.01621	Mining CFD Rules on Big Data	Current conditional functional dependencies (CFDs) discovery algorithms always need a well-prepared training data set. This makes them difficult to be applied on large datasets which are always in low-quality. To handle the volume issue of big data, we develop the sampling algorithms to obtain a small representative tr...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	104,607
1510.01776	Capacity-Achieving Rate-Compatible Polar Codes	We present a method of constructing rate-compatible polar codes that are capacity-achieving with low-complexity sequential decoders. The proposed code construction allows for incremental retransmissions at different rates in order to adapt to channel conditions. The main idea of the construction exploits the common cha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	47,657
2103.02644	Compute and memory efficient universal sound source separation	Recent progress in audio source separation lead by deep learning has enabled many neural network models to provide robust solutions to this fundamental estimation problem. In this study, we provide a family of efficient neural network architectures for general purpose audio source separation while focusing on multiple ...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	223,020
2502.08786	MRUCT: Mixed Reality Assistance for Acupuncture Guided by Ultrasonic Computed Tomography	Chinese acupuncture practitioners primarily depend on muscle memory and tactile feedback to insert needles and accurately target acupuncture points, as the current workflow lacks imaging modalities and visual aids. Consequently, new practitioners often learn through trial and error, requiring years of experience to bec...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	533,164
2409.17046	Detecting Temporal Ambiguity in Questions	Detecting and answering ambiguous questions has been a challenging task in open-domain question answering. Ambiguous questions have different answers depending on their interpretation and can take diverse forms. Temporally ambiguous questions are one of the most common types of such questions. In this paper, we introdu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	491,627
1811.03195	Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering	Consider an instance of Euclidean $k$-means or $k$-medians clustering. We show that the cost of the optimal solution is preserved up to a factor of $(1+\varepsilon)$ under a projection onto a random $O(\log(k / \varepsilon) / \varepsilon^2)$-dimensional subspace. Further, the cost of every clustering is preserved withi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	112,776
2403.02241	Neural Redshift: Random Networks are not Random Functions	Our understanding of the generalization capabilities of neural networks (NNs) is still incomplete. Prevailing explanations are based on implicit biases of gradient descent (GD) but they cannot account for the capabilities of models from gradient-free methods nor the simplicity bias recently observed in untrained networ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	434,739
2412.15250	An Enhanced Text Compression Approach Using Transformer-based Language Models	Text compression shrinks textual data while keeping crucial information, eradicating constraints on storage, bandwidth, and computational efficacy. The integration of lossless compression techniques with transformer-based text decompression has received negligible attention, despite the increasing volume of English tex...	false	false	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	519,010
2205.06840	IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations	What is the relation between a word and its description, or a word and its embedding? Both descriptions and embeddings are semantic representations of words. But, what information from the original word remains in these representations? Or more importantly, which information about a word do these two representations sh...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	296,371
2203.02540	Evolving symbolic density functionals	Systematic development of accurate density functionals has been a decades-long challenge for scientists. Despite the emerging application of machine learning (ML) in approximating functionals, the resulting ML functionals usually contain more than tens of thousands parameters, which makes a huge gap in the formulation ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	283,776
2001.00621	BehavDT: A Behavioral Decision Tree Learning to Build User-Centric Context-Aware Predictive Model	This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as that of decision tree is considered as one of the most popular classification techniques, which can be used ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	159,275
1806.01050	On the Computational Complexity of Blind Detection of Binary Linear Codes	In this work, we study the computational complexity of the Minimum Distance Code Detection problem. In this problem, we are given a set of noisy codeword observations and we wish to find a code in a set of linear codes $\mathcal{C}$ of a given dimension $k$, for which the sum of distances between the observations and t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	99,471
1912.01875	3D Hand Pose Estimation via Regularized Graph Representation Learning	This paper addresses the problem of 3D hand pose estimation from a monocular RGB image. While previous methods have shown great success, the structure of hands has not been fully exploited, which is critical in pose estimation. To this end, we propose a regularized graph representation learning under a conditional adve...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	156,201
2307.06576	Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations	Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent works primarily focus on using advanced natural language processing techniques to extract semantic information from rich textual data, employing content-based methods derive...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	379,113
2201.03696	Stratified Graph Spectra	In classic graph signal processing, given a real-valued graph signal, its graph Fourier transform is typically defined as the series of inner products between the signal and each eigenvector of the graph Laplacian. Unfortunately, this definition is not mathematically valid in the cases of vector-valued graph signals wh...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	274,909
2203.15781	Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information	The impact of Vehicle-to-Everything (V2X) communications on platoon control performance is investigated. Platoon control is essentially a sequential stochastic decision problem (SSDP), which can be solved by Deep Reinforcement Learning (DRL) to deal with both the control constraints and uncertainty in the platoon leadi...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	288,532
2309.09043	Forward Invariance in Neural Network Controlled Systems	We present a framework based on interval analysis and monotone systems theory to certify and search for forward invariant sets in nonlinear systems with neural network controllers. The framework (i) constructs localized first-order inclusion functions for the closed-loop system using Jacobian bounds and existing neural...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	392,446
2201.11524	Probabilistic Query Evaluation with Bag Semantics	We study the complexity of evaluating queries on probabilistic databases under bag semantics. We focus on self-join free conjunctive queries, and probabilistic databases where occurrences of different facts are independent, which is the natural generalization of tuple-independent probabilistic databases to the bag sema...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	277,323
1912.02493	Ordinal Bayesian Optimisation	Bayesian optimisation is a powerful tool to solve expensive black-box problems, but fails when the stationary assumption made on the objective function is strongly violated, which is the case in particular for ill-conditioned or discontinuous objectives. We tackle this problem by proposing a new Bayesian optimisation f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	156,364
2001.05763	GMD-Based Hybrid Beamforming for Large Reconfigurable Intelligent Surface Assisted Millimeter-Wave Massive MIMO	Reconfigurable intelligent surface (RIS) is considered to be an energy-efficient approach to reshape the wireless environment for improved throughput. Its passive feature greatly reduces the energy consumption, which makes RIS a promising technique for enabling the future smart city. Existing beamforming designs for RI...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	160,633
1809.10443	New Radio Beam-based Access to Unlicensed Spectrum: Design Challenges and Solutions	This paper elaborates on the design challenges, opportunities, and solutions for New Radio-based access to Unlicensed spectrum (NR-U) by taking into account the beam-based transmissions and the worldwide regulatory requirements. NR-U intends to expand the applicability of 5th generation New Radio access technology to s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	108,913
2103.02567	Advanced control based on Recurrent Neural Networks learned using Virtual Reference Feedback Tuning and application to an Electronic Throttle Body (with supplementary material)	In this paper the application of Virtual Reference Feedback Tuning (VRFT) for control of nonlinear systems with regulators defined by Echo State Networks (ESN) and Long Short Term Memory (LSTM) networks is investigated. The capability of this class of regulators of constraining the control variable is pointed out and a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	223,005
2202.07184	On the Origins of the Block Structure Phenomenon in Neural Network Representations	Recent work has uncovered a striking phenomenon in large-capacity neural networks: they contain blocks of contiguous hidden layers with highly similar representations. This block structure has two seemingly contradictory properties: on the one hand, its constituent layers exhibit highly similar dominant first principal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	280,464
2411.15113	Efficient Pruning of Text-to-Image Models: Insights from Pruning Stable Diffusion	As text-to-image models grow increasingly powerful and complex, their burgeoning size presents a significant obstacle to widespread adoption, especially on resource-constrained devices. This paper presents a pioneering study on post-training pruning of Stable Diffusion 2, addressing the critical need for model compress...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	510,444
1306.6659	Millimeter Wave Beamforming for Wireless Backhaul and Access in Small Cell Networks	Recently, there has been considerable interest in new tiered network cellular architectures, which would likely use many more cell sites than found today. Two major challenges will be i) providing backhaul to all of these cells and ii) finding efficient techniques to leverage higher frequency bands for mobile access an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,492
2406.03879	Decay Pruning Method: Smooth Pruning With a Self-Rectifying Procedure	Current structured pruning methods often result in considerable accuracy drops due to abrupt network changes and loss of information from pruned structures. To address these issues, we introduce the Decay Pruning Method (DPM), a novel smooth pruning approach with a self-rectifying mechanism. DPM consists of two key com...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	461,434
1706.01869	StreetStyle: Exploring world-wide clothing styles from millions of photos	Each day billions of photographs are uploaded to photo-sharing services and social media platforms. These images are packed with information about how people live around the world. In this paper we exploit this rich trove of data to understand fashion and style trends worldwide. We present a framework for visual discov...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	74,881
2206.08425	DialogueScript: Using Dialogue Agents to Produce a Script	We present a novel approach to generating scripts by using agents with different personality types. To manage character interaction in the script, we employ simulated dramatic networks. Automatic and human evaluation on multiple criteria shows that our approach outperforms a vanilla-GPT2-based baseline. We further intr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	303,127
1506.00246	Using Twitter to learn about the autism community	Considering the raising socio-economic burden of autism spectrum disorder (ASD), timely and evidence-driven public policy decision making and communication of the latest guidelines pertaining to the treatment and management of the disorder is crucial. Yet evidence suggests that policy makers and medical practitioners d...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	43,641
1804.07871	A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers	Lane change is a crucial vehicle maneuver which needs coordination with surrounding vehicles. Automated lane changing functions built on rule-based models may perform well under pre-defined operating conditions, but they may be prone to failure when unexpected situations are encountered. In our study, we proposed a Rei...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	95,622
2111.13295	Medial Spectral Coordinates for 3D Shape Analysis	In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects represented by surface meshes, their voxelized interiors, or surface point clouds. In part, this interest has been stimulated by the increased availability of RGBD cameras, and by applications of computer vision...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	268,249
2309.17058	Imagery Dataset for Condition Monitoring of Synthetic Fibre Ropes	Automatic visual inspection of synthetic fibre ropes (SFRs) is a challenging task in the field of offshore, wind turbine industries, etc. The presence of any defect in SFRs can compromise their structural integrity and pose significant safety risks. Due to the large size and weight of these ropes, it is often impractic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	395,613
1012.5956	A New Noncoherent Decoder for Wireless Network Coding	This work deals with the decoding aspect of wireless network coding in the canonical two-way relay channel where two senders exchange messages via a common relay and they receive the mixture of two messages. One of the recent works on wireless network coding was well explained by Katti \textit{et al.} in SIGCOMM'07. In...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	8,674
2404.13567	On the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysis	A major challenge in Explainable AI is in correctly interpreting activations of hidden neurons: accurate interpretations would help answer the question of what a deep learning system internally detects as relevant in the input, demystifying the otherwise black-box nature of deep learning systems. The state of the art i...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	448,348
2111.07228	Curriculum Learning for Vision-and-Language Navigation	Vision-and-Language Navigation (VLN) is a task where an agent navigates in an embodied indoor environment under human instructions. Previous works ignore the distribution of sample difficulty and we argue that this potentially degrade their agent performance. To tackle this issue, we propose a novel curriculum-based tr...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	266,313
2306.08553	Noise Stability Optimization for Finding Flat Minima: A Hessian-based Regularization Approach	The training of over-parameterized neural networks has received much study in recent literature. An important consideration is the regularization of over-parameterized networks due to their highly nonconvex and nonlinear geometry. In this paper, we study noise injection algorithms, which can regularize the Hessian of t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	373,450
1405.1499	NScale: Neighborhood-centric Large-Scale Graph Analytics in the Cloud	There is an increasing interest in executing complex analyses over large graphs, many of which require processing a large number of multi-hop neighborhoods or subgraphs. Examples include ego network analysis, motif counting, personalized recommendations, and others. These tasks are not well served by existing vertex-ce...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	32,885
2304.01828	Learning Stable and Robust Linear Parameter-Varying State-Space Models	This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models. The model parametrizations guarantee a priori that for all parameter values during training, the allowed models are stable in the contraction sense or have their Lipschitz constant bounded by a us...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	356,220
2208.08464	CTRL: Clustering Training Losses for Label Error Detection	In supervised machine learning, use of correct labels is extremely important to ensure high accuracy. Unfortunately, most datasets contain corrupted labels. Machine learning models trained on such datasets do not generalize well. Thus, detecting their label errors can significantly increase their efficacy. We propose a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	313,373
2010.05903	PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation	Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning. Surprisingly, a very promising direction, using pretrained deep features, has been mostly overlooked. In this paper, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	200,296
2003.11561	Predicting Legal Proceedings Status: Approaches Based on Sequential Text Data	The objective of this paper is to develop predictive models to classify Brazilian legal proceedings in three possible classes of status: (i) archived proceedings, (ii) active proceedings, and (iii) suspended proceedings. This problem's resolution is intended to assist public and private institutions in managing large p...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	169,641
1411.4156	Using Description Logics for RDF Constraint Checking and Closed-World Recognition	RDF and Description Logics work in an open-world setting where absence of information is not information about absence. Nevertheless, Description Logic axioms can be interpreted in a closed-world setting and in this setting they can be used for both constraint checking and closed-world recognition against information s...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	37,596
2309.00730	Tempestas ex machina: A review of machine learning methods for wavefront control	As we look to the next generation of adaptive optics systems, now is the time to develop and explore the technologies that will allow us to image rocky Earth-like planets; wavefront control algorithms are not only a crucial component of these systems, but can benefit our adaptive optics systems without requiring increa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	389,399
1708.07987	Stereo Matching With Color-Weighted Correlation, Hierarchical Belief Propagation And Occlusion Handling	In this paper, we contrive a stereo matching algorithm with careful handling of disparity, discontinuity and occlusion. This algorithm works a worldwide matching stereo model which is based on minimization of energy. The global energy comprises two terms, firstly the data term and secondly the smoothness term. The data...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	79,575
2108.03956	Distribution Grid Robust Operation under Forecast Uncertainties with Flexibility Estimation from Low Voltage Grids using a Monitoring and Control Equipment	Due to increased penetration of renewable resources in the distribution grid, the distribution system operator (DSO) faces increased challenges to maintain security and quality of supply. Since, a large proportion of renewables are intermittent generations, maintaining production and consumption balance of the electric...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	249,843
2405.02941	Boundary-aware Decoupled Flow Networks for Realistic Extreme Rescaling	Recently developed generative methods, including invertible rescaling network (IRN) based and generative adversarial network (GAN) based methods, have demonstrated exceptional performance in image rescaling. However, IRN-based methods tend to produce over-smoothed results, while GAN-based methods easily generate fake d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	451,984
1909.11362	Robust Monocular Edge Visual Odometry through Coarse-to-Fine Data Association	In this work, we propose a monocular visual odometry framework, which allows exploiting the best attributes of edge feature for illumination-robust camera tracking, while at the same time ameliorating the performance degradation of edge mapping. In the front-end, an ICP-based edge registration can provide robust motion...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	146,793
2210.01069	Dual-former: Hybrid Self-attention Transformer for Efficient Image Restoration	Recently, image restoration transformers have achieved comparable performance with previous state-of-the-art CNNs. However, how to efficiently leverage such architectures remains an open problem. In this work, we present Dual-former whose critical insight is to combine the powerful global modeling ability of self-atten...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	321,118
1909.13559	Towards a Framework for Observational Causality From Time Series: When Shannon Meets Turing	We propose a novel tensor-based formalism for inferring causal structures from time series. An information theoretical analysis of transfer entropy, shows that transfer entropy results from transmission of information over a set of communication channels. Tensors are the mathematical equivalents of these multi-channel ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	147,458
2211.04258	MetaLoc: Learning to Learn Wireless Localization	Existing localization methods that intensively leverage the environment-specific received signal strength (RSS) or channel state information (CSI) of wireless signals are rather accurate in certain environments. However, these methods, whether based on pure statistical signal processing or data-driven approaches, often...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	329,191
2206.12909	Tackling Asymmetric and Circular Sequential Social Dilemmas with Reinforcement Learning and Graph-based Tit-for-Tat	In many societal and industrial interactions, participants generally prefer their pure self-interest at the expense of the global welfare. Known as social dilemmas, this category of non-cooperative games offers situations where multiple actors should all cooperate to achieve the best outcome but greed and fear lead to ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	304,772
2405.14436	LARS-VSA: A Vector Symbolic Architecture For Learning with Abstract Rules	Human cognition excels at symbolic reasoning, deducing abstract rules from limited samples. This has been explained using symbolic and connectionist approaches, inspiring the development of a neuro-symbolic architecture that combines both paradigms. In parallel, recent studies have proposed the use of a "relational bot...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	456,426
2205.04989	Exploring Viable Algorithmic Options for Learning from Demonstration (LfD): A Parameterized Complexity Approach	The key to reconciling the polynomial-time intractability of many machine learning tasks in the worst case with the surprising solvability of these tasks by heuristic algorithms in practice seems to be exploiting restrictions on real-world data sets. One approach to investigating such restrictions is to analyze why heu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	295,808
2304.03906	InstructBio: A Large-scale Semi-supervised Learning Paradigm for Biochemical Problems	In the field of artificial intelligence for science, it is consistently an essential challenge to face a limited amount of labeled data for real-world problems. The prevailing approach is to pretrain a powerful task-agnostic model on a large unlabeled corpus but may struggle to transfer knowledge to downstream tasks. I...	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	356,988
2403.16263	Emotion Recognition from the perspective of Activity Recognition	Applications of an efficient emotion recognition system can be found in several domains such as medicine, driver fatigue surveillance, social robotics, and human-computer interaction. Appraising human emotional states, behaviors, and reactions displayed in real-world settings can be accomplished using latent continuous...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	440,953
2407.04029	Robust Learning under Hybrid Noise	Feature noise and label noise are ubiquitous in practical scenarios, which pose great challenges for training a robust machine learning model. Most previous approaches usually deal with only a single problem of either feature noise or label noise. However, in real-world applications, hybrid noise, which contains both f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	470,397
2404.00162	Modeling Large-Scale Walking and Cycling Networks: A Machine Learning Approach Using Mobile Phone and Crowdsourced Data	Walking and cycling are known to bring substantial health, environmental, and economic advantages. However, the development of evidence-based active transportation planning and policies has been impeded by significant data limitations, such as biases in crowdsourced data and representativeness issues of mobile phone da...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	442,784
1609.00448	Do Mathematicians, Economists and Biomedical Scientists Trace Large Topics More Strongly Than Physicists?	In this work, we extend our previous work on largeness tracing among physicists to other fields, namely mathematics, economics and biomedical science. Overall, the results confirm our previous discovery, indicating that scientists in all these fields trace large topics. Surprisingly, however, it seems that researchers ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	60,478
2210.17312	Neural network-based CUSUM for online change-point detection	Change-point detection, detecting an abrupt change in the data distribution from sequential data, is a fundamental problem in statistics and machine learning. CUSUM is a popular statistical method for online change-point detection due to its efficiency from recursive computation and constant memory requirement, and it ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,642
2010.02154	Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis: A Review	Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. Sometimes the symptoms of the disease increase so much they lead to the death of the patients. The dise...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	198,920
1904.08365	Information and Memory in Dynamic Resource Allocation	We propose a general framework, dubbed Stochastic Processing under Imperfect Information (SPII), to study the impact of information constraints and memories on dynamic resource allocation. The framework involves a Stochastic Processing Network (SPN) scheduling problem in which the scheduler may access the system state ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	128,036
1912.13469	Pricing Multi-Interval Dispatch under Uncertainty Part II: Generalization and Performance	Pricing multi-interval economic dispatch of electric power under operational uncertainty is considered in this two-part paper. Part I investigates dispatch-following incentives for generators under the locational marginal pricing (LMP) and temporal locational marginal pricing (TLMP) policies. Extending the theoretical ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	159,090
2410.18477	Monge-Ampere Regularization for Learning Arbitrary Shapes from Point Clouds	As commonly used implicit geometry representations, the signed distance function (SDF) is limited to modeling watertight shapes, while the unsigned distance function (UDF) is capable of representing various surfaces. However, its inherent theoretical shortcoming, i.e., the non-differentiability at the zero level set, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	501,902
2404.18928	Stylus: Automatic Adapter Selection for Diffusion Models	Beyond scaling base models with more data or parameters, fine-tuned adapters provide an alternative way to generate high fidelity, custom images at reduced costs. As such, adapters have been widely adopted by open-source communities, accumulating a database of over 100K adapters-most of which are highly customized with...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	true	450,438
2401.00690	Benchmarking Large Language Models on Controllable Generation under Diversified Instructions	While large language models (LLMs) have exhibited impressive instruction-following capabilities, it is still unclear whether and to what extent they can respond to explicit constraints that might be entailed in various instructions. As a significant aspect of LLM alignment, it is thus important to formulate such a spec...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	419,074
2307.12152	Real-Time Neural Video Recovery and Enhancement on Mobile Devices	As mobile devices become increasingly popular for video streaming, it's crucial to optimize the streaming experience for these devices. Although deep learning-based video enhancement techniques are gaining attention, most of them cannot support real-time enhancement on mobile devices. Additionally, many of these techni...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	381,157

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