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
2411.10275	4DPV: 4D Pet from Videos by Coarse-to-Fine Non-Rigid Radiance Fields	We present a coarse-to-fine neural deformation model to simultaneously recover the camera pose and the 4D reconstruction of an unknown object from multiple RGB sequences in the wild. To that end, our approach does not consider any pre-built 3D template nor 3D training data as well as controlled illumination conditions,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	508,577
2105.08997	When Deep Classifiers Agree: Analyzing Correlations between Learning Order and Image Statistics	Although a plethora of architectural variants for deep classification has been introduced over time, recent works have found empirical evidence towards similarities in their training process. It has been hypothesized that neural networks converge not only to similar representations, but also exhibit a notion of empiric...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	235,938
0903.2870	On $p$-adic Classification	A $p$-adic modification of the split-LBG classification method is presented in which first clusterings and then cluster centers are computed which locally minimise an energy function. The outcome for a fixed dataset is independent of the prime number $p$ with finitely many exceptions. The methods are applied to the con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	3,366
2304.13267	Bayesian Federated Learning: A Survey	Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the robustness and capabilities of existing FL methods are challenged by limited and dynamic data and conditions, complexities including heterogen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	360,522
1209.1426	Power Control and Multiuser Diversity for the Distributed Cognitive Uplink	This paper studies optimum power control and sum-rate scaling laws for the distributed cognitive uplink. It is first shown that the optimum distributed power control policy is in the form of a threshold based water-filling power control. Each secondary user executes the derived power control policy in a distributed fas...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,445
2311.03609	Testing RadiX-Nets: Advances in Viable Sparse Topologies	The exponential growth of data has sparked computational demands on ML research and industry use. Sparsification of hyper-parametrized deep neural networks (DNNs) creates simpler representations of complex data. Past research has shown that some sparse networks achieve similar performance as dense ones, reducing runtim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	405,910
2002.06170	Transformer on a Diet	Transformer has been widely used thanks to its ability to capture sequence information in an efficient way. However, recent developments, such as BERT and GPT-2, deliver only heavy architectures with a focus on effectiveness. In this paper, we explore three carefully-designed light Transformer architectures to figure o...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	164,103
1807.11264	Real Time Lidar and Radar High-Level Fusion for Obstacle Detection and Tracking with evaluation on a ground truth	- Both Lidars and Radars are sensors for obstacle detection. While Lidars are very accurate on obstacles positions and less accurate on their velocities, Radars are more precise on obstacles velocities and less precise on their positions. Sensor fusion between Lidar and Radar aims at improving obstacle detection using ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	104,145
2205.07162	GLaMa: Joint Spatial and Frequency Loss for General Image Inpainting	The purpose of image inpainting is to recover scratches and damaged areas using context information from remaining parts. In recent years, thanks to the resurgence of convolutional neural networks (CNNs), image inpainting task has made great breakthroughs. However, most of the work consider insufficient types of mask, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	296,500
2305.01579	Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise	Most existing retrieval-augmented language models (LMs) assume a naive dichotomy within a retrieved document set: query-relevance and irrelevance. Our work investigates a more challenging scenario in which even the "relevant" documents may contain misleading or incorrect information, causing conflict among the retrieve...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	361,724
2403.08284	MGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge Detection on Federated Learning	As a new distributed computing framework that can protect data privacy, federated learning (FL) has attracted more and more attention in recent years. It receives gradients from users to train the global model and releases the trained global model to working users. Nonetheless, the gradient inversion (GI) attack reflec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	437,277
1801.04003	Some techniques in density estimation	Density estimation is an interdisciplinary topic at the intersection of statistics, theoretical computer science and machine learning. We review some old and new techniques for bounding the sample complexity of estimating densities of continuous distributions, focusing on the class of mixtures of Gaussians and its subc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	88,194
2303.00276	Entire Space Learning Framework: Unbias Conversion Rate Prediction in Full Stages of Recommender System	Recommender system is an essential part of online services, especially for e-commerce platform. Conversion Rate (CVR) prediction in RS plays a significant role in optimizing Gross Merchandise Volume (GMV) goal of e-commerce. However, CVR suffers from well-known Sample Selection Bias (SSB) and Data Sparsity (DS) problem...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	348,541
1405.7348	ergm.graphlets: A Package for ERG Modeling Based on Graphlet Statistics	Exponential-family random graph models (ERGMs) are probabilistic network models that are parametrized by sufficient statistics based on structural (i.e., graph-theoretic) properties. The ergm package for the R statistical computing system is a collection of tools for the analysis of network data within an ERGM framewor...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	33,452
1211.2291	Sequentiality and Adaptivity Gains in Active Hypothesis Testing	Consider a decision maker who is responsible to collect observations so as to enhance his information in a speedy manner about an underlying phenomena of interest. The policies under which the decision maker selects sensing actions can be categorized based on the following two factors: i) sequential vs. non-sequential;...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	19,667
2302.00911	Conditional expectation with regularization for missing data imputation	Missing data frequently occurs in datasets across various domains, such as medicine, sports, and finance. In many cases, to enable proper and reliable analyses of such data, the missing values are often imputed, and it is necessary that the method used has a low root mean square error (RMSE) between the imputed and the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	343,405
2212.13196	Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers	Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to a specific form of design-by-analogy called bio-inspired design (BID). Although BID as...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	338,243
2501.07834	Flow: A Modular Approach to Automated Agentic Workflow Generation	Multi-agent frameworks powered by large language models (LLMs) have demonstrated great success in automated planning and task execution. However, the effective adjustment of Agentic workflows during execution has not been well-studied. A effective workflow adjustment is crucial, as in many real-world scenarios, the ini...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	524,528
1906.02108	Evaluating Explanation Methods for Deep Learning in Security	Deep learning is increasingly used as a building block of security systems. Unfortunately, neural networks are hard to interpret and typically opaque to the practitioner. The machine learning community has started to address this problem by developing methods for explaining the predictions of neural networks. While sev...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	133,940
2304.03384	Beyond NeRF Underwater: Learning Neural Reflectance Fields for True Color Correction of Marine Imagery	Underwater imagery often exhibits distorted coloration as a result of light-water interactions, which complicates the study of benthic environments in marine biology and geography. In this research, we propose an algorithm to restore the true color (albedo) in underwater imagery by jointly learning the effects of the m...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	356,784
2112.08175	A Factorization Approach for Motor Imagery Classification	Brain-computer interface uses brain signals to communicate with external devices without actual control. Many studies have been conducted to classify motor imagery based on machine learning. However, classifying imagery data with sparse spatial characteristics, such as single-arm motor imagery, remains a challenge. In ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	271,713
1810.13024	Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation	The standard approach to mitigate errors made by an automatic speech recognition system is to use confidence scores associated with each predicted word. In the simplest case, these scores are word posterior probabilities whilst more complex schemes utilise bi-directional recurrent neural network (BiRNN) models. A numbe...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	111,885
2310.12538	Solving Expensive Optimization Problems in Dynamic Environments with Meta-learning	Dynamic environments pose great challenges for expensive optimization problems, as the objective functions of these problems change over time and thus require remarkable computational resources to track the optimal solutions. Although data-driven evolutionary optimization and Bayesian optimization (BO) approaches have ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	401,061
2405.00491	On the Relevance of Byzantine Robust Optimization Against Data Poisoning	The success of machine learning (ML) has been intimately linked with the availability of large amounts of data, typically collected from heterogeneous sources and processed on vast networks of computing devices (also called {\em workers}). Beyond accuracy, the use of ML in critical domains such as healthcare and autono...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	450,944
2102.02831	Decoding of (Interleaved) Generalized Goppa Codes	Generalized Goppa codes are defined by a code locator set $\mathcal{L}$ of polynomials and a Goppa polynomial $G(x)$. When the degree of all code locator polynomials in $\mathcal{L}$ is one, generalized Goppa codes are classical Goppa codes. In this work, binary generalized Goppa codes are investigated. First, a parity...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	218,532
2003.02683	SketchyCOCO: Image Generation from Freehand Scene Sketches	We introduce the first method for automatic image generation from scene-level freehand sketches. Our model allows for controllable image generation by specifying the synthesis goal via freehand sketches. The key contribution is an attribute vector bridged Generative Adversarial Network called EdgeGAN, which supports hi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	167,012
2203.02038	Robust Counterexample-guided Optimization for Planning from Differentiable Temporal Logic	Signal temporal logic (STL) provides a powerful, flexible framework for specifying complex autonomy tasks; however, existing methods for planning based on STL specifications have difficulty scaling to long-horizon tasks and are not robust to external disturbances. In this paper, we present an algorithm for finding robu...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	283,602
2410.10209	Effi-Code: Unleashing Code Efficiency in Language Models	As the use of large language models (LLMs) for code generation becomes more prevalent in software development, it is critical to enhance both the efficiency and correctness of the generated code. Existing methods and models primarily focus on the correctness of LLM-generated code, ignoring efficiency. In this work, we ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	497,964
2408.06874	Leveraging Language Models for Emotion and Behavior Analysis in Education	The analysis of students' emotions and behaviors is crucial for enhancing learning outcomes and personalizing educational experiences. Traditional methods often rely on intrusive visual and physiological data collection, posing privacy concerns and scalability issues. This paper proposes a novel method leveraging large...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	480,376
1806.10950	A Decomposition-Based Many-Objective Evolutionary Algorithm with Local Iterative Update	Existing studies have shown that the conventional multi-objective evolutionary algorithms (MOEAs) based on decomposition may lose the population diversity when solving some many-objective optimization problems. In this paper, a simple decomposition-based MOEA with local iterative update (LIU) is proposed. The LIU strat...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	101,631
1403.7022	Abstraction of Elementary Hybrid Systems by Variable Transformation	Elementary hybrid systems (EHSs) are those hybrid systems (HSs) containing elementary functions such as exp, ln, sin, cos, etc. EHSs are very common in practice, especially in safety-critical domains. Due to the non-polynomial expressions which lead to undecidable arithmetic, verification of EHSs is very hard. Existing...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	31,866
1611.05128	Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning	Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile devices, such as smartphones and wearable gadgets, where vision algorithms can enable many revolutionary real-world applications. The key limiting...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	63,956
2103.15352	Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps	We study the differentially private Empirical Risk Minimization (ERM) and Stochastic Convex Optimization (SCO) problems for non-smooth convex functions. We get a (nearly) optimal bound on the excess empirical risk and excess population loss with subquadratic gradient complexity. More precisely, our differentially priva...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	227,171
2304.13852	Categorising Products in an Online Marketplace: An Ensemble Approach	In recent years, product categorisation has been a common issue for E-commerce companies who have utilised machine learning to categorise their products automatically. In this study, we propose an ensemble approach, using a combination of different models to separately predict each product's category, subcategory, and ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	360,730
1312.1075	A Necessary and Sufficient Condition for the Existence of Potential Functions for Heterogeneous Routing Games	We study a heterogeneous routing game in which vehicles might belong to more than one type. The type determines the cost of traveling along an edge as a function of the flow of various types of vehicles over that edge. We relax the assumptions needed for the existence of a Nash equilibrium in this heterogeneous routing...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	28,838
2410.09103	Parameter-Efficient Fine-Tuning via Selective Discrete Cosine Transform	In the era of large language models, parameter-efficient fine-tuning (PEFT) has been extensively studied. However, these approaches usually rely on the space domain, which encounters storage challenges especially when handling extensive adaptations or larger models. The frequency domain, in contrast, is more effective ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	497,436
2006.05726	Estimating semantic structure for the VQA answer space	Since its appearance, Visual Question Answering (VQA, i.e. answering a question posed over an image), has always been treated as a classification problem over a set of predefined answers. Despite its convenience, this classification approach poorly reflects the semantics of the problem limiting the answering to a choic...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	181,185
2011.12114	On Solar Photovoltaic Parameter Estimation: Global Optimality Analysis and a Simple Efficient Differential Evolution Method	A large variety of sophisticated metaheuristic methods have been proposed for photovoltaic parameter extraction. Our aim is not to develop another metaheuristic method but to investigate two practically important yet rarely studied issues: (i) whether existing results are already globally optimal; (ii) whether a signif...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	208,063
2101.10011	They See Me Rollin': Inherent Vulnerability of the Rolling Shutter in CMOS Image Sensors	In this paper, we describe how the electronic rolling shutter in CMOS image sensors can be exploited using a bright, modulated light source (e.g., an inexpensive, off-the-shelf laser), to inject fine-grained image disruptions. We demonstrate the attack on seven different CMOS cameras, ranging from cheap IoT to semi-pro...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	216,791
2110.07118	Nuisance-Label Supervision: Robustness Improvement by Free Labels	In this paper, we present a Nuisance-label Supervision (NLS) module, which can make models more robust to nuisance factor variations. Nuisance factors are those irrelevant to a task, and an ideal model should be invariant to them. For example, an activity recognition model should perform consistently regardless of the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	260,866
2006.10966	Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection	Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable. In this work, we propose a method to both interpret and augment the predictions of black-box recommender systems. In particular, we propose to interp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,064
2003.14121	HATSUKI : An anime character like robot figure platform with anime-style expressions and imitation learning based action generation	Japanese character figurines are popular and have pivot position in Otaku culture. Although numerous robots have been developed, less have focused on otaku-culture or on embodying the anime character figurine. Therefore, we take the first steps to bridge this gap by developing Hatsuki, which is a humanoid robot platfor...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	170,420
2011.03712	DeepCFL: Deep Contextual Features Learning from a Single Image	Recently, there is a vast interest in developing image feature learning methods that are independent of the training data, such as deep image prior, InGAN, SinGAN, and DCIL. These methods are unsupervised and are used to perform low-level vision tasks such as image restoration, image editing, and image synthesis. In th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	205,326
1605.03871	Adapting the Bron-Kerbosch Algorithm for Enumerating Maximal Cliques in Temporal Graphs	Dynamics of interactions play an increasingly important role in the analysis of complex networks. A modeling framework to capture this are temporal graphs which consist of a set of vertices (entities in the network) and a set of time-stamped binary interactions between the vertices. We focus on enumerating delta-clique...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	55,806
2401.00241	Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features	The Swin Transformer image super-resolution reconstruction network only relies on the long-range relationship of window attention and shifted window attention to explore features. This mechanism has two limitations. On the one hand, it only focuses on global features while ignoring local features. On the other hand, it...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	418,924
2405.18634	A Theoretical Understanding of Self-Correction through In-context Alignment	Going beyond mimicking limited human experiences, recent studies show initial evidence that, like humans, large language models (LLMs) are capable of improving their abilities purely by self-correction, i.e., correcting previous responses through self-examination, in certain circumstances. Nevertheless, little is known...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	458,506
2109.11192	Predicting the Timing of Camera Movements From the Kinematics of Instruments in Robotic-Assisted Surgery Using Artificial Neural Networks	Robotic-assisted surgeries benefit both surgeons and patients, however, surgeons frequently need to adjust the endoscopic camera to achieve good viewpoints. Simultaneously controlling the camera and the surgical instruments is impossible, and consequentially, these camera adjustments repeatedly interrupt the surgery. A...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	256,867
2210.07771	Learning to Jointly Transcribe and Subtitle for End-to-End Spontaneous Speech Recognition	TV subtitles are a rich source of transcriptions of many types of speech, ranging from read speech in news reports to conversational and spontaneous speech in talk shows and soaps. However, subtitles are not verbatim (i.e. exact) transcriptions of speech, so they cannot be used directly to improve an Automatic Speech R...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	323,862
2112.02285	Configuring Intelligent Reflecting Surface with Performance Guarantees: Blind Beamforming	This work gives a blind beamforming strategy for intelligent reflecting surface (IRS), aiming to boost the received signal-to-noise ratio (SNR) by coordinating phase shifts across reflective elements in the absence of channel information. While the existing methods of IRS beamforming typically first estimate channels a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	269,797
2204.10595	Spacing Loss for Discovering Novel Categories	Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes. In this work, we first characterize existing NCD approaches into single-stage and two-stage methods based on wh...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	292,845
2502.05242	SEER: Self-Explainability Enhancement of Large Language Models' Representations	Explaining the hidden representations of Large Language Models (LLMs) is a perspective to understand LLMs' underlying inference logic and improve their reliability in application scenarios. However, previous methods introduce external ''black-box'' modules to explain ''black-box'' LLMs, increasing the potential uncerta...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	531,521
2410.00296	VLMGuard: Defending VLMs against Malicious Prompts via Unlabeled Data	Vision-language models (VLMs) are essential for contextual understanding of both visual and textual information. However, their vulnerability to adversarially manipulated inputs presents significant risks, leading to compromised outputs and raising concerns about the reliability in VLM-integrated applications. Detectin...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	493,306
2111.04628	Accelerating GAN training using highly parallel hardware on public cloud	With the increasing number of Machine and Deep Learning applications in High Energy Physics, easy access to dedicated infrastructure represents a requirement for fast and efficient R&D. This work explores different types of cloud services to train a Generative Adversarial Network (GAN) in a parallel environment, using ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	265,540
2009.05231	Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications	Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems. However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error rate (BER). To eliminate the requirement of channel estimation and to improve the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	195,263
2111.10717	Improving Sum-Rate of Cell-Free Massive MIMO with Expanded Compute-and-Forward	Cell-free massive multiple-input multiple-output (MIMO) employs a large number of distributed access points (APs) to serve a small number of user equipments (UEs) via the same time/frequency resource. Due to the strong macro diversity gain, cell-free massive MIMO can considerably improve the achievable sum-rate compare...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	267,423
1912.03500	Optimizing Rank-based Metrics with Blackbox Differentiation	Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models. Despite years of effort, direct optimization for these metrics remains a challenge due to their non-differentiable and non-decomposable nature. We present an efficient, theoretically sound, and general met...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	156,611
1612.07801	Probabilistic graphical model based approach for water mapping using GaoFen-2 (GF-2) high resolution imagery and Landsat 8 time series	The objective of this paper is to evaluate the potential of Gaofen-2 (GF-2) high resolution multispectral sensor (MS) and panchromatic (PAN) imagery on water mapping. Difficulties of water mapping on high resolution data includes: 1) misclassification between water and shadows or other low-reflectance ground objects, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	65,984
2409.12521	GraspSAM: When Segment Anything Model Meets Grasp Detection	Grasp detection requires flexibility to handle objects of various shapes without relying on prior knowledge of the object, while also offering intuitive, user-guided control. This paper introduces GraspSAM, an innovative extension of the Segment Anything Model (SAM), designed for prompt-driven and category-agnostic gra...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	489,629
1304.3110	Appropriate and Inappropriate Estimation Techniques	Mode {also called MAP} estimation, mean estimation and median estimation are examined here to determine when they can be safely used to derive {posterior) cost minimizing estimates. (These are all Bayes procedures, using the mode. mean. or median of the posterior distribution). It is found that modal estimation only re...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,826
2410.16121	Extracting Spatiotemporal Data from Gradients with Large Language Models	Recent works show that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning. While success was demonstrated primarily on image data, these methods do not directly transfer to other domains, such as spatiotemporal data. To understand privacy risks in spat...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	500,882
2410.11295	BRC20 Pinning Attack	BRC20 tokens are a type of non-fungible asset on the Bitcoin network. They allow users to embed customized content within Bitcoin satoshis. The related token frenzy has reached a market size of US$2,650b over the past year (2023Q3-2024Q3). However, this intuitive design has not undergone serious security scrutiny. We...	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	498,491
2409.18459	FoodMLLM-JP: Leveraging Multimodal Large Language Models for Japanese Recipe Generation	Research on food image understanding using recipe data has been a long-standing focus due to the diversity and complexity of the data. Moreover, food is inextricably linked to people's lives, making it a vital research area for practical applications such as dietary management. Recent advancements in Multimodal Large L...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	492,267
1905.12310	Adversarial Imitation Learning from Incomplete Demonstrations	Imitation learning targets deriving a mapping from states to actions, a.k.a. policy, from expert demonstrations. Existing methods for imitation learning typically require any actions in the demonstrations to be fully available, which is hard to ensure in real applications. Though algorithms for learning with unobservab...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	132,731
1606.03143	PerSum: Novel Systems for Document Summarization in Persian	In this paper we explore the problem of document summarization in Persian language from two distinct angles. In our first approach, we modify a popular and widely cited Persian document summarization framework to see how it works on a realistic corpus of news articles. Human evaluation on generated summaries shows that...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	57,057
1611.03410	Binomial Checkpointing for Arbitrary Programs with No User Annotation	Heretofore, automatic checkpointing at procedure-call boundaries, to reduce the space complexity of reverse mode, has been provided by systems like Tapenade. However, binomial checkpointing, or treeverse, has only been provided in Automatic Differentiation (AD) systems in special cases, e.g., through user-provided prag...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	63,696
2309.11351	C$\cdot$ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters	We present C$\cdot$ASE, an efficient and effective framework that learns conditional Adversarial Skill Embeddings for physics-based characters. Our physically simulated character can learn a diverse repertoire of skills while providing controllability in the form of direct manipulation of the skills to be performed. C$...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	393,375
1604.01497	How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution	Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem. To address this issue, we analyze the attributes of two methodologies and find two observations of their recovered details: 1) they are complementary in both feature space and image plane, 2) they distribute spar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	54,206
1708.05027	Neural Factorization Machines for Sparse Predictive Analytics	Many predictive tasks of web applications need to model categorical variables, such as user IDs and demographics like genders and occupations. To apply standard machine learning techniques, these categorical predictors are always converted to a set of binary features via one-hot encoding, making the resultant feature v...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	79,060
1906.10816	Program Synthesis and Semantic Parsing with Learned Code Idioms	Program synthesis of general-purpose source code from natural language specifications is challenging due to the need to reason about high-level patterns in the target program and low-level implementation details at the same time. In this work, we present PATOIS, a system that allows a neural program synthesizer to expl...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	136,520
cs/0409044	Some Applications of Coding Theory in Computational Complexity	Error-correcting codes and related combinatorial constructs play an important role in several recent (and old) results in computational complexity theory. In this paper we survey results on locally-testable and locally-decodable error-correcting codes, and their applications to complexity theory and to cryptography. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	538,340
2204.04812	OutfitTransformer: Learning Outfit Representations for Fashion Recommendation	Learning an effective outfit-level representation is critical for predicting the compatibility of items in an outfit, and retrieving complementary items for a partial outfit. We present a framework, OutfitTransformer, that uses the proposed task-specific tokens and leverages the self-attention mechanism to learn effect...	false	false	false	false	true	true	true	false	false	false	false	true	false	false	false	false	false	false	290,796
2209.13818	Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidence	Small lesions in magnetic resonance imaging (MRI) images are crucial for clinical diagnosis of many kinds of diseases. However, the MRI quality can be easily degraded by various noise, which can greatly affect the accuracy of diagnosis of small lesion. Although some methods for denoising MR images have been proposed, t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	320,035
1212.4303	On the notion of balance in social network analysis	The notion of "balance" is fundamental for sociologists who study social networks. In formal mathematical terms, it concerns the distribution of triad configurations in actual networks compared to random networks of the same edge density. On reading Charles Kadushin's recent book "Understanding Social Networks", we wer...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	20,461
2109.13318	Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation	Automating sign language translation (SLT) is a challenging real world application. Despite its societal importance, though, research progress in the field remains rather poor. Crucially, existing methods that yield viable performance necessitate the availability of laborious to obtain gloss sequence groundtruth. In th...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	257,591
2007.02209	On Connections between Regularizations for Improving DNN Robustness	This paper analyzes regularization terms proposed recently for improving the adversarial robustness of deep neural networks (DNNs), from a theoretical point of view. Specifically, we study possible connections between several effective methods, including input-gradient regularization, Jacobian regularization, curvature...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	true	false	false	185,671
1904.06396	Macrocanonical Models for Texture Synthesis	In this article we consider macrocanonical models for texture synthesis. In these models samples are generated given an input texture image and a set of features which should be matched in expectation. It is known that if the images are quantized, macrocanonical models are given by Gibbs measures, using the maximum ent...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	127,541
2406.17557	The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale	The performance of a large language model (LLM) depends heavily on the quality and size of its pretraining dataset. However, the pretraining datasets for state-of-the-art open LLMs like Llama 3 and Mixtral are not publicly available and very little is known about how they were created. In this work, we introduce FineWe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	467,621
2306.01499	Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today	Recent investigations show that large language models (LLMs), specifically GPT-4, not only have remarkable capabilities in common Natural Language Processing (NLP) tasks but also exhibit human-level performance on various professional and academic benchmarks. However, whether GPT-4 can be directly used in practical app...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	370,485
2406.04249	Conv-INR: Convolutional Implicit Neural Representation for Multimodal Visual Signals	Implicit neural representation (INR) has recently emerged as a promising paradigm for signal representations. Typically, INR is parameterized by a multiplayer perceptron (MLP) which takes the coordinates as the inputs and generates corresponding attributes of a signal. However, MLP-based INRs face two critical issues: ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	461,584
2109.04144	Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning	Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream tasks as a language modeling problem. In this work, we demonstrate that, despite its advantages on low data regimes, finetuned prompt-based models for sentence pair classific...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	254,294
1207.6253	On When and How to use SAT to Mine Frequent Itemsets	A new stream of research was born in the last decade with the goal of mining itemsets of interest using Constraint Programming (CP). This has promoted a natural way to combine complex constraints in a highly flexible manner. Although CP state-of-the-art solutions formulate the task using Boolean variables, the few atte...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	17,776
2312.12173	A Globally Convergent Policy Gradient Method for Linear Quadratic Gaussian (LQG) Control	We present a model-based globally convergent policy gradient method (PGM) for linear quadratic Gaussian (LQG) control. Firstly, we establish equivalence between optimizing dynamic output feedback controllers and designing a static feedback gain for a system represented by a finite-length input-output history (IOH). Thi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	416,866
2109.01861	Length Scale Control in Topology Optimization using Fourier Enhanced Neural Networks	Length scale control is imposed in topology optimization (TO) to make designs amenable to manufacturing and other functional requirements. Broadly, there are two types of length-scale control in TO: \emph {exact} and \emph {approximate}. While the former is desirable, its implementation can be difficult, and is computa...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	253,560
2409.00342	AdaNAT: Exploring Adaptive Policy for Token-Based Image Generation	Recent studies have demonstrated the effectiveness of token-based methods for visual content generation. As a representative work, non-autoregressive Transformers (NATs) are able to synthesize images with decent quality in a small number of steps. However, NATs usually necessitate configuring a complicated generation p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,891
1807.11089	Towards Automatic Speech Identification from Vocal Tract Shape Dynamics in Real-time MRI	Vocal tract configurations play a vital role in generating distinguishable speech sounds, by modulating the airflow and creating different resonant cavities in speech production. They contain abundant information that can be utilized to better understand the underlying speech production mechanism. As a step towards aut...	false	false	true	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	104,097
2309.15313	M$^{3}$3D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding	We present a new pre-training strategy called M$^{3}$3D ($\underline{M}$ulti-$\underline{M}$odal $\underline{M}$asked $\underline{3D}$) built based on Multi-modal masked autoencoders that can leverage 3D priors and learned cross-modal representations in RGB-D data. We integrate two major self-supervised learning framew...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	394,922
2402.03754	Intensive Vision-guided Network for Radiology Report Generation	Automatic radiology report generation is booming due to its huge application potential for the healthcare industry. However, existing computer vision and natural language processing approaches to tackle this problem are limited in two aspects. First, when extracting image features, most of them neglect multi-view reaso...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	427,174
1811.12809	Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model	Vertex centrality measures are a multi-purpose analysis tool, commonly used in many application environments to retrieve information and unveil knowledge from the graphs and network structural properties. However, the algorithms of such metrics are expensive in terms of computational resources when running real-time ap...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	true	false	true	115,106
2001.02539	Deep learning reveals hidden interactions in complex systems	Rich phenomena from complex systems have long intrigued researchers, and yet modeling system micro-dynamics and inferring the forms of interaction remain challenging for conventional data-driven approaches, being generally established by human scientists. In this study, we propose AgentNet, a model-free data-driven fra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	159,763
2304.00245	Reusing Deep Neural Network Models through Model Re-engineering	Training deep neural network (DNN) models, which has become an important task in today's software development, is often costly in terms of computational resources and time. With the inspiration of software reuse, building DNN models through reusing existing ones has gained increasing attention recently. Prior approache...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	355,614
2402.00904	Graph Domain Adaptation: Challenges, Progress and Prospects	As graph representation learning often suffers from label scarcity problems in real-world applications, researchers have proposed graph domain adaptation (GDA) as an effective knowledge-transfer paradigm across graphs. In particular, to enhance model performance on target graphs with specific tasks, GDA introduces a bu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	425,777
2109.02868	HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning	Many real-world data can be represented as heterogeneous graphs with different types of nodes and connections. Heterogeneous graph neural network model aims to embed nodes or subgraphs into low-dimensional vector space for various downstream tasks such as node classification, link prediction, etc. Although several mode...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	253,882
2407.11917	Global Optimisation of Black-Box Functions with Generative Models in the Wasserstein Space	We propose a new uncertainty estimator for gradient-free optimisation of black-box simulators using deep generative surrogate models. Optimisation of these simulators is especially challenging for stochastic simulators and higher dimensions. To address these issues, we utilise a deep generative surrogate approach to mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	473,673
1210.4909	Active Learning with Distributional Estimates	Active Learning (AL) is increasingly important in a broad range of applications. Two main AL principles to obtain accurate classification with few labeled data are refinement of the current decision boundary and exploration of poorly sampled regions. In this paper we derive a novel AL scheme that balances these two pri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	19,232
2109.05493	LEA-Net: Layer-wise External Attention Network for Efficient Color Anomaly Detection	The utilization of prior knowledge about anomalies is an essential issue for anomaly detections. Recently, the visual attention mechanism has become a promising way to improve the performance of CNNs for some computer vision tasks. In this paper, we propose a novel model called Layer-wise External Attention Network (LE...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	254,818
1806.09029	Improving Text-to-SQL Evaluation Methodology	To be informative, an evaluation must measure how well systems generalize to realistic unseen data. We identify limitations of and propose improvements to current evaluations of text-to-SQL systems. First, we compare human-generated and automatically generated questions, characterizing properties of queries necessary f...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	true	false	101,273
2404.10662	Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay	We study continual offline reinforcement learning, a practical paradigm that facilitates forward transfer and mitigates catastrophic forgetting to tackle sequential offline tasks. We propose a dual generative replay framework that retains previous knowledge by concurrent replay of generated pseudo-data. First, we decou...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	447,191
2404.10260	HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights	While monomer protein structure prediction tools boast impressive accuracy, the prediction of protein complex structures remains a daunting challenge in the field. This challenge is particularly pronounced in scenarios involving complexes with protein chains from different species, such as antigen-antibody interactions...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	447,017
1906.10343	Exploring Self-Supervised Regularization for Supervised and Semi-Supervised Learning	Recent advances in semi-supervised learning have shown tremendous potential in overcoming a major barrier to the success of modern machine learning algorithms: access to vast amounts of human-labeled training data. Previous algorithms based on consistency regularization can harness the abundance of unlabeled data to pr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	136,417
2205.03210	Atlas-powered deep learning (ADL) -- application to diffusion weighted MRI	Deep learning has a great potential for estimating biomarkers in diffusion weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a unique tool for modeling the spatio-temporal variability of biomarkers. In this paper, we propose the first framework to exploit both deep learning and atlases for bio...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	295,208

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