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
q-bio/0406015	Information theory, multivariate dependence, and genetic network inference	We define the concept of dependence among multiple variables using maximum entropy techniques and introduce a graphical notation to denote the dependencies. Direct inference of information theoretic quantities from data uncovers dependencies even in undersampled regimes when the joint probability distribution cannot be...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,843
2404.15598	Federated Learning with Only Positive Labels by Exploring Label Correlations	Federated learning aims to collaboratively learn a model by using the data from multiple users under privacy constraints. In this paper, we study the multi-label classification problem under the federated learning setting, where trivial solution and extremely poor performance may be obtained, especially when only posit...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	449,153
2106.05779	Deep Implicit Surface Point Prediction Networks	Deep neural representations of 3D shapes as implicit functions have been shown to produce high fidelity models surpassing the resolution-memory trade-off faced by the explicit representations using meshes and point clouds. However, most such approaches focus on representing closed shapes. Unsigned distance function (UD...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	240,223
1509.01360	Proximal Multitask Learning over Networks with Sparsity-inducing Coregularization	In this work, we consider multitask learning problems where clusters of nodes are interested in estimating their own parameter vector. Cooperation among clusters is beneficial when the optimal models of adjacent clusters have a good number of similar entries. We propose a fully distributed algorithm for solving this pr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	46,602
1209.6491	Review of Statistical Shape Spaces for 3D Data with Comparative Analysis for Human Faces	With systems for acquiring 3D surface data being evermore commonplace, it has become important to reliably extract specific shapes from the acquired data. In the presence of noise and occlusions, this can be done through the use of statistical shape models, which are learned from databases of clean examples of the shap...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	18,821
1901.07469	Estimating Buildings' Parameters over Time Including Prior Knowledge	Modeling buildings' heat dynamics is a complex process which depends on various factors including weather, building thermal capacity, insulation preservation, and residents' behavior. Gray-box models offer a causal inference of those dynamics expressed in few parameters specific to built environments. These parameters ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	119,220
2205.02477	New Techniques Based On Odd-Edge Total Colorings In Topological Cryptosystem	For building up twin-graphic lattices towards topological cryptograph, we define four kinds of new odd-magic-type colorings: odd-edge graceful-difference total coloring, odd-edge edge-difference total coloring, odd-edge edge-magic total coloring, and odd-edge felicitous-difference total coloring in this article. Our RA...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	294,958
1010.5290	Converged Algorithms for Orthogonal Nonnegative Matrix Factorizations	This paper proposes uni-orthogonal and bi-orthogonal nonnegative matrix factorization algorithms with robust convergence proofs. We design the algorithms based on the work of Lee and Seung [1], and derive the converged versions by utilizing ideas from the work of Lin [2]. The experimental results confirm the theoretica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	8,018
1604.03296	Channel Parameter Estimation for LOS MIMO Systems	In this paper we consider the estimation of channel coefficients and frequency offsets for LOS MIMO systems. We propose that by exploiting the structure of the channel matrix, which is present due to the geometrical nature of the channel, the estimation process can be enhanced. If a single oscillator setup is used at t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	54,483
2410.22176	Comparative Analysis of PI and PID Controllers for Level and Flow Control in Coupled Tank Systems	The comparative study of Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers applied to level and flow control in coupled tank systems is presented in this research work. The coupled tank system, characterized by its nonlinear behavior, was selected due to its relevance in chemical process...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	503,527
2110.09215	A Primer on the Statistical Relation between Wireless Ultra-Reliability and Location Estimation	Location information is often used as a proxy to infer the performance of a wireless communication link. Using a very simple model, this letter unveils a basic statistical relation between the location estimation uncertainty and wireless link reliability. First, a Cram\'er-Rao bound for the localization error is derive...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	261,730
2203.01666	Correlation-Aware Deep Tracking	Robustness and discrimination power are two fundamental requirements in visual object tracking. In most tracking paradigms, we find that the features extracted by the popular Siamese-like networks cannot fully discriminatively model the tracked targets and distractor objects, hindering them from simultaneously meeting ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	283,471
2003.09998	Efficient Behavior-aware Control of Automated Vehicles at Crosswalks using Minimal Information Pedestrian Prediction Model	For automated vehicles (AVs) to reliably navigate through crosswalks, they need to understand pedestrians crossing behaviors. Simple and reliable pedestrian behavior models aid in real-time AV control by allowing the AVs to predict future pedestrian behaviors. In this paper, we present a Behavior aware Model Predictive...	true	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	169,207
2501.00826	LLM-Powered Multi-Agent System for Automated Crypto Portfolio Management	Cryptocurrency investment is inherently difficult due to its shorter history compared to traditional assets, the need to integrate vast amounts of data from various modalities, and the requirement for complex reasoning. While deep learning approaches have been applied to address these challenges, their black-box nature...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	521,814
1604.00693	Pareto Optimality and Strategy Proofness in Group Argument Evaluation (Extended Version)	An inconsistent knowledge base can be abstracted as a set of arguments and a defeat relation among them. There can be more than one consistent way to evaluate such an argumentation graph. Collective argument evaluation is the problem of aggregating the opinions of multiple agents on how a given set of arguments should ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	54,084
2008.10362	Fast Approximate Dynamic Programming for Input-Affine Dynamics	We propose two novel numerical schemes for approximate implementation of the dynamic programming~(DP) operation concerned with finite-horizon, optimal control of discrete-time systems with input-affine dynamics. The proposed algorithms involve discretization of the state and input spaces and are based on an alternative...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	192,973
2407.21049	Evaluating Long Range Dependency Handling in Code Generation Models using Multi-Step Key Retrieval	As language models support larger and larger context sizes, evaluating their ability to make effective use of that context becomes increasingly important. We analyze the ability of several code generation models to handle long range dependencies using a suite of multi-step key retrieval tasks in context windows up to 8...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	477,381
2301.00394	Deep Learning Technique for Human Parsing: A Survey and Outlook	Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts. In the last decade, it has gained significantly increased interest in the computer vision community and has been utilized in a broad range of practical applications, from security monitoring, to social media, to visual sp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	338,893
2003.09322	Crime Prediction Using Spatio-Temporal Data	A crime is a punishable offence that is harmful for an individual and his society. It is obvious to comprehend the patterns of criminal activity to prevent them. Research can help society to prevent and solve crime activates. Study shows that only 10 percent offenders commits 50 percent of the total offences. The enfor...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	169,016
2303.17086	Modularized Control Synthesis for Complex Signal Temporal Logic Specifications	The control synthesis of a dynamic system subject to a signal temporal logic (STL) specification is commonly formulated as a mixed-integer linear/convex programming (MILP/MICP) problem. Solving such a problem is computationally expensive when the specification is long and complex. In this paper, we propose a framework ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	355,101
1312.7223	Quality Estimation of English-Hindi Outputs using Naive Bayes Classifier	In this paper we present an approach for estimating the quality of machine translation system. There are various methods for estimating the quality of output sentences, but in this paper we focus on Na\"ive Bayes classifier to build model using features which are extracted from the input sentences. These features are u...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	29,460
2101.10015	AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence	Convolutional neural networks (CNN) have been widely used for boosting the performance of many machine intelligence tasks. However, the CNN models are usually computationally intensive and energy consuming, since they are often designed with numerous multiply-operations and considerable parameters for the accuracy reas...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	216,793
2206.06355	Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets	Smart manufacturing systems are being deployed at a growing rate because of their ability to interpret a wide variety of sensed information and act on the knowledge gleaned from system observations. In many cases, the principal goal of the smart manufacturing system is to rapidly detect (or anticipate) failures to redu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	302,346
2308.13142	A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions	Recently, there has been significant progress in the development of large models. Following the success of ChatGPT, numerous language models have been introduced, demonstrating remarkable performance. Similar advancements have also been observed in image generation models, such as Google's Imagen model, OpenAI's DALL-E...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	387,793
2202.09691	Parallel Sampling for Efficient High-dimensional Bayesian Network Structure Learning	Score-based algorithms that learn the structure of Bayesian networks can be used for both exact and approximate solutions. While approximate learning scales better with the number of variables, it can be computationally expensive in the presence of high dimensional data. This paper describes an approximate algorithm th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	281,281
2211.00241	Adversarial Policies Beat Superhuman Go AIs	We attack the state-of-the-art Go-playing AI system KataGo by training adversarial policies against it, achieving a >97% win rate against KataGo running at superhuman settings. Our adversaries do not win by playing Go well. Instead, they trick KataGo into making serious blunders. Our attack transfers zero-shot to other...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	327,808
2404.05540	OPSD: an Offensive Persian Social media Dataset and its baseline evaluations	The proliferation of hate speech and offensive comments on social media has become increasingly prevalent due to user activities. Such comments can have detrimental effects on individuals' psychological well-being and social behavior. While numerous datasets in the English language exist in this domain, few equivalent ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	445,117
1909.08059	Risk Assessment and Planning with Bidirectional Reachability for Autonomous Driving	Knowing and predicting dangerous factors within a scene are two key components during autonomous driving, especially in a crowded urban environment. To navigate safely in environments, risk assessment is needed to quantify and associate the risk of taking a certain action. Risk assessment and planning is usually done b...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	145,852
2210.07242	OpenOOD: Benchmarking Generalized Out-of-Distribution Detection	Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the literature. However, the field currently lacks a unified, strictly formulated, and comprehensive benchmark, which often results in unfair compa...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	323,631
2009.05257	Hierarchical Roofline Performance Analysis for Deep Learning Applications	This paper presents a practical methodology for collecting performance data necessary to conduct hierarchical Roofline analysis on NVIDIA GPUs. It discusses the extension of the Empirical Roofline Toolkit for broader support of a range of data precisions and Tensor Core support and introduces a Nsight Compute based met...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	195,275
2006.09486	Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters	Although model-agnostic meta-learning (MAML) is a very successful algorithm in meta-learning practice, it can have high computational cost because it updates all model parameters over both the inner loop of task-specific adaptation and the outer-loop of meta initialization training. A more efficient algorithm ANIL (whi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,556
2207.06167	Unsupervised Visual Representation Learning by Synchronous Momentum Grouping	In this paper, we propose a genuine group-level contrastive visual representation learning method whose linear evaluation performance on ImageNet surpasses the vanilla supervised learning. Two mainstream unsupervised learning schemes are the instance-level contrastive framework and clustering-based schemes. The former ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	307,787
2404.14167	A multi-robot system for the detection of explosive devices	In order to clear the world of the threat posed by landmines and other explosive devices, robotic systems can play an important role. However, the development of such field robots that need to operate in hazardous conditions requires the careful consideration of multiple aspects related to the perception, mobility, and...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	448,591
2003.09372	One Neuron to Fool Them All	Despite vast research in adversarial examples, the root causes of model susceptibility are not well understood. Instead of looking at attack-specific robustness, we propose a notion that evaluates the sensitivity of individual neurons in terms of how robust the model's output is to direct perturbations of that neuron's...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	169,028
2410.14858	Misleading Ourselves: How Disinformation Manipulates Sensemaking	Informal sensemaking surrounding U.S. election processes has been fraught in recent years, due to the inherent uncertainty of elections, the complexity of election processes in the U.S., and to disinformation. Based on insights from qualitative analysis of election rumors spreading online in 2020 and 2022, we introduce...	true	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	500,239
2309.14763	ConPET: Continual Parameter-Efficient Tuning for Large Language Models	Continual learning necessitates the continual adaptation of models to newly emerging tasks while minimizing the catastrophic forgetting of old ones. This is extremely challenging for large language models (LLMs) with vanilla full-parameter tuning due to high computation costs, memory consumption, and forgetting issue. ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	394,730
2003.01463	Robust High-Transparency Haptic Exploration for Dexterous Telemanipulation	Robotic teleoperation will allow us to perform complex manipulation tasks in dangerous or remote environments, such as needed for planetary exploration or nuclear decommissioning. This work proposes a novel telemanipulation architecture using a passive Fractal Impedance Controller (FIC), which does not depend upon an a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	166,661
1307.5503	Mathematical models for epidemic spreading on complex networks	We propose a model for epidemic spreading on a finite complex network with a restriction to at most one contamination per time step. Because of a highly discrete character of the process, the analysis cannot use the continous approximation, widely exploited for most of the models. Using discrete approach we investigate...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	25,948
1906.01772	Reinforcement Learning When All Actions are Not Always Available	The Markov decision process (MDP) formulation used to model many real-world sequential decision making problems does not efficiently capture the setting where the set of available decisions (actions) at each time step is stochastic. Recently, the stochastic action set Markov decision process (SAS-MDP) formulation has b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,830
2210.00812	A Benchmark for Multi-Modal Lidar SLAM with Ground Truth in GNSS-Denied Environments	Lidar-based simultaneous localization and mapping (SLAM) approaches have obtained considerable success in autonomous robotic systems. This is in part owing to the high-accuracy of robust SLAM algorithms and the emergence of new and lower-cost lidar products. This study benchmarks current state-of-the-art lidar SLAM alg...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	321,013
1611.03012	New CRT sequence sets for a collision channel without feedback	Protocol sequences are binary and periodic sequences used for deterministic multiple access in a collision channel without feedback. In this paper, we focus on user-irrepressible (UI) protocol sequences that can guarantee a positive individual throughput per sequence period with probability one for a slot-synchronous c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	63,638
2103.13812	Reframing demand forecasting: a two-fold approach for lumpy and intermittent demand	Demand forecasting is a crucial component of demand management. While shortening the forecasting horizon allows for more recent data and less uncertainty, this frequently means lower data aggregation levels and a more significant data sparsity. Sparse demand data usually results in lumpy or intermittent demand patterns...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	226,617
2311.07991	Nonlinear Stability Boundary Assessment Of Wind Power Plants Based on Reverse-Time Trajectory	This letter determines the nonlinear stability boundary of a wind power plant (WPP) connected to an AC power grid via a long HVAC cable. The analysis focuses on the slow Phase-Locked Loop (PLL) dynamics, with an assumption that the fast current control dynamics can be neglected. To begin, we propose an aggregated reduc...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	407,549
2204.02633	DAGAM: Data Augmentation with Generation And Modification	Text classification is a representative downstream task of natural language processing, and has exhibited excellent performance since the advent of pre-trained language models based on Transformer architecture. However, in pre-trained language models, under-fitting often occurs due to the size of the model being very l...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	290,028
2010.03542	Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification using Pre-trained Language Models	This paper describes Galileo's performance in SemEval-2020 Task 12 on detecting and categorizing offensive language in social media. For Offensive Language Identification, we proposed a multi-lingual method using Pre-trained Language Models, ERNIE and XLM-R. For offensive language categorization, we proposed a knowledg...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	199,432
2310.10828	Robustness and Approximation of Discrete-time Mean-field Games under Discounted Cost Criterion	In this paper, we investigate the robustness of stationary mean-field equilibria in the presence of model uncertainties, specifically focusing on infinite-horizon discounted cost functions. To achieve this, we initially establish convergence conditions for value iteration-based algorithms in mean-field games. Subsequen...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	400,396
2203.09397	Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models	Relations between words are governed by hierarchical structure rather than linear ordering. Sequence-to-sequence (seq2seq) models, despite their success in downstream NLP applications, often fail to generalize in a hierarchy-sensitive manner when performing syntactic transformations - for example, transforming declarat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	286,143
2311.08398	Are Large Language Models Temporally Grounded?	Are Large language models (LLMs) temporally grounded? Since LLMs cannot perceive and interact with the environment, it is impossible to answer this question directly. Instead, we provide LLMs with textual narratives and probe them with respect to their common-sense knowledge of the structure and duration of events, the...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	407,712
2401.08478	Solving Continual Offline Reinforcement Learning with Decision Transformer	Continuous offline reinforcement learning (CORL) combines continuous and offline reinforcement learning, enabling agents to learn multiple tasks from static datasets without forgetting prior tasks. However, CORL faces challenges in balancing stability and plasticity. Existing methods, employing Actor-Critic structures ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	421,903
2306.02658	Faster Training of Diffusion Models and Improved Density Estimation via Parallel Score Matching	In Diffusion Probabilistic Models (DPMs), the task of modeling the score evolution via a single time-dependent neural network necessitates extended training periods and may potentially impede modeling flexibility and capacity. To counteract these challenges, we propose leveraging the independence of learning tasks at d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	370,999
2107.11921	Compensation Learning	Weighting strategy prevails in machine learning. For example, a common approach in robust machine learning is to exert lower weights on samples which are likely to be noisy or quite hard. This study reveals another undiscovered strategy, namely, compensating. Various incarnations of compensating have been utilized but ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,742
1812.08224	Specializing Underdetermined Action Descriptions Through Plan Projection	Plan execution on real robots in realistic environments is underdetermined and often leads to failures. The choice of action parameterization is crucial for task success. By thinking ahead of time with the fast plan projection mechanism proposed in this paper, a general plan can be specialized towards the environment a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	116,956
2403.01718	$L_0$ Regularization of Field-Aware Factorization Machine through Ising Model	We examined the use of the Ising model as an $L_0$ regularization method for field-aware factorization machines (FFM). This approach improves generalization performance and has the advantage of simultaneously determining the best feature combinations for each of several groups. We can deepen the interpretation and unde...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	434,544
2103.03389	An Analytical Solution to the IMU Initialization Problem for Visual-Inertial Systems	The fusion of visual and inertial measurements is becoming more and more popular in the robotics community since both sources of information complement well each other. However, in order to perform this fusion, the biases of the Inertial Measurement Unit (IMU) as well as the direction of gravity must be initialized fir...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	223,252
1202.3731	What Cannot be Learned with Bethe Approximations	We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its Bethe approximation. We show that there exists a regime of empirical marginals where such Bethe learning will fail. By failure we mean that th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	14,403
1404.6048	List and Unique Error-Erasure Decoding of Interleaved Gabidulin Codes with Interpolation Techniques	A new interpolation-based decoding principle for interleaved Gabidulin codes is presented. The approach consists of two steps: First, a multi-variate linearized polynomial is constructed which interpolates the coefficients of the received word and second, the roots of this polynomial have to be found. Due to the specif...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,557
2311.11339	Assessment of Transmission-level Fault Impacts on 3-phase and 1-phase Distribution IBR Operation	The widespread deployment of inverter-based resources (IBRs) renders distribution systems susceptible to transmission-level faults. This paper presents a comprehensive analysis of the impact of transmission-level faults on 3-phase and 1-phase distribution IBR operation. To evaluate distributed IBR tripping across vario...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	408,912
2001.11198	A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme	Recently, CNN is a popular choice to handle the hyperspectral image classification challenges. In spite of having such large spectral information in Hyper-Spectral Image(s) (HSI), it creates a curse of dimensionality. Also, large spatial variability of spectral signature adds more difficulty in classification problem. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	162,010
1809.02727	Decentralized Differentially Private Without-Replacement Stochastic Gradient Descent	While machine learning has achieved remarkable results in a wide variety of domains, the training of models often requires large datasets that may need to be collected from different individuals. As sensitive information may be contained in the individual's dataset, sharing training data may lead to severe privacy conc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	107,113
2410.23280	RelationBooth: Towards Relation-Aware Customized Object Generation	Customized image generation is crucial for delivering personalized content based on user-provided image prompts, aligning large-scale text-to-image diffusion models with individual needs. However, existing models often overlook the relationships between customized objects in generated images. Instead, this work address...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	503,977
2402.15952	ViSTec: Video Modeling for Sports Technique Recognition and Tactical Analysis	The immense popularity of racket sports has fueled substantial demand in tactical analysis with broadcast videos. However, existing manual methods require laborious annotation, and recent attempts leveraging video perception models are limited to low-level annotations like ball trajectories, overlooking tactics that ne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	432,349
2408.03508	SemiEpi: Self-driving, Closed-loop Multi-Step Growth of Semiconductor Heterostructures Guided by Machine Learning	The semiconductor industry has prioritized automating repetitive tasks through closed-loop, self-driving experimentation, accelerating the optimization of complex multi-step processes. The emergence of machine learning (ML) has ushered in self-driving processes with minimal human intervention. This work introduces Semi...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	479,043
2108.13004	X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph	3D teeth reconstruction from X-ray is important for dental diagnosis and many clinical operations. However, no existing work has explored the reconstruction of teeth for a whole cavity from a single panoramic radiograph. Different from single object reconstruction from photos, this task has the unique challenge of cons...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	252,677
2303.07216	Parallel Vertex Diffusion for Unified Visual Grounding	Unified visual grounding pursues a simple and generic technical route to leverage multi-task data with less task-specific design. The most advanced methods typically present boxes and masks as vertex sequences to model referring detection and segmentation as an autoregressive sequential vertex generation paradigm. Howe...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	351,174
1811.11896	Variational Autoencoding the Lagrangian Trajectories of Particles in a Combustion System	We introduce a deep learning method to simulate the motion of particles trapped in a chaotic recirculating flame. The Lagrangian trajectories of particles, captured using a high-speed camera and subsequently reconstructed in 3-dimensional space, were used to train a variational autoencoder (VAE) which comprises multipl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	114,877
2307.16183	HD-Fusion: Detailed Text-to-3D Generation Leveraging Multiple Noise Estimation	In this paper, we study Text-to-3D content generation leveraging 2D diffusion priors to enhance the quality and detail of the generated 3D models. Recent progress (Magic3D) in text-to-3D has shown that employing high-resolution (e.g., 512 x 512) renderings can lead to the production of high-quality 3D models using late...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	382,507
2406.17235	Task-Agnostic Federated Learning	In the realm of medical imaging, leveraging large-scale datasets from various institutions is crucial for developing precise deep learning models, yet privacy concerns frequently impede data sharing. federated learning (FL) emerges as a prominent solution for preserving privacy while facilitating collaborative learning...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	467,476
2408.01993	Towards Automatic Hands-on-Keyboard Attack Detection Using LLMs in EDR Solutions	Endpoint Detection and Remediation (EDR) platforms are essential for identifying and responding to cyber threats. This study presents a novel approach using Large Language Models (LLMs) to detect Hands-on-Keyboard (HOK) cyberattacks. Our method involves converting endpoint activity data into narrative forms that LLMs c...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	478,455
2307.09288	Llama 2: Open Foundation and Fine-Tuned Chat Models	In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	380,123
2501.06963	Generative Artificial Intelligence-Supported Pentesting: A Comparison between Claude Opus, GPT-4, and Copilot	The advent of Generative Artificial Intelligence (GenAI) has brought a significant change to our society. GenAI can be applied across numerous fields, with particular relevance in cybersecurity. Among the various areas of application, its use in penetration testing (pentesting) or ethical hacking processes is of specia...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	524,205
1812.11166	Learning to Reconstruct Shapes from Unseen Classes	From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life. Contemporary single-image 3D reconstruction algorithms aim to solve this task in a similar fashion, but often end up with priors that are highly biased by training classes. Here we pres...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	117,506
2302.02530	A Stability Analysis for the Reaction Torque Observer-based Sensorless Force Control Systems	This paper proposes a new stability analysis for the Reaction Torque Observer (RTOb) based robust force control systems in the discrete-time domain. The robust force controller is implemented by employing a Disturbance Observer (DOb) to suppress disturbances, such as friction and hysteresis, in an inner-loop and anothe...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	344,029
2009.02608	Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks	Deep neural networks (DNNs) are now commonly used in many domains. However, they are vulnerable to adversarial attacks: carefully crafted perturbations on data inputs that can fool a model into making incorrect predictions. Despite significant research on developing DNN attack and defense techniques, people still lack ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	194,594
2501.14586	A sub-structuring approach for model reduction of frictionally clamped thin-walled structures	Thin-walled structures clamped by friction joints, such as aircraft skin panels are exposed to bending-stretching coupling and frictional contact. We propose an original sub-structuring approach, where the system is divided into thin-walled and support regions, so that geometrically nonlinear behavior is relevant only ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	527,175
1911.06515	Likelihood Assignment for Out-of-Distribution Inputs in Deep Generative Models is Sensitive to Prior Distribution Choice	Recent work has shown that deep generative models assign higher likelihood to out-of-distribution inputs than to training data. We show that a factor underlying this phenomenon is a mismatch between the nature of the prior distribution and that of the data distribution, a problem found in widely used deep generative mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	153,562
2002.12408	High Precision In-Pipe Robot Localization with Reciprocal Sensor Fusion	The huge advantage of in-pipe robots is that they are able to measure from inside the pipes, and to sense the geometry, appearance and radiometry directly. The downside is the inability to know precise, absolute position of the measurements in very long pipe runs. This paper develops the unprecedented localization requ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	166,017
2011.03577	A Weakly Supervised Convolutional Network for Change Segmentation and Classification	Fully supervised change detection methods require difficult to procure pixel-level labels, while weakly supervised approaches can be trained with image-level labels. However, most of these approaches require a combination of changed and unchanged image pairs for training. Thus, these methods can not directly be used fo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	205,277
2404.00593	LAESI: Leaf Area Estimation with Synthetic Imagery	We introduce LAESI, a Synthetic Leaf Dataset of 100,000 synthetic leaf images on millimeter paper, each with semantic masks and surface area labels. This dataset provides a resource for leaf morphology analysis primarily aimed at beech and oak leaves. We evaluate the applicability of the dataset by training machine lea...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	443,011
1811.11163	Class-Distinct and Class-Mutual Image Generation with GANs	Class-conditional extensions of generative adversarial networks (GANs), such as auxiliary classifier GAN (AC-GAN) and conditional GAN (cGAN), have garnered attention owing to their ability to decompose representations into class labels and other factors and to boost the training stability. However, a limitation is that...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	114,699
1902.01177	Unsupervised Clinical Language Translation	As patients' access to their doctors' clinical notes becomes common, translating professional, clinical jargon to layperson-understandable language is essential to improve patient-clinician communication. Such translation yields better clinical outcomes by enhancing patients' understanding of their own health condition...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	120,603
2302.12978	Impact of Thermal Variability on SOC Estimation Algorithms	While the efficiency of renewable energy components like inverters and PV panels is at an all-time high, there are still research gaps for batteries. Lithium-ion batteries have a lot of potential, but there are still some problems that need fixing, such as thermal management. Because of this, the battery management sys...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	347,759
2405.08715	DeVOS: Flow-Guided Deformable Transformer for Video Object Segmentation	The recent works on Video Object Segmentation achieved remarkable results by matching dense semantic and instance-level features between the current and previous frames for long-time propagation. Nevertheless, global feature matching ignores scene motion context, failing to satisfy temporal consistency. Even though som...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	454,191
1610.02258	Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion Simulation with High Performance Computers	Stochastic, spatial reaction-diffusion simulations have been widely used in systems biology and computational neuroscience. However, the increasing scale and complexity of simulated models and morphologies have exceeded the capacity of any serial implementation. This led to development of parallel solutions that benefi...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	62,074
2402.04504	Text2Street: Controllable Text-to-image Generation for Street Views	Text-to-image generation has made remarkable progress with the emergence of diffusion models. However, it is still a difficult task to generate images for street views based on text, mainly because the road topology of street scenes is complex, the traffic status is diverse and the weather condition is various, which m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	427,477
1803.06540	Learning over Knowledge-Base Embeddings for Recommendation	State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured k...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	92,858
2003.12881	Streamlined Empirical Bayes Fitting of Linear Mixed Models in Mobile Health	To effect behavior change a successful algorithm must make high-quality decisions in real-time. For example, a mobile health (mHealth) application designed to increase physical activity must make contextually relevant suggestions to motivate users. While machine learning offers solutions for certain stylized settings, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	170,038
1811.05468	Few-shot Learning for Named Entity Recognition in Medical Text	Deep neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks (Young, Hazarika, Poria, & Cambria, 2017). However, these gains rely on the availability of large amounts of annotated examples, without which state-of-the-art performance is rare...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	113,322
2206.03426	Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage	Graph Neural Networks (GNNs) have shown great power in learning node representations on graphs. However, they may inherit historical prejudices from training data, leading to discriminatory bias in predictions. Although some work has developed fair GNNs, most of them directly borrow fair representation learning techniq...	false	false	false	false	false	false	true	false	false	false	false	false	true	true	false	false	false	false	301,279
1906.03648	LSTM Networks Can Perform Dynamic Counting	In this paper, we systematically assess the ability of standard recurrent networks to perform dynamic counting and to encode hierarchical representations. All the neural models in our experiments are designed to be small-sized networks both to prevent them from memorizing the training sets and to visualize and interpre...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	134,446
2007.02227	Solving stochastic optimal control problem via stochastic maximum principle with deep learning method	In this paper, we aim to solve the high dimensional stochastic optimal control problem from the view of the stochastic maximum principle via deep learning. By introducing the extended Hamiltonian system which is essentially an FBSDE with a maximum condition, we reformulate the original control problem as a new one. Thr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	185,676
2307.08580	The Resume Paradox: Greater Language Differences, Smaller Pay Gaps	Over the past decade, the gender pay gap has remained steady with women earning 84 cents for every dollar earned by men on average. Many studies explain this gap through demand-side bias in the labor market represented through employers' job postings. However, few studies analyze potential bias from the worker supply-s...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	379,856
2206.03398	Towards a General Purpose CNN for Long Range Dependencies in $N$D	The use of Convolutional Neural Networks (CNNs) is widespread in Deep Learning due to a range of desirable model properties which result in an efficient and effective machine learning framework. However, performant CNN architectures must be tailored to specific tasks in order to incorporate considerations such as the i...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	301,270
1809.07704	On Information Transfer Based Characterization of Power System Stability	In this paper, we present a novel approach to identify the generators and states responsible for the small-signal stability of power networks. To this end, the newly developed notion of information transfer between the states of a dynamical system is used. In particular, using the concept of information transfer, which...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	108,339
2306.00415	Mixed-Integer MPC Strategies for Fueling and Density Control in Fusion Tokamaks	Model predictive control (MPC) is promising for fueling and core density feedback control in nuclear fusion tokamaks, where the primary actuators, frozen hydrogen fuel pellets fired into the plasma, are discrete. Previous density feedback control approaches have only approximated pellet injection as a continuous input ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	370,007
1905.11275	Scaling Fine-grained Modularity Clustering for Massive Graphs	Modularity clustering is an essential tool to understand complicated graphs. However, existing methods are not applicable to massive graphs due to two serious weaknesses. (1) It is difficult to fully reproduce ground-truth clusters due to the resolution limit problem. (2) They are computationally expensive because all ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	132,382
2212.03423	Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning	Interactive data exploration (IDE) is an effective way of comprehending big data, whose volume and complexity are beyond human abilities. The main goal of IDE is to discover user interest regions from a database through multi-rounds of user labelling. Existing IDEs adopt active-learning framework, where users iterative...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	335,115
2003.03919	Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution	Time series prediction is an important problem in machine learning. Previous methods for time series prediction did not involve additional information. With a lot of dynamic knowledge graphs available, we can use this additional information to predict the time series better. Recently, there has been a focus on the appl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	167,406
1801.01229	Modular Networks for Validating Community Detection Algorithms	How can we accurately compare different community detection algorithms? These algorithms cluster nodes in a given network, and their performance is often validated on benchmark networks with explicit ground-truth communities. Given the lack of cluster labels in real-world networks, a model that generates realistic netw...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	87,693
2004.08851	Approximate Nearest Neighbour Search on Privacy-aware Encoding of User Locations to Identify Susceptible Infections in Simulated Epidemics	Amidst an increasing number of infected cases during the Covid-19 pandemic, it is essential to trace, as early as possible, the susceptible people who might have been infected by the disease due to their close proximity with people who were tested positive for the virus. This early contact tracing is likely to limit th...	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	false	173,187
2401.08602	Learning with Chemical versus Electrical Synapses -- Does it Make a Difference?	Bio-inspired neural networks have the potential to advance our understanding of neural computation and improve the state-of-the-art of AI systems. Bio-electrical synapses directly transmit neural signals, by enabling fast current flow between neurons. In contrast, bio-chemical synapses transmit neural signals indirectl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	421,954

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