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
2210.13938	Dual Mechanism Priming Effects in Hindi Word Order	Word order choices during sentence production can be primed by preceding sentences. In this work, we test the DUAL MECHANISM hypothesis that priming is driven by multiple different sources. Using a Hindi corpus of text productions, we model lexical priming with an n-gram cache model and we capture more abstract syntact...	false	false	false	false	true	false	false	false	true	true	false	false	false	false	false	false	false	false	326,367
2410.06121	Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA	Retrieval-Augmented Generation (RAG) is widely used to inject external non-parametric knowledge into large language models (LLMs). Recent works suggest that Knowledge Graphs (KGs) contain valuable external knowledge for LLMs. Retrieving information from KGs differs from extracting it from document sets. Most existing a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	496,059
2405.00846	Gameplay Filters: Robust Zero-Shot Safety through Adversarial Imagination	Despite the impressive recent advances in learning-based robot control, ensuring robustness to out-of-distribution conditions remains an open challenge. Safety filters can, in principle, keep arbitrary control policies from incurring catastrophic failures by overriding unsafe actions, but existing solutions for complex...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	451,103
2104.04715	Object Priors for Classifying and Localizing Unseen Actions	This work strives for the classification and localization of human actions in videos, without the need for any labeled video training examples. Where existing work relies on transferring global attribute or object information from seen to unseen action videos, we seek to classify and spatio-temporally localize unseen a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	229,470
2011.03118	Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages	In this work, we explore the benefits of using multilingual bottleneck features (mBNF) in acoustic modelling for the automatic speech recognition of code-switched (CS) speech in African languages. The unavailability of annotated corpora in the languages of interest has always been a primary challenge when developing sp...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	205,138
2108.11472	Bandwidth Allocation and Service Differentiation in D2D Wireless Networks	Inspired by a new feature in 5G NR called bandwidth part (BWP), this paper presents a bandwidth allocation (BA) model that allows one to adapt the bandwidth allocated to users depending on their data rate needs. Specifically, in adaptive BA, a wide bandwidth is divided into chunks of smaller bandwidths and the number o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	252,184
2501.06492	A New Flexible Train-Test Split Algorithm, an approach for choosing among the Hold-out, K-fold cross-validation, and Hold-out iteration	Artificial Intelligent transformed industries, like engineering, medicine, finance. Predictive models use supervised learning, a vital Machine learning subset. Crucial for model evaluation, cross-validation includes re-substitution, hold-out, and K-fold. This study focuses on improving the accuracy of ML algorithms acr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	524,007
2010.08884	On the best choice of Lasso program given data parameters	Generalized compressed sensing (GCS) is a paradigm in which a structured high-dimensional signal may be recovered from random, under-determined, and corrupted linear measurements. Generalized Lasso (GL) programs are effective for solving GCS problems due to their proven ability to leverage underlying signal structure. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	201,338
1901.05517	Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles	This article reviews the applications of Bayesian Networks to Intelligent Autonomous Vehicles (IAV) from the decision making point of view, which represents the final step for fully Autonomous Vehicles (currently under discussion). Until now, when it comes making high level decisions for Autonomous Vehicles (AVs), huma...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	118,800
2009.00724	On Throughput Improvement using Immediate Re-transmission in Grant-Free Random Access with Massive MIMO	To support machine-type communication (MTC), massive multiple-input multiple-output (MIMO) has been considered for grant-free random access. In general, the performance of grant-free random access with massive MIMO is limited by the number of preambles and the number of active devices. In particular, when there are a n...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	194,116
2108.10168	CGEMs: A Metric Model for Automatic Code Generation using GPT-3	Today, AI technology is showing its strengths in almost every industry and walks of life. From text generation, text summarization, chatbots, NLP is being used widely. One such paradigm is automatic code generation. An AI could be generating anything; hence the output space is unconstrained. A self-driving car is drive...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	251,819
1904.08265	Cycle-SUM: Cycle-consistent Adversarial LSTM Networks for Unsupervised Video Summarization	In this paper, we present a novel unsupervised video summarization model that requires no manual annotation. The proposed model termed Cycle-SUM adopts a new cycle-consistent adversarial LSTM architecture that can effectively maximize the information preserving and compactness of the summary video. It consists of a fra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	128,016
2402.08117	A Universal Non-Parametric Approach For Improved Molecular Sequence Analysis	In the field of biological research, it is essential to comprehend the characteristics and functions of molecular sequences. The classification of molecular sequences has seen widespread use of neural network-based techniques. Despite their astounding accuracy, these models often require a substantial number of paramet...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	428,968
1901.11383	Automatic Information Extraction from Piping and Instrumentation Diagrams	One of the most common modes of representing engineering schematics are Piping and Instrumentation diagrams (P&IDs) that describe the layout of an engineering process flow along with the interconnected process equipment. Over the years, P&ID diagrams have been manually generated, scanned and stored as image files. Thes...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	120,237
1212.2823	Tracking Revisited using RGBD Camera: Baseline and Benchmark	Although there has been significant progress in the past decade,tracking is still a very challenging computer vision task, due to problems such as occlusion and model drift.Recently, the increased popularity of depth sensors e.g. Microsoft Kinect has made it easy to obtain depth data at low cost.This may be a game chan...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	20,347
2407.18154	Identification of a time-varying SIR Model for Covid-19	Throughout human history, epidemics have been a constant presence. Understanding their dynamics is essential to predict scenarios and make substantiated decisions. Mathematical models are powerful tools to describe an epidemic behavior. Among the most used, the compartmental ones stand out, dividing population into cla...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	476,267
2501.01711	LLMs & Legal Aid: Understanding Legal Needs Exhibited Through User Queries	The paper presents a preliminary analysis of an experiment conducted by Frank Bold, a Czech expert group, to explore user interactions with GPT-4 for addressing legal queries. Between May 3, 2023, and July 25, 2023, 1,252 users submitted 3,847 queries. Unlike studies that primarily focus on the accuracy, factuality, or...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	522,182
2006.16916	Counterfactual Predictions under Runtime Confounding	Algorithms are commonly used to predict outcomes under a particular decision or intervention, such as predicting whether an offender will succeed on parole if placed under minimal supervision. Generally, to learn such counterfactual prediction models from observational data on historical decisions and corresponding out...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	184,947
1608.06879	AIDE: Fast and Communication Efficient Distributed Optimization	In this paper, we present two new communication-efficient methods for distributed minimization of an average of functions. The first algorithm is an inexact variant of the DANE algorithm that allows any local algorithm to return an approximate solution to a local subproblem. We show that such a strategy does not affect...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	60,170
2209.04145	ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation	Text-guided 3D shape generation remains challenging due to the absence of large paired text-shape data, the substantial semantic gap between these two modalities, and the structural complexity of 3D shapes. This paper presents a new framework called Image as Stepping Stone (ISS) for the task by introducing 2D image as ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	316,698
2205.09552	Hybrid Intelligent Testing in Simulation-Based Verification	Efficient and effective testing for simulation-based hardware verification is challenging. Using constrained random test generation, several millions of tests may be required to achieve coverage goals. The vast majority of tests do not contribute to coverage progress, yet they consume verification resources. In this pa...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	297,315
2102.04897	Learning State Representations from Random Deep Action-conditional Predictions	Our main contribution in this work is an empirical finding that random General Value Functions (GVFs), i.e., deep action-conditional predictions -- random both in what feature of observations they predict as well as in the sequence of actions the predictions are conditioned upon -- form good auxiliary tasks for reinfor...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	219,259
2210.08788	EISeg: An Efficient Interactive Segmentation Tool based on PaddlePaddle	In recent years, the rapid development of deep learning has brought great advancements to image and video segmentation methods based on neural networks. However, to unleash the full potential of such models, large numbers of high-quality annotated images are necessary for model training. Currently, many widely used ope...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	324,294
1807.10653	Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas	The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function. Cardiac motion atlases provide a space of reference in which the motion fields of a cohort of subjects can be directl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,004
2401.12700	Securing Recommender System via Cooperative Training	Recommender systems are often susceptible to well-crafted fake profiles, leading to biased recommendations. Among existing defense methods, data-processing-based methods inevitably exclude normal samples, while model-based methods struggle to enjoy both generalization and robustness. To this end, we suggest integrating...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	423,470
2406.03537	A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models	High-dimensional data commonly lies on low-dimensional submanifolds, and estimating the local intrinsic dimension (LID) of a datum -- i.e. the dimension of the submanifold it belongs to -- is a longstanding problem. LID can be understood as the number of local factors of variation: the more factors of variation a datum...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	461,279
2109.05671	Shape-Biased Domain Generalization via Shock Graph Embeddings	There is an emerging sense that the vulnerability of Image Convolutional Neural Networks (CNN), i.e., sensitivity to image corruptions, perturbations, and adversarial attacks, is connected with Texture Bias. This relative lack of Shape Bias is also responsible for poor performance in Domain Generalization (DG). The inc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	254,884
2410.23152	When can classical neural networks represent quantum states?	A naive classical representation of an n-qubit state requires specifying exponentially many amplitudes in the computational basis. Past works have demonstrated that classical neural networks can succinctly express these amplitudes for many physically relevant states, leading to computationally powerful representations ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	503,924
2212.08458	Fast Rule-Based Decoding: Revisiting Syntactic Rules in Neural Constituency Parsing	Most recent studies on neural constituency parsing focus on encoder structures, while few developments are devoted to decoders. Previous research has demonstrated that probabilistic statistical methods based on syntactic rules are particularly effective in constituency parsing, whereas syntactic rules are not used duri...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	336,756
2111.06053	Improving Large-scale Language Models and Resources for Filipino	In this paper, we improve on existing language resources for the low-resource Filipino language in two ways. First, we outline the construction of the TLUnified dataset, a large-scale pretraining corpus that serves as an improvement over smaller existing pretraining datasets for the language in terms of scale and topic...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	265,970
2309.05497	Personality Detection and Analysis using Twitter Data	Personality types are important in various fields as they hold relevant information about the characteristics of a human being in an explainable format. They are often good predictors of a person's behaviors in a particular environment and have applications ranging from candidate selection to marketing and mental healt...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	391,105
2106.07068	HistoTransfer: Understanding Transfer Learning for Histopathology	Advancement in digital pathology and artificial intelligence has enabled deep learning-based computer vision techniques for automated disease diagnosis and prognosis. However, WSIs present unique computational and algorithmic challenges. WSIs are gigapixel-sized, making them infeasible to be used directly for training ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	240,759
2404.07525	Enhancing Policy Gradient with the Polyak Step-Size Adaption	Policy gradient is a widely utilized and foundational algorithm in the field of reinforcement learning (RL). Renowned for its convergence guarantees and stability compared to other RL algorithms, its practical application is often hindered by sensitivity to hyper-parameters, particularly the step-size. In this paper, w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	445,870
2204.02019	Mixing detection on Bitcoin transactions using statistical patterns	Cryptocurrencies gained lots of attention mainly because of the anonymous way of online payment, which they suggested. Meanwhile, Bitcoin and other major cryptocurrencies have experienced severe deanonymization attacks. To address these attacks, Bitcoin contributors introduced services called mixers or tumblers. Mixing...	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	289,800
2108.10367	Marine vessel tracking using a monocular camera	In this paper, a new technique for camera calibration using only GPS data is presented. A new way of tracking objects that move on a plane in a video is achieved by using the location and size of the bounding box to estimate the distance, achieving an average prediction error of 5.55m per 100m distance from the camera....	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	251,875
2307.15934	A Noisy-Label-Learning Formulation for Immune Repertoire Classification and Disease-Associated Immune Receptor Sequence Identification	Immune repertoire classification, a typical multiple instance learning (MIL) problem, is a frontier research topic in computational biology that makes transformative contributions to new vaccines and immune therapies. However, the traditional instance-space MIL, directly assigning bag-level labels to instances, suffers...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	382,417
2301.10619	Simultaneous Transmitting and Reflecting (STAR)-RIS for Harmonious Millimeter Wave Spectrum Sharing	The opening of the millimeter wave (mmWave) spectrum bands for 5G communications has motivated the need for novel spectrum sharing solutions at these high frequencies. In fact, reconfigurable intelligent surfaces (RISs) have recently emerged to enable spectrum sharing while enhancing the incumbents' quality-of-service ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	341,864
2110.06546	A Melody-Unsupervision Model for Singing Voice Synthesis	Recent studies in singing voice synthesis have achieved high-quality results leveraging advances in text-to-speech models based on deep neural networks. One of the main issues in training singing voice synthesis models is that they require melody and lyric labels to be temporally aligned with audio data. The temporal a...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	260,663
2112.04934	Model Doctor: A Simple Gradient Aggregation Strategy for Diagnosing and Treating CNN Classifiers	Recently, Convolutional Neural Network (CNN) has achieved excellent performance in the classification task. It is widely known that CNN is deemed as a 'black-box', which is hard for understanding the prediction mechanism and debugging the wrong prediction. Some model debugging and explanation works are developed for so...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	270,690
2203.07671	Safe Neurosymbolic Learning with Differentiable Symbolic Execution	We study the problem of learning worst-case-safe parameters for programs that use neural networks as well as symbolic, human-written code. Such neurosymbolic programs arise in many safety-critical domains. However, because they can use nondifferentiable operations, it is hard to learn their parameters using existing gr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	285,513
2207.05708	Improved Batching Strategy For Irregular Time-Series ODE	Irregular time series data are prevalent in the real world and are challenging to model with a simple recurrent neural network (RNN). Hence, a model that combines the use of ordinary differential equations (ODE) and RNN was proposed (ODE-RNN) to model irregular time series with higher accuracy, but it suffers from high...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	307,636
1011.0506	A Very Fast Algorithm for Matrix Factorization	We present a very fast algorithm for general matrix factorization of a data matrix for use in the statistical analysis of high-dimensional data via latent factors. Such data are prevalent across many application areas and generate an ever-increasing demand for methods of dimension reduction in order to undertake the st...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	8,115
1407.1723	The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings	This paper deals with the analysis of a recent reformulation of the primal-dual hybrid gradient method [Zhu and Chan 2008, Pock, Cremers, Bischof and Chambolle 2009, Esser, Zhang and Chan 2010, Chambolle and Pock 2011], which allows to apply it to nonconvex regularizers as first proposed for truncated quadratic penaliz...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	34,466
2006.00457	Construction of MDS Euclidean Self-Dual Codes via Two Subsets	The parameters of a $q$-ary MDS Euclidean self-dual codes are completely determined by its length and the construction of MDS Euclidean self-dual codes with new length has been widely investigated in recent years. In this paper, we give a further study on the construction of MDS Euclidean self-dual codes via generalize...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	179,475
2209.15276	Machine Unlearning Method Based On Projection Residual	Machine learning models (mainly neural networks) are used more and more in real life. Users feed their data to the model for training. But these processes are often one-way. Once trained, the model remembers the data. Even when data is removed from the dataset, the effects of these data persist in the model. With more ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	320,550
2111.15407	Monotone one-port circuits	Maximal monotonicity is explored as a generalization of the linear theory of passivity, aiming at an algorithmic input/output analysis of physical models. The theory is developed for maximal monotone one-port circuits, formed by the series and parallel interconnection of basic elements. These circuits generalize passiv...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	268,915
2008.12691	On Transfer Learning of Traditional Frequency and Time Domain Features in Turning	There has been an increasing interest in leveraging machine learning tools for chatter prediction and diagnosis in discrete manufacturing processes. Some of the most common features for studying chatter include traditional signal processing tools such as Fast Fourier Transform (FFT), Power Spectral Density (PSD), and t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	193,653
cs/0006047	Geometric Morphology of Granular Materials	We present a new method to transform the spectral pixel information of a micrograph into an affine geometric description, which allows us to analyze the morphology of granular materials. We use spectral and pulse-coupled neural network based segmentation techniques to generate blobs, and a newly developed algorithm to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	537,147
1405.2708	Application of Modified Multi Model Predictive Control Algorithm to Fluid Catalytic Cracking Unit	This paper presents a modified multi model predictive control algorithm for the control of riser outlet temperature and regenerator temperature for the fluid catalytic cracking unit (FCCU). The models of the fluid catalytic cracking unit are estimated using subspace identification (N4SID) algorithm. The PRBS signal is ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	33,013
2107.13800	CI-Net: Contextual Information for Joint Semantic Segmentation and Depth Estimation	Monocular depth estimation and semantic segmentation are two fundamental goals of scene understanding. Due to the advantages of task interaction, many works study the joint task learning algorithm. However, most existing methods fail to fully leverage the semantic labels, ignoring the provided context structures and on...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	248,308
2009.04796	XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification	Multivariate Time Series (MTS) classification has gained importance over the past decade with the increase in the number of temporal datasets in multiple domains. The current state-of-the-art MTS classifier is a heavyweight deep learning approach, which outperforms the second-best MTS classifier only on large datasets....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	195,147
2404.09828	Interaction as Explanation: A User Interaction-based Method for Explaining Image Classification Models	In computer vision, explainable AI (xAI) methods seek to mitigate the 'black-box' problem by making the decision-making process of deep learning models more interpretable and transparent. Traditional xAI methods concentrate on visualizing input features that influence model predictions, providing insights primarily sui...	true	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	446,846
2311.13589	Risk-sensitive Markov Decision Process and Learning under General Utility Functions	Reinforcement Learning (RL) has gained substantial attention across diverse application domains and theoretical investigations. Existing literature on RL theory largely focuses on risk-neutral settings where the decision-maker learns to maximize the expected cumulative reward. However, in practical scenarios such as po...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	409,788
2206.12614	BokehMe: When Neural Rendering Meets Classical Rendering	We propose BokehMe, a hybrid bokeh rendering framework that marries a neural renderer with a classical physically motivated renderer. Given a single image and a potentially imperfect disparity map, BokehMe generates high-resolution photo-realistic bokeh effects with adjustable blur size, focal plane, and aperture shape...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	304,660
2312.10094	Evaluative Item-Contrastive Explanations in Rankings	The remarkable success of Artificial Intelligence in advancing automated decision-making is evident both in academia and industry. Within the plethora of applications, ranking systems hold significant importance in various domains. This paper advocates for the application of a specific form of Explainable AI -- namely,...	true	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	false	false	416,006
2102.04895	Leveraging cross-platform data to improve automated hate speech detection	Hate speech is increasingly prevalent online, and its negative outcomes include increased prejudice, extremism, and even offline hate crime. Automatic detection of online hate speech can help us to better understand these impacts. However, while the field has recently progressed through advances in natural language pro...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	219,258
1909.04236	Online Planning with Lookahead Policies	Real Time Dynamic Programming (RTDP) is an online algorithm based on Dynamic Programming (DP) that acts by 1-step greedy planning. Unlike DP, RTDP does not require access to the entire state space, i.e., it explicitly handles the exploration. This fact makes RTDP particularly appealing when the state space is large and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	144,739
2406.17196	Coded Kalman Filtering over MIMO Gaussian Channels with Feedback	We consider the problem of remotely stabilizing a linear dynamical system. In this setting, a sensor co-located with the system communicates the system's state to a controller over a noisy communication channel with feedback. The objective of the controller (decoder) is to use the channel outputs to estimate the vector...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	467,461
2112.07031	Teaching a Robot to Walk Using Reinforcement Learning	Classical control techniques such as PID and LQR have been used effectively in maintaining a system state, but these techniques become more difficult to implement when the model dynamics increase in complexity and sensitivity. For adaptive robotic locomotion tasks with several degrees of freedom, this task becomes infe...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	271,351
2309.13893	Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments	Navigating complex and dynamic environments requires autonomous vehicles (AVs) to reason about both visible and occluded regions. This involves predicting the future motion of observed agents, inferring occluded ones, and modeling their interactions based on vectorized scene representations of the partially observable ...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	394,397
1702.06527	Competition and Selection Among Conventions	In many domains, a latent competition among different conventions determines which one will come to dominate. One sees such effects in the success of community jargon, of competing frames in political rhetoric, or of terminology in technical contexts. These effects have become widespread in the online domain, where the...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	68,629
2404.04062	Derivative-free tree optimization for complex systems	A tremendous range of design tasks in materials, physics, and biology can be formulated as finding the optimum of an objective function depending on many parameters without knowing its closed-form expression or the derivative. Traditional derivative-free optimization techniques often rely on strong assumptions about ob...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	444,498
1503.02389	Random Coding Error Exponents for the Two-User Interference Channel	This paper is about deriving lower bounds on the error exponents for the two-user interference channel under the random coding regime for several ensembles. Specifically, we first analyze the standard random coding ensemble, where the codebooks are comprised of independently and identically distributed (i.i.d.) codewor...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	40,939
2001.01958	A kernel Principal Component Analysis (kPCA) digest with a new backward mapping (pre-image reconstruction) strategy	Methodologies for multidimensionality reduction aim at discovering low-dimensional manifolds where data ranges. Principal Component Analysis (PCA) is very effective if data have linear structure. But fails in identifying a possible dimensionality reduction if data belong to a nonlinear low-dimensional manifold. For non...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	159,626
2207.00345	HyperTensioN and Total-order Forward Decomposition optimizations	Hierarchical Task Networks (HTN) planners generate plans using a decomposition process with extra domain knowledge to guide search towards a planning task. While domain experts develop HTN descriptions, they may repeatedly describe the same preconditions, or methods that are rarely used or possible to be decomposed. By...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	true	false	false	false	305,725
2412.04011	A Note on Spectral Map	In molecular dynamics (MD) simulations, transitions between states are often rare events due to energy barriers that exceed the thermal temperature. Because of their infrequent occurrence and the huge number of degrees of freedom in molecular systems, understanding the physical properties that drive rare events is imme...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	514,217
1611.04226	An algebraic framework for end-to-end physical-layer network coding	We propose an algebraic setup for end-to-end physical-layer network coding based on submodule transmission. We introduce a distance function between modules, describe how it relates to information loss and errors, and show how to compute it. Then we propose a definition of submodule error-correcting code, and investiga...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	63,811
1912.06466	Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds	Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design. Despite the recent progress in deep generative modelling, synthesis of finely detailed 3D surfaces, such as high-resolution point clouds, from scr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	157,360
2010.11720	A study of the Multicriteria decision analysis based on the time-series features and a TOPSIS method proposal for a tensorial approach	A number of Multiple Criteria Decision Analysis (MCDA) methods have been developed to rank alternatives based on several decision criteria. Usually, MCDA methods deal with the criteria value at the time the decision is made without considering their evolution over time. However, it may be relevant to consider the crite...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	202,393
1704.03594	Deep Contextual Recurrent Residual Networks for Scene Labeling	Designed as extremely deep architectures, deep residual networks which provide a rich visual representation and offer robust convergence behaviors have recently achieved exceptional performance in numerous computer vision problems. Being directly applied to a scene labeling problem, however, they were limited to captur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	71,658
2208.13101	An event detection technique using social media data	People post information about different topics which are in their active vocabulary over social media platforms (like Twitter, Facebook, PInterest and Google+). They follow each other and it is more likely that the person who posts information about current happenings will receive better response. Manual analysis of hu...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	314,955
2006.13882	Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics	We propose an automatic method for pain intensity measurement from video. For each video, pain intensity was measured using the dynamics of facial movement using 66 facial points. Gram matrices formulation was used for facial points trajectory representations on the Riemannian manifold of symmetric positive semi-defini...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	184,060
1910.04927	GREASE: A Generative Model for Relevance Search over Knowledge Graphs	Relevance search is to find top-ranked entities in a knowledge graph (KG) that are relevant to a query entity. Relevance is ambiguous, particularly over a schema-rich KG like DBpedia which supports a wide range of different semantics of relevance based on numerous types of relations and attributes. As users may lack th...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	148,908
cs/0601060	Robot Swarms in an Uncertain World: Controllable Adaptability	There is a belief that complexity and chaos are essential for adaptability. But life deals with complexity every moment, without the chaos that engineers fear so, by invoking goal-directed behaviour. Goals can be programmed. That is why living organisms give us hope to achieve adaptability in robots. In this paper a me...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	539,200
2203.11912	What can we Learn Even From the Weakest? Learning Sketches for Programmatic Strategies	In this paper we show that behavioral cloning can be used to learn effective sketches of programmatic strategies. We show that even the sketches learned by cloning the behavior of weak players can help the synthesis of programmatic strategies. This is because even weak players can provide helpful information, e.g., tha...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	287,084
1803.02179	The Impact of Semantic Context Cues on the User Acceptance of Tag Recommendations: An Online Study	In this paper, we present the results of an online study with the aim to shed light on the impact that semantic context cues have on the user acceptance of tag recommendations. Therefore, we conducted a work-integrated social bookmarking scenario with 17 university employees in order to compare the user acceptance of a...	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	92,007
2207.14514	Factorizable Joint Shift in Multinomial Classification	Factorizable joint shift (FJS) was recently proposed as a type of dataset shift for which the complete characteristics can be estimated from feature data observations on the test dataset by a method called Joint Importance Aligning. For the multinomial (multiclass) classification setting, we derive a representation of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	310,614
1510.07790	Distributed Real-Time Non-Linear Receding Horizon Control Methodology for Multi-Agent Consensus Problems	This work investigates the consensus problem for multi-agent nonlinear systems through the distributed real-time nonlinear receding horizon control methodology. With this work, we develop a scheme to reach the consensus for nonlinear multi agent systems under fixed directed/undirected graph(s) without the need of any l...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	48,234
2204.05149	The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink	Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x and CO2 emissions up to 1000x. By following best practices, overall ML energy use (across research, development, and production) held steady...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	290,931
2310.18265	Structured Semidefinite Programming for Recovering Structured Preconditioners	We develop a general framework for finding approximately-optimal preconditioners for solving linear systems. Leveraging this framework we obtain improved runtimes for fundamental preconditioning and linear system solving problems including the following. We give an algorithm which, given positive definite $\mathbf{K} \...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	403,449
2410.11279	Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study	Recent empirical studies have identified fixed point iteration phenomena in deep neural networks, where the hidden state tends to stabilize after several layers, showing minimal change in subsequent layers. This observation has spurred the development of practical methodologies, such as accelerating inference by bypass...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	498,480
1907.12667	Reinforced Dynamic Reasoning for Conversational Question Generation	This paper investigates a new task named Conversational Question Generation (CQG) which is to generate a question based on a passage and a conversation history (i.e., previous turns of question-answer pairs). CQG is a crucial task for developing intelligent agents that can drive question-answering style conversations o...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	140,167
2212.06300	Accidental Turntables: Learning 3D Pose by Watching Objects Turn	We propose a technique for learning single-view 3D object pose estimation models by utilizing a new source of data -- in-the-wild videos where objects turn. Such videos are prevalent in practice (e.g., cars in roundabouts, airplanes near runways) and easy to collect. We show that classical structure-from-motion algorit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	336,062
1912.08322	Finding Effective Geo-Social Group for Impromptu Activity with Multiple Demands	Geo-social group search aims to find a group of people proximate to a location while socially related. One of the driven applications for geo-social group search is organizing an impromptu activity. This is because the social cohesiveness of a found geo-social group ensures a good communication atmosphere and the spati...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	157,802
2312.13596	Anchoring Path for Inductive Relation Prediction in Knowledge Graphs	Aiming to accurately predict missing edges representing relations between entities, which are pervasive in real-world Knowledge Graphs (KGs), relation prediction plays a critical role in enhancing the comprehensiveness and utility of KGs. Recent research focuses on path-based methods due to their inductive and explaina...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	417,359
1811.05827	Jointly identifying opinion mining elements and fuzzy measurement of opinion intensity to analyze product features	Opinion mining mainly involves three elements: feature and feature-of relations, opinion expressions and the related opinion attributes (e.g. Polarity), and feature-opinion relations. Although many works have emerged to achieve its aim of gaining information, the previous researches typically handled each of the three ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	113,395
2306.10702	A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News	Online news platforms commonly employ personalized news recommendation methods to assist users in discovering interesting articles, and many previous works have utilized language model techniques to capture user interests and understand news content. With the emergence of large language models like GPT-3 and T-5, a new...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	374,329
2405.15081	Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization	Independent and identically distributed (i.i.d.) data is essential to many data analysis and modeling techniques. In the medical domain, collecting data from multiple sites or institutions is a common strategy that guarantees sufficient clinical diversity, determined by the decentralized nature of medical data. However...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	456,732
2004.08981	Stochastic gradient algorithms from ODE splitting perspective	We present a different view on stochastic optimization, which goes back to the splitting schemes for approximate solutions of ODE. In this work, we provide a connection between stochastic gradient descent approach and first-order splitting scheme for ODE. We consider the special case of splitting, which is inspired by ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	173,223
0911.2948	Spatial Analysis of Opportunistic Downlink Relaying in a Two-Hop Cellular System	We consider a two-hop cellular system in which the mobile nodes help the base station by relaying information to the dead spots. While two-hop cellular schemes have been analyzed previously, the distribution of the node locations has not been explicitly taken into account. In this paper, we model the node locations of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	4,947
2501.18664	Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution	Deep learning has achieved significant success in single hyperspectral image super-resolution (SHSR); however, the high spectral dimensionality leads to a heavy computational burden, thus making it difficult to deploy in real-time scenarios. To address this issue, this paper proposes a novel lightweight SHSR network, i...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	528,807
2412.08508	Comparative Opinion Mining in Product Reviews: Multi-perspective Prompt-based Learning	Comparative reviews are pivotal in understanding consumer preferences and influencing purchasing decisions. Comparative Quintuple Extraction (COQE) aims to identify five key components in text: the target entity, compared entities, compared aspects, opinions on these aspects, and polarity. Extracting precise comparativ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	516,115
2011.10492	A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks	The Universal Trigger (UniTrigger) is a recently-proposed powerful adversarial textual attack method. Utilizing a learning-based mechanism, UniTrigger generates a fixed phrase that, when added to any benign inputs, can drop the prediction accuracy of a textual neural network (NN) model to near zero on a target class. T...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	207,528
2209.13006	Dynamic Unicast-Multicast Scheduling for Age-Optimal Information Dissemination in Vehicular Networks	This paper investigates the problem of minimizing the age-of-information (AoI) and transmit power consumption in a vehicular network, where a roadside unit (RSU) provides timely updates about a set of physical processes to vehicles. Each vehicle is interested in maintaining the freshness of its information status about...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	319,731
1710.06578	Acceleration of Gradient-based Path Integral Method for Efficient Optimal and Inverse Optimal Control	This paper deals with a new accelerated path integral method, which iteratively searches optimal controls with a small number of iterations. This study is based on the recent observations that a path integral method for reinforcement learning can be interpreted as gradient descent. This observation also applies to an i...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	82,800
2205.10320	Nothing makes sense in deep learning, except in the light of evolution	Deep Learning (DL) is a surprisingly successful branch of machine learning. The success of DL is usually explained by focusing analysis on a particular recent algorithm and its traits. Instead, we propose that an explanation of the success of DL must look at the population of all algorithms in the field and how they ha...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	297,650
2404.07687	Chaos in Motion: Unveiling Robustness in Remote Heart Rate Measurement through Brain-Inspired Skin Tracking	Heart rate is an important physiological indicator of human health status. Existing remote heart rate measurement methods typically involve facial detection followed by signal extraction from the region of interest (ROI). These SOTA methods have three serious problems: (a) inaccuracies even failures in detection caused...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	445,936
1310.8540	Quantitative Assessment of TV White Space in India	Licensed but unutilized television (TV) band spectrum is called as TV white space in the literature. Ultra high frequency (UHF) TV band spectrum has very good wireless radio propagation characteristics. The amount of TV white space in the UHF TV band in India is of interest. Comprehensive quantitative assessment and es...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,114
1506.05011	Bayesian representation learning with oracle constraints	Representation learning systems typically rely on massive amounts of labeled data in order to be trained to high accuracy. Recently, high-dimensional parametric models like neural networks have succeeded in building rich representations using either compressive, reconstructive or supervised criteria. However, the seman...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	44,247
1106.3745	Composition with Target Constraints	It is known that the composition of schema mappings, each specified by source-to-target tgds (st-tgds), can be specified by a second-order tgd (SO tgd). We consider the question of what happens when target constraints are allowed. Specifically, we consider the question of specifying the composition of standard schema m...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	10,908

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