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
2102.04271	Curse of Dimensionality for TSK Fuzzy Neural Networks: Explanation and Solutions	Takagi-Sugeno-Kang (TSK) fuzzy system with Gaussian membership functions (MFs) is one of the most widely used fuzzy systems in machine learning. However, it usually has difficulty handling high-dimensional datasets. This paper explores why TSK fuzzy systems with Gaussian MFs may fail on high-dimensional inputs. After t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,055
2004.04571	Learning Bayesian Networks that enable full propagation of evidence	This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent. This assumption can be viewed as a learning constraint geared towards cases where the input variables are known or assumed to be dependent. It addresses the pro...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	171,918
2312.06634	Data-Driven Bifurcation Analysis via Learning of Homeomorphism	This work proposes a data-driven approach for bifurcation analysis in nonlinear systems when the governing differential equations are not available. Specifically, regularized regression with barrier terms is used to learn a homeomorphism that transforms the underlying system to a reference linear dynamics -- either an ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	414,600
2111.15192	PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction	Stereo matching is an important task in computer vision which has drawn tremendous research attention for decades. While in terms of disparity accuracy, density and data size, public stereo datasets are difficult to meet the requirements of models. In this paper, we aim to address the issue between datasets and models ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,854
2310.10959	Origami-inspired Bi-directional Actuator with Orthogonal Actuation	Origami offers a promising alternative for designing innovative soft robotic actuators. While features of origami, such as bi-directional motion and structural anisotropy, haven't been extensively explored in the past, this letter presents a novel design inspired by origami tubes for a bi-directional actuator. This act...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	400,455
2110.09182	Graph Partner Neural Networks for Semi-Supervised Learning on Graphs	Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, link prediction, and graph classification. However, it is inevitable for deep GCNs to suffer from an over-smoothing issue that the representa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	261,716
2209.13635	Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning	The pioneering method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervised algorithms to learn from unlabeled data. However, it often suffers from label inconsistency or limited diversity, which leads to poor per...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	319,972
1902.10815	Generalizing Deep Learning MRI Reconstruction across Different Domains	We look into the robustness of deep learning based MRI reconstruction when tested on unseen contrasts and organs. We then propose to generalize the network by training with large publicly-available natural image datasets with synthesized phase information to achieve high cross-domain reconstruction performance which is...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	122,775
2006.03175	Simulating COVID-19 in a University Environment	Residential colleges and universities face unique challenges in providing in-person instruction during the COVID-19 pandemic. Administrators are currently faced with decisions about whether to open during the pandemic and what modifications of their normal operations might be necessary to protect students, faculty and ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	180,231
2103.12141	Performance Bounds for Neural Network Estimators: Applications in Fault Detection	We exploit recent results in quantifying the robustness of neural networks to input variations to construct and tune a model-based anomaly detector, where the data-driven estimator model is provided by an autoregressive neural network. In tuning, we specifically provide upper bounds on the rate of false alarms expected...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	226,058
2412.18619	Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey	Building on the foundations of language modeling in natural language processing, Next Token Prediction (NTP) has evolved into a versatile training objective for machine learning tasks across various modalities, achieving considerable success. As Large Language Models (LLMs) have advanced to unify understanding and gene...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	true	520,498
2012.03405	The Neural Coding Framework for Learning Generative Models	Neural generative models can be used to learn complex probability distributions from data, to sample from them, and to produce probability density estimates. We propose a computational framework for developing neural generative models inspired by the theory of predictive processing in the brain. According to predictive...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	210,097
2303.06347	User Retention-oriented Recommendation with Decision Transformer	Improving user retention with reinforcement learning~(RL) has attracted increasing attention due to its significant importance in boosting user engagement. However, training the RL policy from scratch without hurting users' experience is unavoidable due to the requirement of trial-and-error searches. Furthermore, the o...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	350,808
1912.06907	Migrating Monarch Butterfly Localization Using Multi-Sensor Fusion Neural Networks	Details of Monarch butterfly migration from the U.S. to Mexico remain a mystery due to lack of a proper localization technology to accurately localize and track butterfly migration. In this paper, we propose a deep learning based butterfly localization algorithm that can estimate a butterfly's daily location by analyzi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	157,462
2006.00988	Attention Word Embedding	Word embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models. The popular continuous bag-of-words (CBOW) model of word2vec learns a vector embedding by masking a given word in a sentence and then using the other words as a conte...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	179,635
2405.16369	Lane Detection using Graph Search and Geometric Constraints for Formula Student Driverless	Lane detection is a fundamental task in autonomous driving. While the problem is typically formulated as the detection of continuous boundaries, we study the problem of detecting lane boundaries that are sparsely marked by 2D points with many false positives. This problem arises in the Formula Student Driverless (FSD) ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	457,382
1301.4552	Sliding Mode Control for Torque Evolution of a Double Feed Asynchronous Generator	This paper proposes a robust control of doublefed induction generator of wind turbine to optimize its production: that means the energy quality and efficiency. The proposed control reposes in the sliding mode control using a multimodel approach which contributes on the minimization of the static error and the chatterin...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	21,253
1209.4535	Application of Fuzzy Mathematics to Speech-to-Text Conversion by Elimination of Paralinguistic Content	For the past few decades, man has been trying to create an intelligent computer which can talk and respond like he can. The task of creating a system that can talk like a human being is the primary objective of Automatic Speech Recognition. Various Speech Recognition techniques have been developed in theory and have be...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	18,658
1504.02921	Antenna Array Signal Processing for Quaternion-Valued Wireless Communication Systems	Quaternion-valued wireless communication systems have been studied in the past. Although progress has been made in this promising area, a crucial missing link is lack of effective and efficient quaternion-valued signal processing algorithms for channel equalisation and beamforming. With most recent developments in quat...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	41,971
2404.02634	3DStyleGLIP: Part-Tailored Text-Guided 3D Neural Stylization	3D stylization, the application of specific styles to three-dimensional objects, offers substantial commercial potential by enabling the creation of uniquely styled 3D objects tailored to diverse scenes. Recent advancements in artificial intelligence and text-driven manipulation methods have made the stylization proces...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	443,945
2004.11804	Deep Face Forgery Detection	Rapid progress in deep learning is continuously making it easier and cheaper to generate video forgeries. Hence, it becomes very important to have a reliable way of detecting these forgeries. This paper describes such an approach for various tampering scenarios. The problem is modelled as a per-frame binary classificat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	174,023
2201.06378	Self-Supervised Anomaly Detection by Self-Distillation and Negative Sampling	Detecting whether examples belong to a given in-distribution or are Out-Of-Distribution (OOD) requires identifying features specific to the in-distribution. In the absence of labels, these features can be learned by self-supervised techniques under the generic assumption that the most abstract features are those which ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	275,709
1710.00165	Dynamic Time-Aware Attention to Speaker Roles and Contexts for Spoken Language Understanding	Spoken language understanding (SLU) is an essential component in conversational systems. Most SLU component treats each utterance independently, and then the following components aggregate the multi-turn information in the separate phases. In order to avoid error propagation and effectively utilize contexts, prior work...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	81,816
1806.05220	Decentralized Ergodic Control: Distribution-Driven Sensing and Exploration for Multi-Agent Systems	We present a decentralized ergodic control policy for time-varying area coverage problems for multiple agents with nonlinear dynamics. Ergodic control allows us to specify distributions as objectives for area coverage problems for nonlinear robotic systems as a closed-form controller. We derive a variation to the ergod...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	100,417
2204.06211	5G Features and Standards for Vehicle Data Exploitation	Cars capture and generate huge volumes of data in real-time about the driving dynamics, the environment, and the driver and passengers' activities. Due to the proliferation of cooperative, connected and automated mobility (CCAM), the value of data from vehicles is getting strategic, not just for the automotive industry...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	291,262
2412.00953	BIGCity: A Universal Spatiotemporal Model for Unified Trajectory and Traffic State Data Analysis	Typical dynamic ST data includes trajectory data (representing individual-level mobility) and traffic state data (representing population-level mobility). Traditional studies often treat trajectory and traffic state data as distinct, independent modalities, each tailored to specific tasks within a single modality. Howe...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	512,880
2004.01860	Deblurring by Realistic Blurring	Existing deep learning methods for image deblurring typically train models using pairs of sharp images and their blurred counterparts. However, synthetically blurring images do not necessarily model the genuine blurring process in real-world scenarios with sufficient accuracy. To address this problem, we propose a new ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	171,033
2502.04367	Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors	Medical image classification is a vital research area that utilizes advanced computational techniques to improve disease diagnosis and treatment planning. Deep learning models, especially Convolutional Neural Networks (CNNs), have transformed this field by providing automated and precise analysis of complex medical ima...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	531,111
2204.01925	Online No-regret Model-Based Meta RL for Personalized Navigation	The interaction between a vehicle navigation system and the driver of the vehicle can be formulated as a model-based reinforcement learning problem, where the navigation systems (agent) must quickly adapt to the characteristics of the driver (environmental dynamics) to provide the best sequence of turn-by-turn driving ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	289,768
2404.17597	KamerRaad: Enhancing Information Retrieval in Belgian National Politics through Hierarchical Summarization and Conversational Interfaces	KamerRaad is an AI tool that leverages large language models to help citizens interactively engage with Belgian political information. The tool extracts and concisely summarizes key excerpts from parliamentary proceedings, followed by the potential for interaction based on generative AI that allows users to steadily bu...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	449,916
1811.10788	Reconstruction Loss Minimized FCN for Single Image Dehazing	Haze and fog reduce the visibility of outdoor scenes as a veil like semi-transparent layer appears over the objects. As a result, images captured under such conditions lack contrast. Image dehazing methods try to alleviate this problem by recovering a clear version of the image. In this paper, we propose a Fully Convol...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	114,586
2501.09262	On the convergence rate of noisy Bayesian Optimization with Expected Improvement	Expected improvement (EI) is one of the most widely used acquisition functions in Bayesian optimization (BO). Despite its proven success in applications for decades, important open questions remain on the theoretical convergence behaviors and rates for EI. In this paper, we contribute to the convergence theory of EI in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	525,069
1908.06592	Seq-SG2SL: Inferring Semantic Layout from Scene Graph Through Sequence to Sequence Learning	Generating semantic layout from scene graph is a crucial intermediate task connecting text to image. We present a conceptually simple, flexible and general framework using sequence to sequence (seq-to-seq) learning for this task. The framework, called Seq-SG2SL, derives sequence proxies for the two modality and a Trans...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	142,055
2010.13422	Lane detection in complex scenes based on end-to-end neural network	The lane detection is a key problem to solve the division of derivable areas in unmanned driving, and the detection accuracy of lane lines plays an important role in the decision-making of vehicle driving. Scenes faced by vehicles in daily driving are relatively complex. Bright light, insufficient light, and crowded ve...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	203,127
2502.01856	Reliability-Driven LiDAR-Camera Fusion for Robust 3D Object Detection	Accurate and robust 3D object detection is essential for autonomous driving, where fusing data from sensors like LiDAR and camera enhances detection accuracy. However, sensor malfunctions such as corruption or disconnection can degrade performance, and existing fusion models often struggle to maintain reliability when ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	530,062
2401.14591	Ricci flow-guided autoencoders in learning time-dependent dynamics	We present a manifold-based autoencoder method for learning dynamics in time, notably partial differential equations (PDEs), in which the manifold latent space evolves according to Ricci flow. This can be accomplished by parameterizing the latent manifold stage and subsequently simulating Ricci flow in a physics-inform...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	424,149
2502.09715	Evaluating GPT's Capability in Identifying Stages of Cognitive Impairment from Electronic Health Data	Identifying cognitive impairment within electronic health records (EHRs) is crucial not only for timely diagnoses but also for facilitating research. Information about cognitive impairment often exists within unstructured clinician notes in EHRs, but manual chart reviews are both time-consuming and error-prone. To addr...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	533,571
2202.08391	Graph Masked Autoencoders with Transformers	Recently, transformers have shown promising performance in learning graph representations. However, there are still some challenges when applying transformers to real-world scenarios due to the fact that deep transformers are hard to train from scratch and the quadratic memory consumption w.r.t. the number of nodes. In...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	280,852
2210.13030	Self-supervised Rewiring of Pre-trained Speech Encoders: Towards Faster Fine-tuning with Less Labels in Speech Processing	Pre-trained speech Transformers have facilitated great success across various speech processing tasks. However, fine-tuning these encoders for downstream tasks require sufficiently large training data to converge or to achieve state-of-the-art. In text domain this has been partly attributed to sub-optimality of the rep...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	326,022
2008.02763	Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network	In the field of multimedia, single image deraining is a basic pre-processing work, which can greatly improve the visual effect of subsequent high-level tasks in rainy conditions. In this paper, we propose an effective algorithm, called JDNet, to solve the single image deraining problem and conduct the segmentation and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,706
1910.01875	An adaptive hybrid algorithm for social networks to choose groups with independent members	Choosing a committee with independent members in social networks can be named as a problem in group selection and independence in the committee is considered as the main criterion of this selection. Independence is calculated based on the social distance between group members. Although there are many solutions to solve...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	false	148,066
1511.03629	A Continuous Max-Flow Approach to Cyclic Field Reconstruction	Reconstruction of an image from noisy data using Markov Random Field theory has been explored by both the graph-cuts and continuous max-flow community in the form of the Potts and Ishikawa models. However, neither model takes into account the particular cyclic topology of specific intensity types such as the hue in nat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	48,777
1703.06270	SIM-CE: An Advanced Simulink Platform for Studying the Brain of Caenorhabditis elegans	We introduce SIM-CE, an advanced, user-friendly modeling and simulation environment in Simulink for performing multi-scale behavioral analysis of the nervous system of Caenorhabditis elegans (C. elegans). SIM-CE contains an implementation of the mathematical models of C. elegans's neurons and synapses, in Simulink, whi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	70,202
1402.1258	In-Memory Database Systems - A Paradigm Shift	In today world, organizations like Google, Yahoo, Amazon, Facebook etc. are facing drastic increase in data. This leads to the problem of capturing, storing, managing and analyzing terabytes or petabytes of data, stored in multiple formats, from different internal and external sources. Moreover, new applications scenar...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	30,651
2412.01946	The Reality of AI and Biorisk	To accurately and confidently answer the question 'could an AI model or system increase biorisk', it is necessary to have both a sound theoretical threat model for how AI models or systems could increase biorisk and a robust method for testing that threat model. This paper provides an analysis of existing available res...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	513,317
2102.10446	Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images	Development of robust and accurate fully automated methods for medical image segmentation is crucial in clinical practice and radiomics studies. In this work, we contributed an automated approach for Head and Neck (H&N) primary tumor segmentation in combined positron emission tomography / computed tomography (PET/CT) i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	221,095
2404.19205	TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains	In this paper, we establish a benchmark for table visual question answering, referred to as the TableVQA-Bench, derived from pre-existing table question-answering (QA) and table structure recognition datasets. It is important to note that existing datasets have not incorporated images or QA pairs, which are two crucial...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	450,542
1705.08722	Open-Category Classification by Adversarial Sample Generation	In real-world classification tasks, it is difficult to collect training samples from all possible categories of the environment. Therefore, when an instance of an unseen class appears in the prediction stage, a robust classifier should be able to tell that it is from an unseen class, instead of classifying it to be any...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	74,071
2110.01053	Treeging	Treeging combines the flexible mean structure of regression trees with the covariance-based prediction strategy of kriging into the base learner of an ensemble prediction algorithm. In so doing, it combines the strengths of the two primary types of spatial and space-time prediction models: (1) models with flexible mean...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	258,635
1803.08476	Word sense induction using word embeddings and community detection in complex networks	Word Sense Induction (WSI) is the ability to automatically induce word senses from corpora. The WSI task was first proposed to overcome the limitations of manually annotated corpus that are required in word sense disambiguation systems. Even though several works have been proposed to induce word senses, existing system...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	93,269
1703.01049	Deconvolving Feedback Loops in Recommender Systems	Collaborative filtering is a popular technique to infer users' preferences on new content based on the collective information of all users preferences. Recommender systems then use this information to make personalized suggestions to users. When users accept these recommendations it creates a feedback loop in the recom...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	69,286
2311.04219	OtterHD: A High-Resolution Multi-modality Model	In this paper, we present OtterHD-8B, an innovative multimodal model evolved from Fuyu-8B, specifically engineered to interpret high-resolution visual inputs with granular precision. Unlike conventional models that are constrained by fixed-size vision encoders, OtterHD-8B boasts the ability to handle flexible input dim...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	406,140
2211.08932	Semantic Communications in Multi-user Wireless Networks	This article investigates the exploitation of semantic communications in multi-user networks. We propose a novel heterogeneous semantic and bit multi-user framework for providing flawless, customized, and intelligent information transmission. We discuss both orthogonal multiple access (OMA) and non-orthogonal multiple ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	330,817
1810.12059	Studio e confronto delle strutture di Apache Spark	English. This document is designed to study the data structures that can be used in the Apache Spark framework and to evaluate the best performing ones to implement solutions, in particular we will evaluate advantages / disadvantages deriving from the use of Dataset for job creation. The observation of the results prov...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	111,672
2501.04871	RieszBoost: Gradient Boosting for Riesz Regression	Answering causal questions often involves estimating linear functionals of conditional expectations, such as the average treatment effect or the effect of a longitudinal modified treatment policy. By the Riesz representation theorem, these functionals can be expressed as the expected product of the conditional expectat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	523,375
cs/0504020	The Viterbi Algorithm: A Personal History	The story of the Viterbi algorithm (VA) is told from a personal perspective. Applications both within and beyond communications are discussed. In brief summary, the VA has proved to be an extremely important algorithm in a surprising variety of fields.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	538,645
2308.15231	Multi-party Goal Tracking with LLMs: Comparing Pre-training, Fine-tuning, and Prompt Engineering	This paper evaluates the extent to which current Large Language Models (LLMs) can capture task-oriented multi-party conversations (MPCs). We have recorded and transcribed 29 MPCs between patients, their companions, and a social robot in a hospital. We then annotated this corpus for multi-party goal-tracking and intent-...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	388,612
1111.5454	The Management and Use of Social Network Sites in a Government Department	In this paper we report findings from a study of social network site use in a UK Government department. We have investigated this from a managerial, organisational perspective. We found at the study site that there are already several social network technologies in use, and that these: misalign with and problematize or...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	13,140
2211.10511	Knowledge Graph Generation From Text	In this work we propose a novel end-to-end multi-stage Knowledge Graph (KG) generation system from textual inputs, separating the overall process into two stages. The graph nodes are generated first using pretrained language model, followed by a simple edge construction head, enabling efficient KG extraction from the t...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	331,328
2012.00314	Decentralized Multi-Agent Linear Bandits with Safety Constraints	We study decentralized stochastic linear bandits, where a network of $N$ agents acts cooperatively to efficiently solve a linear bandit-optimization problem over a $d$-dimensional space. For this problem, we propose DLUCB: a fully decentralized algorithm that minimizes the cumulative regret over the entire network. At ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,093
2208.06746	Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems	The field of generating recommendations within the framework of causal inference has seen a recent surge, with recommendations being likened to treatments. This approach enhances insights into the influence of recommendations on user behavior and helps in identifying the underlying factors. Existing research has often ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	312,813
2406.01594	DiffUHaul: A Training-Free Method for Object Dragging in Images	Text-to-image diffusion models have proven effective for solving many image editing tasks. However, the seemingly straightforward task of seamlessly relocating objects within a scene remains surprisingly challenging. Existing methods addressing this problem often struggle to function reliably in real-world scenarios du...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	460,385
2303.00721	Bootstrapping Parallel Anchors for Relative Representations	The use of relative representations for latent embeddings has shown potential in enabling latent space communication and zero-shot model stitching across a wide range of applications. Nevertheless, relative representations rely on a certain amount of parallel anchors to be given as input, which can be impractical to ob...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	348,688
1507.04935	The Inflection Point of the Speed-Density Relation and the Social Force Model	It has been argued that the speed-density digram of pedestrian movement has an inflection point. This inflection point was found empirically in investigations of closed-loop single-file pedestrian movement. The reduced complexity of single-file movement does not only allow a higher precision for the evaluation of empir...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	45,232
2207.03235	Consistent discretization of finite/fixed-time controllers	The paper proposes an algorithm for a discretization (sampled-time implementation) of a homogeneous control preserving the finite-time and nearly fixed-time stability property of the original (sampling-free) system. The sampling period is assumed to be constant. Both single-input and multiple-input cases are considered...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	306,773
2005.09148	Out-of-Core GPU Gradient Boosting	GPU-based algorithms have greatly accelerated many machine learning methods; however, GPU memory is typically smaller than main memory, limiting the size of training data. In this paper, we describe an out-of-core GPU gradient boosting algorithm implemented in the XGBoost library. We show that much larger datasets can ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	177,832
2202.07648	EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs	How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs represent facts about entities and their relations, where each fact is associated with a timestamp. Reasoning over TKGs, i.e., inferring new facts from time-evolving KGs, is crucial for many applications to provide intelligent services. ...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	280,618
1907.06333	Myers-Briggs Personality Classification and Personality-Specific Language Generation Using Pre-trained Language Models	The Myers-Briggs Type Indicator (MBTI) is a popular personality metric that uses four dichotomies as indicators of personality traits. This paper examines the use of pre-trained language models to predict MBTI personality types based on scraped labeled texts. The proposed model reaches an accuracy of $0.47$ for correct...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,593
2409.18061	Optimal Protocols for Continual Learning via Statistical Physics and Control Theory	Artificial neural networks often struggle with catastrophic forgetting when learning multiple tasks sequentially, as training on new tasks degrades the performance on previously learned ones. Recent theoretical work has addressed this issue by analysing learning curves in synthetic frameworks under predefined training ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	492,092
1805.07509	Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation	Recent Image-to-Image Translation algorithms have achieved significant progress in neural style transfer and image attribute manipulation tasks. However, existing approaches require exhaustively labelling training data, which is labor demanding, difficult to scale up, and hard to migrate into new domains. To overcome s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	97,852
2011.07231	ActBERT: Learning Global-Local Video-Text Representations	In this paper, we introduce ActBERT for self-supervised learning of joint video-text representations from unlabeled data. First, we leverage global action information to catalyze the mutual interactions between linguistic texts and local regional objects. It uncovers global and local visual clues from paired video sequ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	206,487
1904.13285	Performing Structured Improvisations with pre-trained Deep Learning Models	The quality of outputs produced by deep generative models for music have seen a dramatic improvement in the last few years. However, most deep learning models perform in "offline" mode, with few restrictions on the processing time. Integrating these types of models into a live structured performance poses a challenge b...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	129,350
1802.07956	Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation	A new obstacle detection algorithm for unmanned surface vehicles (USVs) is presented. A state-of-the-art graphical model for semantic segmentation is extended to incorporate boat pitch and roll measurements from the on-board inertial measurement unit (IMU), and a stereo verification algorithm that consolidates tentativ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	91,006
2001.04829	Bayesian Inversion Of Generative Models For Geologic Storage Of Carbon Dioxide	Carbon capture and storage (CCS) can aid decarbonization of the atmosphere to limit further global temperature increases. A framework utilizing unsupervised learning is used to generate a range of subsurface geologic volumes to investigate potential sites for long-term storage of carbon dioxide. Generative adversarial ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	160,372
2502.05479	Model Validity in Observers: When to Increase the Complexity of Your Model?	Model validity is key to the accurate and safe behavior of autonomous vehicles. Using invalid vehicle models in the different plan and control vehicle frameworks puts the stability of the vehicle, and thus its safety at stake. In this work, we analyze the validity of several popular vehicle models used in the literatur...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	531,633
2111.13974	Exploring Transformer Based Models to Identify Hate Speech and Offensive Content in English and Indo-Aryan Languages	Hate speech is considered to be one of the major issues currently plaguing online social media. Repeated and repetitive exposure to hate speech has been shown to create physiological effects on the target users. Thus, hate speech, in all its forms, should be addressed on these platforms in order to maintain good health...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	268,448
2303.11040	Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving	3D object detection is an important task in autonomous driving to perceive the surroundings. Despite the excellent performance, the existing 3D detectors lack the robustness to real-world corruptions caused by adverse weathers, sensor noises, etc., provoking concerns about the safety and reliability of autonomous drivi...	false	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	false	false	352,680
2206.13637	Utility Theory for Sequential Decision Making	The von Neumann-Morgenstern (VNM) utility theorem shows that under certain axioms of rationality, decision-making is reduced to maximizing the expectation of some utility function. We extend these axioms to increasingly structured sequential decision making settings and identify the structure of the corresponding utili...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	305,035
1610.03736	Burst Transmission Symbol Synchronization in the Presence of Cycle Slip Arising from Different Clock Frequencies	In digital communication systems different clock frequencies of transmitter and receiver usually is translated into cycle slips. Receivers might experience different sampling frequencies from transmitter due to manufacturing imperfection, Doppler Effect introduced by channel or wrong estimation of symbol rate. Timing s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	62,292
2303.04327	Using Memory-Based Learning to Solve Tasks with State-Action Constraints	Tasks where the set of possible actions depend discontinuously on the state pose a significant challenge for current reinforcement learning algorithms. For example, a locked door must be first unlocked, and then the handle turned before the door can be opened. The sequential nature of these tasks makes obtaining final ...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	350,040
2405.00461	Enhancing Surgical Robots with Embodied Intelligence for Autonomous Ultrasound Scanning	Ultrasound robots are increasingly used in medical diagnostics and early disease screening. However, current ultrasound robots lack the intelligence to understand human intentions and instructions, hindering autonomous ultrasound scanning. To solve this problem, we propose a novel Ultrasound Embodied Intelligence syste...	true	false	false	false	true	false	false	true	true	false	false	false	false	false	false	false	false	false	450,930
2202.07190	Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters	This paper focuses on filter-level network pruning. A novel pruning method, termed CLR-RNF, is proposed. We first reveal a "long-tail" long-tail pruning problem in magnitude-based weight pruning methods, and then propose a computation-aware measurement for individual weight importance, followed by a Cross-Layer Ranking...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	280,466
1902.10873	Joint Design of Fronthauling and Hybrid Beamforming for Downlink C-RAN Systems	Hybrid beamforming is known to be a cost-effective and wide-spread solution for a system with large-scale antenna arrays. This work studies the optimization of the analog and digital components of the hybrid beamforming solution for remote radio heads (RRHs) in a downlink cloud radio access network (C-RAN) architecture...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	122,788
2402.05129	Best Practices for Text Annotation with Large Language Models	Large Language Models (LLMs) have ushered in a new era of text annotation, as their ease-of-use, high accuracy, and relatively low costs have meant that their use has exploded in recent months. However, the rapid growth of the field has meant that LLM-based annotation has become something of an academic Wild West: the ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	427,737
2108.02740	WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration	In this work, we present a novel method called WSDesc to learn 3D local descriptors in a weakly supervised manner for robust point cloud registration. Our work builds upon recent 3D CNN-based descriptor extractors, which leverage a voxel-based representation to parameterize local geometry of 3D points. Instead of using...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	249,430
2501.04926	FLowHigh: Towards Efficient and High-Quality Audio Super-Resolution with Single-Step Flow Matching	Audio super-resolution is challenging owing to its ill-posed nature. Recently, the application of diffusion models in audio super-resolution has shown promising results in alleviating this challenge. However, diffusion-based models have limitations, primarily the necessity for numerous sampling steps, which causes sign...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	523,394
2108.10535	Design and Performance Evaluation of Joint Sensing and Communication Integrated System for 5G MmWave Enabled CAVs	The safety of connected automated vehicles (CAVs) relies on the reliable and efficient raw data sharing from multiple types of sensors. The 5G millimeter wave (mmWave) communication technology can enhance the environment sensing ability of different isolated vehicles. In this paper, a joint sensing and communication in...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	251,924
2312.00215	Learning active tactile perception through belief-space control	Robots operating in an open world will encounter novel objects with unknown physical properties, such as mass, friction, or size. These robots will need to sense these properties through interaction prior to performing downstream tasks with the objects. We propose a method that autonomously learns tactile exploration p...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	411,961
2312.04862	Damage GAN: A Generative Model for Imbalanced Data	This study delves into the application of Generative Adversarial Networks (GANs) within the context of imbalanced datasets. Our primary aim is to enhance the performance and stability of GANs in such datasets. In pursuit of this objective, we introduce a novel network architecture known as Damage GAN, building upon the...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	413,860
2406.09998	Understanding Pedestrian Movement Using Urban Sensing Technologies: The Promise of Audio-based Sensors	While various sensors have been deployed to monitor vehicular flows, sensing pedestrian movement is still nascent. Yet walking is a significant mode of travel in many cities, especially those in Europe, Africa, and Asia. Understanding pedestrian volumes and flows is essential for designing safer and more attractive ped...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	464,184
2410.04870	On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent	The Adam optimizer is widely used for transformer optimization in practice, which makes understanding the underlying optimization mechanisms an important problem. However, due to the Adam's complexity, theoretical analysis of how it optimizes transformers remains a challenging task. Fortunately, Sign Gradient Descent (...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	495,479
2309.17395	AV-CPL: Continuous Pseudo-Labeling for Audio-Visual Speech Recognition	Audio-visual speech contains synchronized audio and visual information that provides cross-modal supervision to learn representations for both automatic speech recognition (ASR) and visual speech recognition (VSR). We introduce continuous pseudo-labeling for audio-visual speech recognition (AV-CPL), a semi-supervised m...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	395,755
1601.00073	Mimir: Bringing CTables into Practice	The present state of the art in analytics requires high upfront investment of human effort and computational resources to curate datasets, even before the first query is posed. So-called pay-as-you-go data curation techniques allow these high costs to be spread out, first by enabling queries over uncertain and incomple...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	50,601
2105.08286	Exploring Driving-aware Salient Object Detection via Knowledge Transfer	Recently, general salient object detection (SOD) has made great progress with the rapid development of deep neural networks. However, task-aware SOD has hardly been studied due to the lack of task-specific datasets. In this paper, we construct a driving task-oriented dataset where pixel-level masks of salient objects h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	235,718
2106.01489	Not All Knowledge Is Created Equal: Mutual Distillation of Confident Knowledge	Mutual knowledge distillation (MKD) improves a model by distilling knowledge from another model. However, \textit{not all knowledge is certain and correct}, especially under adverse conditions. For example, label noise usually leads to less reliable models due to undesired memorization \cite{zhang2017understanding,arpi...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	238,511
2407.04714	Efficient Hybrid Neuromorphic-Bayesian Model for Olfaction Sensing: Detection and Classification	Olfaction sensing in autonomous robotics faces challenges in dynamic operations, energy efficiency, and edge processing. It necessitates a machine learning algorithm capable of managing real-world odor interference, ensuring resource efficiency for mobile robotics, and accurately estimating gas features for critical ta...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	470,670
2108.08708	Czech News Dataset for Semantic Textual Similarity	This paper describes a novel dataset consisting of sentences with semantic similarity annotations. The data originate from the journalistic domain in the Czech language. We describe the process of collecting and annotating the data in detail. The dataset contains 138,556 human annotations divided into train and test se...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	true	false	false	251,351
1701.03452	Simplified Minimal Gated Unit Variations for Recurrent Neural Networks	Recurrent neural networks with various types of hidden units have been used to solve a diverse range of problems involving sequence data. Two of the most recent proposals, gated recurrent units (GRU) and minimal gated units (MGU), have shown comparable promising results on example public datasets. In this paper, we int...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	66,707
1812.00855	Towards Solving Text-based Games by Producing Adaptive Action Spaces	To solve a text-based game, an agent needs to formulate valid text commands for a given context and find the ones that lead to success. Recent attempts at solving text-based games with deep reinforcement learning have focused on the latter, i.e., learning to act optimally when valid actions are known in advance. In thi...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	115,359
2311.07884	Fair Abstractive Summarization of Diverse Perspectives	People from different social and demographic groups express diverse perspectives and conflicting opinions on a broad set of topics such as product reviews, healthcare, law, and politics. A fair summary should provide a comprehensive coverage of diverse perspectives without underrepresenting certain groups. However, cur...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	407,505

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