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
2011.05934	Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy	In this paper, we study the Empirical Risk Minimization (ERM) problem in the non-interactive Local Differential Privacy (LDP) model. Previous research on this problem \citep{smith2017interaction} indicates that the sample complexity, to achieve error $\alpha$, needs to be exponentially depending on the dimensionality $...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	206,089
2112.05074	Critical configurations for two projective views, a new approach	The problem of structure from motion is concerned with recovering 3-dimensional structure of an object from a set of 2-dimensional images. Generally, all information can be uniquely recovered if enough images and image points are provided, but there are certain cases where unique recovery is impossible; these are calle...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	270,724
2005.04006	Robust distributed model predictive control of linear systems: analysis and synthesis	To provide robustness of distributed model predictive control (DMPC), this work proposes a robust DMPC formulation for discrete-time linear systems subject to unknown-but-bounded disturbances. Taking advantage of the structure of certain classes of distributed systems seen in applications with interagent coupling like ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	176,329
1907.02797	Predicting e-commerce customer conversion from minimal temporal patterns on symbolized clickstream trajectories	Knowing if a user is a buyer or window shopper solely based on clickstream data is of crucial importance for e-commerce platforms seeking to implement real-time accurate NBA (next best action) policies. However, due to the low frequency of conversion events and the noisiness of browsing data, classifying user sessions ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	137,687
1301.2137	A Forgetting-based Approach to Merging Knowledge Bases	This paper presents a novel approach based on variable forgetting, which is a useful tool in resolving contradictory by filtering some given variables, to merging multiple knowledge bases. This paper first builds a relationship between belief merging and variable forgetting by using dilation. Variable forgetting is app...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	20,910
1410.2702	Generalized Hamming Weights of Irreducible Cyclic Codes	The generalized Hamming weight (GHW) $d_r(C)$ of linear codes $C$ is a natural generalization of the minimum Hamming distance $d(C)(=d_1(C))$ and has become one of important research objects in coding theory since Wei's originary work [23] in 1991. In this paper two general formulas on $d_r(C)$ for irreducible cyclic c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	36,641
2209.11471	Modeling and Leveraging Prerequisite Context in Recommendation	Prerequisites can play a crucial role in users' decision-making yet recommendation systems have not fully utilized such contextual background knowledge. Traditional recommendation systems (RS) mostly enrich user-item interactions where the context consists of static user profiles and item descriptions, ignoring the con...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	319,198
2006.10202	HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss	Recent works show that local descriptor learning benefits from the use of L2 normalisation, however, an in-depth analysis of this effect lacks in the literature. In this paper, we investigate how L2 normalisation affects the back-propagated descriptor gradients during training. Based on our observations, we propose HyN...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	182,796
2406.09366	Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations	Maximum Manifold Capacity Representations (MMCR) is a recent multi-view self-supervised learning (MVSSL) method that matches or surpasses other leading MVSSL methods. MMCR is intriguing because it does not fit neatly into any of the commonplace MVSSL lineages, instead originating from a statistical mechanical perspecti...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	463,894
2405.04146	pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving	Deep learning-based Autonomous Driving (AD) models often exhibit poor generalization due to data heterogeneity in an ever domain-shifting environment. While Federated Learning (FL) could improve the generalization of an AD model (known as FedAD system), conventional models often struggle with under-fitting as the amoun...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	452,465
2412.16271	Long-Term Upper-Limb Prosthesis Myocontrol via High-Density sEMG and Incremental Learning	Noninvasive human-machine interfaces such as surface electromyography (sEMG) have long been employed for controlling robotic prostheses. However, classical controllers are limited to few degrees of freedom (DoF). More recently, machine learning methods have been proposed to learn personalized controllers from user data...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	519,455
2311.09353	Flexible and Adaptive Manufacturing by Complementing Knowledge Representation, Reasoning and Planning with Reinforcement Learning	This paper describes a novel approach to adaptive manufacturing in the context of small batch production and customization. It focuses on integrating task-level planning and reasoning with reinforcement learning (RL) in the SkiROS2 skill-based robot control platform. This integration enhances the efficiency and adaptab...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	408,103
2401.01133	A Stochastic-MILP dispatch optimization model for Concentrated Solar Thermal under uncertainty	Concentrated Solar Thermal (CST) offers a promising solution for large-scale solar energy utilization as Thermal Energy Storage (TES) enables electricity generation independently of daily solar fluctuations, shifting to high-priced electricity intervals. The development of dispatch planning tools is mandatory to accoun...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	419,240
1403.7883	Multiple-Access Relay Wiretap Channel	In this paper, we investigate the effects of an additional trusted relay node on the secrecy of multiple-access wiretap channel (MAC-WT) by considering the model of multiple-access relay wiretap channel (MARC-WT). More specifically, first, we investigate the discrete memoryless MARC-WT. Three inner bounds (with respect...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	31,947
2005.01246	Generalized Reinforcement Meta Learning for Few-Shot Optimization	We present a generic and flexible Reinforcement Learning (RL) based meta-learning framework for the problem of few-shot learning. During training, it learns the best optimization algorithm to produce a learner (ranker/classifier, etc) by exploiting stable patterns in loss surfaces. Our method implicitly estimates the g...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	175,532
2112.10103	SAGA: Stochastic Whole-Body Grasping with Contact	The synthesis of human grasping has numerous applications including AR/VR, video games and robotics. While methods have been proposed to generate realistic hand-object interaction for object grasping and manipulation, these typically only consider interacting hand alone. Our goal is to synthesize whole-body grasping mo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	272,347
2102.00490	Online Markov Decision Processes with Aggregate Bandit Feedback	We study a novel variant of online finite-horizon Markov Decision Processes with adversarially changing loss functions and initially unknown dynamics. In each episode, the learner suffers the loss accumulated along the trajectory realized by the policy chosen for the episode, and observes aggregate bandit feedback: the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	217,803
2206.04771	Joint Entropy Search for Maximally-Informed Bayesian Optimization	Information-theoretic Bayesian optimization techniques have become popular for optimizing expensive-to-evaluate black-box functions due to their non-myopic qualities. Entropy Search and Predictive Entropy Search both consider the entropy over the optimum in the input space, while the recent Max-value Entropy Search con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,758
2305.11472	Testing System Intelligence	We discuss the adequacy of tests for intelligent systems and practical problems raised by their implementation. We propose the replacement test as the ability of a system to replace successfully another system performing a task in a given context. We show how it can characterize salient aspects of human intelligence th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	365,549
1905.12679	Nonvolatile Spintronic Memory Cells for Neural Networks	A new spintronic nonvolatile memory cell analogous to 1T DRAM with non-destructive read is proposed. The cells can be used as neural computing units. A dual-circuit neural network architecture is proposed to leverage these devices against the complex operations involved in convolutional networks. Simulations based on H...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	132,836
2203.05787	Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection	Co-salient object detection, with the target of detecting co-existed salient objects among a group of images, is gaining popularity. Recent works use the attention mechanism or extra information to aggregate common co-salient features, leading to incomplete even incorrect responses for target objects. In this paper, we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	284,912
2408.05933	Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models	With the growing demand for offline PDF chatbots in automotive industrial production environments, optimizing the deployment of large language models (LLMs) in local, low-performance settings has become increasingly important. This study focuses on enhancing Retrieval-Augmented Generation (RAG) techniques for processin...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	true	false	false	false	480,006
2002.05556	Sparse and Structured Visual Attention	Visual attention mechanisms are widely used in multimodal tasks, as visual question answering (VQA). One drawback of softmax-based attention mechanisms is that they assign some probability mass to all image regions, regardless of their adjacency structure and of their relevance to the text. In this paper, to better lin...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	163,937
2106.04404	The Struggle with Academic Plagiarism: Approaches based on Semantic Similarity	Academic plagiarism is a serious problem nowadays. Due to the existence of inexhaustible sources of digital information, today it is easier to plagiarize more than ever before. The good thing is that plagiarism detection techniques have improved and are powerful enough to detect attempts of plagiarism in education. We ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	239,708
2306.17042	Towards Grammatical Tagging for the Legal Language of Cybersecurity	Legal language can be understood as the language typically used by those engaged in the legal profession and, as such, it may come both in spoken or written form. Recent legislation on cybersecurity obviously uses legal language in writing, thus inheriting all its interpretative complications due to the typical abundan...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	376,572
2306.00541	Decomposing Global Feature Effects Based on Feature Interactions	Global feature effect methods, such as partial dependence plots, provide an intelligible visualization of the expected marginal feature effect. However, such global feature effect methods can be misleading, as they do not represent local feature effects of single observations well when feature interactions are present....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	370,051
2408.04961	In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation	We present lazy visual grounding, a two-stage approach of unsupervised object mask discovery followed by object grounding, for open-vocabulary semantic segmentation. Plenty of the previous art casts this task as pixel-to-text classification without object-level comprehension, leveraging the image-to-text classification...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	479,601
2009.05698	Relation Detection for Indonesian Language using Deep Neural Network -- Support Vector Machine	Relation Detection is a task to determine whether two entities are related or not. In this paper, we employ neural network to do relation detection between two named entities for Indonesian Language. We used feature such as word embedding, position embedding, POS-Tag embedding, and character embedding. For the model, w...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	195,395
2406.13650	Advanced Maximum Adhesion Tracking Strategies in Railway Traction Drives	Modern railway traction systems are often equipped with anti-slip control strategies to comply with performance and safety requirements. A certain amount of slip is needed to increase the torque transferred by the traction motors onto the rail. Commonly, constant slip control is used to limit the slip velocity between ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	465,935
2205.13769	Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection	Supervised deep learning models depend on massive labeled data. Unfortunately, it is time-consuming and labor-intensive to collect and annotate bitemporal samples containing desired changes. Transfer learning from pre-trained models is effective to alleviate label insufficiency in remote sensing (RS) change detection (...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	299,081
2203.06591	ORDSIM: Ordinal Regression for E-Commerce Query Similarity Prediction	Query similarity prediction task is generally solved by regression based models with square loss. Such a model is agnostic of absolute similarity values and it penalizes the regression error at all ranges of similarity values at the same scale. However, to boost e-commerce platform's monetization, it is important to pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	285,167
2404.03881	A Bi-consolidating Model for Joint Relational Triple Extraction	Current methods to extract relational triples directly make a prediction based on a possible entity pair in a raw sentence without depending on entity recognition. The task suffers from a serious semantic overlapping problem, in which several relation triples may share one or two entities in a sentence. In this paper, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	444,434
2211.15182	Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout	Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area. However, for the nodes in graph, their ST patterns can vary greatly in difficulties for modeling, owning to the heterogeneous nature of ST data. We argue that unveiling the nod...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	333,149
1503.07609	An Evolutionary Algorithm for Error-Driven Learning via Reinforcement	Although different learning systems are coordinated to afford complex behavior, little is known about how this occurs. This article describes a theoretical framework that specifies how complex behaviors that might be thought to require error-driven learning might instead be acquired through simple reinforcement. This f...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	41,492
1806.11538	Factorizable Net: An Efficient Subgraph-based Framework for Scene Graph Generation	Generating scene graph to describe all the relations inside an image gains increasing interests these years. However, most of the previous methods use complicated structures with slow inference speed or rely on the external data, which limits the usage of the model in real-life scenarios. To improve the efficiency of s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,738
2410.17948	Generalized Resubstitution for Regression Error Estimation	We propose generalized resubstitution error estimators for regression, a broad family of estimators, each corresponding to a choice of empirical probability measures and loss function. The usual sum of squares criterion is a special case corresponding to the standard empirical probability measure and the quadratic loss...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	501,679
2312.02214	FlashAvatar: High-fidelity Head Avatar with Efficient Gaussian Embedding	We propose FlashAvatar, a novel and lightweight 3D animatable avatar representation that could reconstruct a digital avatar from a short monocular video sequence in minutes and render high-fidelity photo-realistic images at 300FPS on a consumer-grade GPU. To achieve this, we maintain a uniform 3D Gaussian field embedde...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	412,767
2210.08538	Advantages of OKID-ERA Identification in Control Systems. An Application to the Tennessee Eastman Plant	Data-driven OKID-ERA identification of the open-loop Tennessee Eastman plant is performed to obtain a linear model for control design purposes. Analysis such as numerical conditioning, output response errors, and zero-pole mappings highlight some definite advantages of the OKID-ERA approach when compared with models de...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	324,194
2209.13966	SoftTreeMax: Policy Gradient with Tree Search	Policy-gradient methods are widely used for learning control policies. They can be easily distributed to multiple workers and reach state-of-the-art results in many domains. Unfortunately, they exhibit large variance and subsequently suffer from high-sample complexity since they aggregate gradients over entire trajecto...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	320,091
2408.06672	Leveraging Priors via Diffusion Bridge for Time Series Generation	Time series generation is widely used in real-world applications such as simulation, data augmentation, and hypothesis test techniques. Recently, diffusion models have emerged as the de facto approach for time series generation, emphasizing diverse synthesis scenarios based on historical or correlated time series data ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	480,295
2203.00870	Faith-Shap: The Faithful Shapley Interaction Index	Shapley values, which were originally designed to assign attributions to individual players in coalition games, have become a commonly used approach in explainable machine learning to provide attributions to input features for black-box machine learning models. A key attraction of Shapley values is that they uniquely s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	283,158
2201.09151	An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring	Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers' resumes or social media profiles. We interrogat...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	276,575
2204.00910	Characterizing Spontaneous Ideation Contest on Social Media: Case Study on the Name Change of Facebook to Meta	Collecting good ideas is vital for organizations, especially companies, to retain their competitiveness. Social media is gathering attention as a place to extract ideas efficiently; however, the characteristics of ideas and the posters of ideas on social media are underexamined. Thus, this study aims to characterize sp...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	289,430
2206.02118	ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation	Cardiac segmentation is an essential step for the diagnosis of cardiovascular diseases. However, pixel-wise dense labeling is both costly and time-consuming. Scribble, as a form of sparse annotation, is more accessible than full annotations. However, it's particularly challenging to train a segmentation network with we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	300,757
2003.11089	G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features	In this paper, we propose a novel real-time 6D object pose estimation framework, named G2L-Net. Our network operates on point clouds from RGB-D detection in a divide-and-conquer fashion. Specifically, our network consists of three steps. First, we extract the coarse object point cloud from the RGB-D image by 2D detecti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	169,510
2208.08790	Explainable Reinforcement Learning on Financial Stock Trading using SHAP	Explainable Artificial Intelligence (XAI) research gained prominence in recent years in response to the demand for greater transparency and trust in AI from the user communities. This is especially critical because AI is adopted in sensitive fields such as finance, medicine etc., where implications for society, ethics,...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	313,479
1611.04798	Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder	In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach. We are then able to employ attention-based NMT for many-to-many multilingual translation tasks. Our approach does not require any special treatment on the network architecture and it al...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	63,900
2311.06285	Sounding Bodies: Modeling 3D Spatial Sound of Humans Using Body Pose and Audio	While 3D human body modeling has received much attention in computer vision, modeling the acoustic equivalent, i.e. modeling 3D spatial audio produced by body motion and speech, has fallen short in the community. To close this gap, we present a model that can generate accurate 3D spatial audio for full human bodies. Th...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	406,882
2201.06771	TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters	Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search and information filtering. Recently, several unsupervised methods have been developed to automatically construct the topic taxonomy from a te...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	275,825
2402.09801	EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models	Multimodal large language models (MLLMs) have attracted increasing attention in the past few years, but they may still generate descriptions that include objects not present in the corresponding images, a phenomenon known as object hallucination. To eliminate hallucinations, existing methods manually annotate paired re...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	429,690
2303.00396	Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learning	For deep ordinal classification, learning a well-structured feature space specific to ordinal classification is helpful to properly capture the ordinal nature among classes. Intuitively, when Euclidean distance metric is used, an ideal ordinal layout in feature space would be that the sample clusters are arranged in cl...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	348,581
2006.15555	When and How Can Deep Generative Models be Inverted?	Deep generative models (e.g. GANs and VAEs) have been developed quite extensively in recent years. Lately, there has been an increased interest in the inversion of such a model, i.e. given a (possibly corrupted) signal, we wish to recover the latent vector that generated it. Building upon sparse representation theory, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	184,545
1905.03561	D2-Net: A Trainable CNN for Joint Detection and Description of Local Features	In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions. We propose an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector. By postponing the detection to a later sta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	130,227
1612.04456	Binary Linear Codes From Vectorial Boolean Functions and Their Weight Distribution	Binary linear codes with good parameters have important applications in secret sharing schemes, authentication codes, association schemes, and consumer electronics and communications. In this paper, we construct several classes of binary linear codes from vectorial Boolean functions and determine their parameters, by f...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	65,525
2501.03664	Local Compositional Complexity: How to Detect a Human-readable Messsage	Data complexity is an important concept in the natural sciences and related areas, but lacks a rigorous and computable definition. In this paper, we focus on a particular sense of complexity that is high if the data is structured in a way that could serve to communicate a message. In this sense, human speech, written l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	522,954
2003.05698	Low-Rank and Total Variation Regularization and Its Application to Image Recovery	In this paper, we study the problem of image recovery from given partial (corrupted) observations. Recovering an image using a low-rank model has been an active research area in data analysis and machine learning. But often, images are not only of low-rank but they also exhibit sparsity in a transformed space. In this ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	167,926
2204.10383	Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning	This paper presents an integrated motion planning system for autonomous vehicle (AV) parking in the presence of other moving vehicles. The proposed system includes 1) a hybrid environment predictor that predicts the motions of the surrounding vehicles and 2) a strategic motion planner that reacts to the predictions. Th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	292,762
1811.11885	Nonlinear Decomposition Principle and Fundamental Matrix Solutions for Dynamic Compartmental Systems	A decomposition principle for nonlinear dynamic compartmental systems is introduced in the present paper. This theory is based on the mutually exclusive and exhaustive, analytical and dynamic, novel system and subsystem partitioning methodologies. A deterministic mathematical method is developed for the dynamic analysi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	114,874
2211.04699	FF2: A Feature Fusion Two-Stream Framework for Punctuation Restoration	To accomplish punctuation restoration, most existing methods focus on introducing extra information (e.g., part-of-speech) or addressing the class imbalance problem. Recently, large-scale transformer-based pre-trained language models (PLMS) have been utilized widely and obtained remarkable success. However, the PLMS ar...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	329,327
1402.0911	A Policy Switching Approach to Consolidating Load Shedding and Islanding Protection Schemes	In recent years there have been many improvements in the reliability of critical infrastructure systems. Despite these improvements, the power systems industry has seen relatively small advances in this regard. For instance, power quality deficiencies, a high number of localized contingencies, and large cascading outag...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	30,629
2404.03594	Setpoint control of bilinear systems from noisy data	We consider the problem of designing a controller for an unknown bilinear system using only noisy input-states data points generated by it. The controller should achieve regulation to a given state setpoint and provide a guaranteed basin of attraction. Determining the equilibrium input to achieve that setpoint is not t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	444,319
2110.04109	Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units	In end-to-end automatic speech recognition (ASR), a model is expected to implicitly learn representations suitable for recognizing a word-level sequence. However, the huge abstraction gap between input acoustic signals and output linguistic tokens makes it challenging for a model to learn the representations. In this w...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	259,760
2302.02849	An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal	The k-space data generated from magnetic resonance imaging (MRI) is only a finite sampling of underlying signals. Therefore, MRI images often suffer from low spatial resolution and Gibbs ringing artifacts. Previous studies tackled these two problems separately, where super resolution methods tend to enhance Gibbs artif...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	344,129
1909.05438	Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning	Neural semantic parsing has achieved impressive results in recent years, yet its success relies on the availability of large amounts of supervised data. Our goal is to learn a neural semantic parser when only prior knowledge about a limited number of simple rules is available, without access to either annotated program...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	145,098
2001.01744	Meshlet Priors for 3D Mesh Reconstruction	Estimating a mesh from an unordered set of sparse, noisy 3D points is a challenging problem that requires carefully selected priors. Existing hand-crafted priors, such as smoothness regularizers, impose an undesirable trade-off between attenuating noise and preserving local detail. Recent deep-learning approaches produ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	159,562
2203.00868	An Instance Space Analysis of Constrained Multi-Objective Optimization Problems	Multi-objective optimization problems with constraints (CMOPs) are generally considered more challenging than those without constraints. This in part can be attributed to the creation of infeasible regions generated by the constraint functions, and/or the interaction between constraints and objectives. In this paper, w...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	283,156
2406.00951	How disinformation and fake news impact public policies?: A review of international literature	This study investigates the impact of disinformation on public policies. Using 28 sets of keywords in eight databases, a systematic review was carried out following the Prisma 2020 model (Page et al., 2021). After applying filters and inclusion and exclusion criteria to 4,128 articles and materials found, 46 publicatio...	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	460,095
2309.05614	Detecting communities via edge Random Walk Centrality	Herein we present a novel approach of identifying community structures in complex networks. We propose the usage of the Random Walk Centrality (RWC), first introduced by Noh and Rieger [Phys. Rev. Lett. 92.11 (2004): 118701]. We adapt this node centrality metric to an edge centrality metric by applying it to the line g...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	391,143
1608.03075	3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information	While there has been a success in 2D human pose estimation with convolutional neural networks (CNNs), 3D human pose estimation has not been thoroughly studied. In this paper, we tackle the 3D human pose estimation task with end-to-end learning using CNNs. Relative 3D positions between one joint and the other joints are...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	59,638
2404.03301	Probing Large Language Models for Scalar Adjective Lexical Semantics and Scalar Diversity Pragmatics	Scalar adjectives pertain to various domain scales and vary in intensity within each scale (e.g. certain is more intense than likely on the likelihood scale). Scalar implicatures arise from the consideration of alternative statements which could have been made. They can be triggered by scalar adjectives and require lis...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	444,198
2106.08233	Spot the Difference: Detection of Topological Changes via Geometric Alignment	Geometric alignment appears in a variety of applications, ranging from domain adaptation, optimal transport, and normalizing flows in machine learning; optical flow and learned augmentation in computer vision and deformable registration within biomedical imaging. A recurring challenge is the alignment of domains whose ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	241,226
2406.01194	AFF-ttention! Affordances and Attention models for Short-Term Object Interaction Anticipation	Short-Term object-interaction Anticipation consists of detecting the location of the next-active objects, the noun and verb categories of the interaction, and the time to contact from the observation of egocentric video. This ability is fundamental for wearable assistants or human robot interaction to understand the us...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	460,214
2409.13710	You can remove GPT2's LayerNorm by fine-tuning	The LayerNorm (LN) layer in GPT-style transformer models has long been a hindrance to mechanistic interpretability. LN is a crucial component required to stabilize the training of large language models, and LN or the similar RMSNorm have been used in practically all large language models based on the transformer archit...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	490,120
2307.13929	Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception	Multi-agent collaborative perception as a potential application for vehicle-to-everything communication could significantly improve the perception performance of autonomous vehicles over single-agent perception. However, several challenges remain in achieving pragmatic information sharing in this emerging research. In ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	381,751
cs/9503101	On the Informativeness of the DNA Promoter Sequences Domain Theory	The DNA promoter sequences domain theory and database have become popular for testing systems that integrate empirical and analytical learning. This note reports a simple change and reinterpretation of the domain theory in terms of M-of-N concepts, involving no learning, that results in an accuracy of 93.4% on the 106 ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	540,303
2211.06168	Unimodal and Multimodal Representation Training for Relation Extraction	Multimodal integration of text, layout and visual information has achieved SOTA results in visually rich document understanding (VrDU) tasks, including relation extraction (RE). However, despite its importance, evaluation of the relative predictive capacity of these modalities is less prevalent. Here, we demonstrate th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	329,805
2109.12253	Development of Safety Monitoring System of Connected and Automated Vehicles considering the Trade-off between Communication Efficiency and Data Reliability	The safety of urban transportation systems is considered a public health issue worldwide, and many researchers have contributed to improving it. Connected automated vehicles (CAVs) and cooperative intelligent transportation systems (C-ITSs) are considered solutions to ensure urban transportation systems' safety using v...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	257,210
2401.08013	A Day-to-Day Dynamical Approach to the Most Likely User Equilibrium Problem	The lack of a unique user equilibrium (UE) route flow in traffic assignment has posed a significant challenge to many transportation applications. The maximum-entropy principle, which advocates for the consistent selection of the most likely solution as a representative, is often used to address the challenge. Built on...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	421,728
2212.14276	Learning Implicit Functions for Dense 3D Shape Correspondence of Generic Objects	The objective of this paper is to learn dense 3D shape correspondence for topology-varying generic objects in an unsupervised manner. Conventional implicit functions estimate the occupancy of a 3D point given a shape latent code. Instead, our novel implicit function produces a probabilistic embedding to represent each ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	338,566
2110.09591	Geometry-Based Output Robust Tracking Control of a Quadrotor	The paper solves the problem of tracking control of a quadrotor with unmeasurable pitch and roll angles based on the geometric approach with the use of the enhanced extended observer and the internal model. The proposed approach makes it possible to ensure the movement of a quadrotor in a horizontal plane along a traje...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	261,855
1909.05016	Proposal Towards a Personalized Knowledge-powered Self-play Based Ensemble Dialog System	This is the application document for the 2019 Amazon Alexa competition. We give an overall vision of our conversational experience, as well as a sample conversation that we would like our dialog system to achieve by the end of the competition. We believe personalization, knowledge, and self-play are important component...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	144,969
cs/0312057	Abduction in Well-Founded Semantics and Generalized Stable Models	Abductive logic programming offers a formalism to declaratively express and solve problems in areas such as diagnosis, planning, belief revision and hypothetical reasoning. Tabled logic programming offers a computational mechanism that provides a level of declarativity superior to that of Prolog, and which has supporte...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	538,076
2203.04812	A high-precision self-supervised monocular visual odometry in foggy weather based on robust cycled generative adversarial networks and multi-task learning aided depth estimation	This paper proposes a high-precision self-supervised monocular VO, which is specifically designed for navigation in foggy weather. A cycled generative adversarial network is designed to obtain high-quality self-supervised loss via forcing the forward and backward half-cycle to output consistent estimation. Moreover, gr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	284,612
2411.11635	Chapter 7 Review of Data-Driven Generative AI Models for Knowledge Extraction from Scientific Literature in Healthcare	This review examines the development of abstractive NLP-based text summarization approaches and compares them to existing techniques for extractive summarization. A brief history of text summarization from the 1950s to the introduction of pre-trained language models such as Bidirectional Encoder Representations from Tr...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	509,124
2107.05911	Model Transferability With Responsive Decision Subjects	Given an algorithmic predictor that is accurate on some source population consisting of strategic human decision subjects, will it remain accurate if the population respond to it? In our setting, an agent or a user corresponds to a sample $(X,Y)$ drawn from a distribution $\cal{D}$ and will face a model $h$ and its cla...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	245,928
2411.14699	DNN based Two-stage Compensation Algorithm for THz Hybrid Beamforming with imperfect Hardware	Terahertz (THz) communication is envisioned as a key technology for 6G and beyond wireless systems owing to its multi-GHz bandwidth. To maintain the same aperture area and the same link budget as the lower frequencies, ultra-massive multi-input and multi-output (UM-MIMO) with hybrid beamforming is promising. Neverthele...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	510,279
2206.12983	Explainable and High-Performance Hate and Offensive Speech Detection	The spread of information through social media platforms can create environments possibly hostile to vulnerable communities and silence certain groups in society. To mitigate such instances, several models have been developed to detect hate and offensive speech. Since detecting hate and offensive speech in social media...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	304,801
2303.13325	DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices	Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems. Recent works mostly focus on learning a deep feature encoder by minimizing the discrepancy between source and target features. In this work, we present...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	353,623
2411.16877	PreF3R: Pose-Free Feed-Forward 3D Gaussian Splatting from Variable-length Image Sequence	We present PreF3R, Pose-Free Feed-forward 3D Reconstruction from an image sequence of variable length. Unlike previous approaches, PreF3R removes the need for camera calibration and reconstructs the 3D Gaussian field within a canonical coordinate frame directly from a sequence of unposed images, enabling efficient nove...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	511,219
2104.08867	User Behavior Discovery in the COVID-19 Era through the Sentiment Analysis of User Tweet Texts	The coronavirus disease (COVID-19) outbreak was declared a pandemic in March 2020 and since then it has had a significant effect on all aspects of life. Although we live in an information era, we do not have accurate information about this disease. Online social networks (OSNs) play a vital role in society, especially ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	231,020
2104.10631	MetricOpt: Learning to Optimize Black-Box Evaluation Metrics	We study the problem of directly optimizing arbitrary non-differentiable task evaluation metrics such as misclassification rate and recall. Our method, named MetricOpt, operates in a black-box setting where the computational details of the target metric are unknown. We achieve this by learning a differentiable value fu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	231,644
2007.10730	Video Representation Learning by Recognizing Temporal Transformations	We introduce a novel self-supervised learning approach to learn representations of videos that are responsive to changes in the motion dynamics. Our representations can be learned from data without human annotation and provide a substantial boost to the training of neural networks on small labeled data sets for tasks s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	188,355
2012.03181	Beam Management in 5G: A Stochastic Geometry Analysis	Beam management is central in the operation of beamformed wireless cellular systems such as 5G New Radio (NR) networks. Focusing the energy radiated to mobile terminals (MTs) by increasing the number of beams per cell increases signal power and decreases interference, and has hence the potential to bring major improvem...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	210,022
2310.18884	Simple and Asymmetric Graph Contrastive Learning without Augmentations	Graph Contrastive Learning (GCL) has shown superior performance in representation learning in graph-structured data. Despite their success, most existing GCL methods rely on prefabricated graph augmentation and homophily assumptions. Thus, they fail to generalize well to heterophilic graphs where connected nodes may ha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	403,745
2105.01924	Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder	Detecting unknown and untested scenarios is crucial for scenario-based testing. Scenario-based testing is considered to be a possible approach to validate autonomous vehicles. A traffic scenario consists of multiple components, with infrastructure being one of it. In this work, a method to detect novel traffic scenario...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	233,674
1912.01805	Adversarial Domain Adaptation with Domain Mixup	Recent works on domain adaptation reveal the effectiveness of adversarial learning on filling the discrepancy between source and target domains. However, two common limitations exist in current adversarial-learning-based methods. First, samples from two domains alone are not sufficient to ensure domain-invariance at mo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	156,177
2408.03542	Automatic identification of the area covered by acorn trees in the dehesa (pastureland) Extremadura of Spain	The acorn is the fruit of the oak and is an important crop in the Spanish dehesa extreme\~na, especially for the value it provides in the Iberian pig food to obtain the "acorn" certification. For this reason, we want to maximise the production of Iberian pigs with the appropriate weight. Hence the need to know the area...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	479,059
2502.04411	Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routing	Model merging aggregates Large Language Models (LLMs) finetuned on different tasks into a stronger one. However, parameter conflicts between models leads to performance degradation in averaging. While model routing addresses this issue by selecting individual models during inference, it imposes excessive storage and co...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	531,148
1412.4433	Inexact Alternating Direction Method Based on Newton descent algorithm with Application to Poisson Image Deblurring	The recovery of images from the observations that are degraded by a linear operator and further corrupted by Poisson noise is an important task in modern imaging applications such as astronomical and biomedical ones. Gradient-based regularizers involve the popular total variation semi-norm have become standard techniqu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	38,393
2402.07320	Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets	This research explores the integration of language embeddings for active learning in autonomous driving datasets, with a focus on novelty detection. Novelty arises from unexpected scenarios that autonomous vehicles struggle to navigate, necessitating higher-level reasoning abilities. Our proposed method employs languag...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	428,655

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