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
2302.10465	A Flexible Multi-view Multi-modal Imaging System for Outdoor Scenes	Multi-view imaging systems enable uniform coverage of 3D space and reduce the impact of occlusion, which is beneficial for 3D object detection and tracking accuracy. However, existing imaging systems built with multi-view cameras or depth sensors are limited by the small applicable scene and complicated composition. In...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	346,819
1004.4880	ECME Thresholding Methods for Sparse Signal Reconstruction	We propose a probabilistic framework for interpreting and developing hard thresholding sparse signal reconstruction methods and present several new algorithms based on this framework. The measurements follow an underdetermined linear model, where the regression-coefficient vector is the sum of an unknown deterministic ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,309
2501.08149	Multiple-Input Variational Auto-Encoder for Anomaly Detection in Heterogeneous Data	Anomaly detection (AD) plays a pivotal role in AI applications, e.g., in classification, and intrusion/threat detection in cybersecurity. However, most existing methods face challenges of heterogeneity amongst feature subsets posed by non-independent and identically distributed (non-IID) data. We propose a novel neural...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	524,646
2403.13384	Optimizing Ride-Pooling Revenue: Pricing Strategies and Driver-Traveller Dynamics	Ride-pooling, to gain momentum, needs to be attractive for all the parties involved. This includes also drivers, who are naturally reluctant to serve pooled rides. This can be controlled by the platform's pricing strategy, which can stimulate drivers to serve pooled rides. Here, we propose an agent-based framework, whe...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	439,620
2405.20486	Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning	As a multitude of capable machine learning (ML) models become widely available in forms such as open-source software and public APIs, central questions remain regarding their use in real-world applications, especially in high-stakes decision-making. Is there always one best model that should be used? When are the model...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	459,369
2312.10479	A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis	Few-shot text classification has attracted great interest in both academia and industry due to the lack of labeled data in many fields. Different from general text classification (e.g., topic classification), few-shot sentiment classification is more challenging because the semantic distances among the classes are more...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	416,188
2309.10065	Bayesian longitudinal tensor response regression for modeling neuroplasticity	A major interest in longitudinal neuroimaging studies involves investigating voxel-level neuroplasticity due to treatment and other factors across visits. However, traditional voxel-wise methods are beset with several pitfalls, which can compromise the accuracy of these approaches. We propose a novel Bayesian tensor re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	392,848
1802.10548	Using Deep Learning for Segmentation and Counting within Microscopy Data	Cell counting is a ubiquitous, yet tedious task that would greatly benefit from automation. From basic biological questions to clinical trials, cell counts provide key quantitative feedback that drive research. Unfortunately, cell counting is most commonly a manual task and can be time-intensive. The task is made even ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	91,551
1304.4415	Mining to Compact CNF Propositional Formulae	In this paper, we propose a first application of data mining techniques to propositional satisfiability. Our proposed Mining4SAT approach aims to discover and to exploit hidden structural knowledge for reducing the size of propositional formulae in conjunctive normal form (CNF). Mining4SAT combines both frequent itemse...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	24,002
1204.6079	Learning Semantic String Transformations from Examples	We address the problem of performing semantic transformations on strings, which may represent a variety of data types (or their combination) such as a column in a relational table, time, date, currency, etc. Unlike syntactic transformations, which are based on regular expressions and which interpret a string as a seque...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	15,684
2302.03281	Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning	Modern representation learning methods often struggle to adapt quickly under non-stationarity because they suffer from catastrophic forgetting and decaying plasticity. Such problems prevent learners from fast adaptation since they may forget useful features or have difficulty learning new ones. Hence, these methods are...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	344,285
2404.07396	Can Base ChatGPT be Used for Forecasting without Additional Optimization?	This study investigates whether OpenAI's ChatGPT-3.5 and ChatGPT-4 can forecast future events. To evaluate the accuracy of the predictions, we take advantage of the fact that the training data at the time of our experiments (mid 2023) stopped at September 2021, and ask about events that happened in 2022. We employed tw...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	445,815
2209.15404	Entropy-driven Unsupervised Keypoint Representation Learning in Videos	Extracting informative representations from videos is fundamental for effectively learning various downstream tasks. We present a novel approach for unsupervised learning of meaningful representations from videos, leveraging the concept of image spatial entropy (ISE) that quantifies the per-pixel information in an imag...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	320,595
1210.6511	Neural Networks for Complex Data	Artificial neural networks are simple and efficient machine learning tools. Defined originally in the traditional setting of simple vector data, neural network models have evolved to address more and more difficulties of complex real world problems, ranging from time evolving data to sophisticated data structures such ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	19,374
2502.04485	Active Task Disambiguation with LLMs	Despite the impressive performance of large language models (LLMs) across various benchmarks, their ability to address ambiguously specified problems--frequent in real-world interactions--remains underexplored. To address this gap, we introduce a formal definition of task ambiguity and frame the problem of task disambi...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	531,176
2401.09510	Community Detection in the Multi-View Stochastic Block Model	This paper considers the problem of community detection on multiple potentially correlated graphs from an information-theoretical perspective. We first put forth a random graph model, called the multi-view stochastic block model (MVSBM), designed to generate correlated graphs on the same set of nodes (with cardinality ...	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	false	false	422,296
1307.3102	Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy	We describe a framework for designing efficient active learning algorithms that are tolerant to random classification noise and are differentially-private. The framework is based on active learning algorithms that are statistical in the sense that they rely on estimates of expectations of functions of filtered random e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	25,775
2311.02892	Human as Points: Explicit Point-based 3D Human Reconstruction from Single-view RGB Images	The latest trends in the research field of single-view human reconstruction devote to learning deep implicit functions constrained by explicit body shape priors. Despite the remarkable performance improvements compared with traditional processing pipelines, existing learning approaches still show different aspects of l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	405,632
1912.08909	Subgraph Classification, Clustering and Centrality for a Degree Asymmetric Twitter Based Graph Case Study: Suicidality	We present some initial results from a case study in social media data harvesting and visualization utilizing the tools and analytical features of NodeXL applied to a degree asymmetric vertex graph set. We consider twitter graphs harvested for topics related to suicidal ideation, suicide attempts, self-harm and bullyci...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	157,938
2405.04048	Philosophy of Cognitive Science in the Age of Deep Learning	Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural networks have made significant strides in overcoming the limitations ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	452,423
cs/0007017	Fuzzy data: XML may handle it	Data modeling is one of the most difficult tasks in application engineering. The engineer must be aware of the use cases and the required application services and at a certain point of time he has to fix the data model which forms the base for the application services. However, once the data model has been fixed it is ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	537,158
1505.03001	Detecting the large entries of a sparse covariance matrix in sub-quadratic time	The covariance matrix of a $p$-dimensional random variable is a fundamental quantity in data analysis. Given $n$ i.i.d. observations, it is typically estimated by the sample covariance matrix, at a computational cost of $O(np^{2})$ operations. When $n,p$ are large, this computation may be prohibitively slow. Moreover, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	43,026
2405.15311	Retro: Reusing teacher projection head for efficient embedding distillation on Lightweight Models via Self-supervised Learning	Self-supervised learning (SSL) is gaining attention for its ability to learn effective representations with large amounts of unlabeled data. Lightweight models can be distilled from larger self-supervised pre-trained models using contrastive and consistency constraints. Still, the different sizes of the projection head...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	456,865
2410.13074	Differential Shape Optimization with Image Representation for Photonic Design	We propose a general framework for differentiating shapes represented in binary images with respect to their parameters. This framework functions as an automatic differentiation tool for shape parameters, generating both binary density maps for optical simulations and computing gradients when the simulation provides a ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	499,353
2011.03214	Distributed Grid restoration based on graph theory	With the emergence of smart grids as the primary means of distribution across wide areas, the importance of improving its resilience to faults and mishaps is increasing. The reliability of a distribution system depends upon its tolerance to attacks and the efficiency of restoration after an attack occurs. This paper pr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	205,180
2203.03195	Unpaired Image Captioning by Image-level Weakly-Supervised Visual Concept Recognition	The goal of unpaired image captioning (UIC) is to describe images without using image-caption pairs in the training phase. Although challenging, we except the task can be accomplished by leveraging a training set of images aligned with visual concepts. Most existing studies use off-the-shelf algorithms to obtain the vi...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	284,009
1912.02258	A Survey of Game Theoretic Approaches for Adversarial Machine Learning in Cybersecurity Tasks	Machine learning techniques are currently used extensively for automating various cybersecurity tasks. Most of these techniques utilize supervised learning algorithms that rely on training the algorithm to classify incoming data into different categories, using data encountered in the relevant domain. A critical vulner...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	156,295
2007.13243	Scalable Derivative-Free Optimization for Nonlinear Least-Squares Problems	Derivative-free - or zeroth-order - optimization (DFO) has gained recent attention for its ability to solve problems in a variety of application areas, including machine learning, particularly involving objectives which are stochastic and/or expensive to compute. In this work, we develop a novel model-based DFO method ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	189,065
2304.12877	Proximal Curriculum for Reinforcement Learning Agents	We consider the problem of curriculum design for reinforcement learning (RL) agents in contextual multi-task settings. Existing techniques on automatic curriculum design typically require domain-specific hyperparameter tuning or have limited theoretical underpinnings. To tackle these limitations, we design our curricul...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	360,377
2108.02074	Multi-Round Parsing-based Multiword Rules for Scientific OpenIE	Information extraction (IE) in scientific literature has facilitated many down-stream tasks. OpenIE, which does not require any relation schema but identifies a relational phrase to describe the relationship between a subject and an object, is being a trending topic of IE in sciences. The subjects, objects, and relatio...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	249,212
1803.08035	Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs	We consider the problem of zero-shot recognition: learning a visual classifier for a category with zero training examples, just using the word embedding of the category and its relationship to other categories, which visual data are provided. The key to dealing with the unfamiliar or novel category is to transfer knowl...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	93,184
1501.02732	Predicting Performance During Tutoring with Models of Recent Performance	In educational technology and learning sciences, there are multiple uses for a predictive model of whether a student will perform a task correctly or not. For example, an intelligent tutoring system may use such a model to estimate whether or not a student has mastered a skill. We analyze the significance of data recen...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	39,214
2105.00260	It's not what you said, it's how you said it: discriminative perception of speech as a multichannel communication system	People convey information extremely effectively through spoken interaction using multiple channels of information transmission: the lexical channel of what is said, and the non-lexical channel of how it is said. We propose studying human perception of spoken communication as a means to better understand how information...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	233,150
2304.10686	A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas	This paper proposes a generalised and robust multi-factor Gated Recurrent Unit (GRU) based Deep Learning (DL) model to forecast electricity load in distribution networks during wildfire seasons. The flexible modelling methods consider data input structure, calendar effects and correlation-based leading temperature cond...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	359,510
2404.06492	Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective	Graphs are a natural representation for systems based on relations between connected entities. Combinatorial optimization problems, which arise when considering an objective function related to a process of interest on discrete structures, are often challenging due to the rapid growth of the solution space. The trial-a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	445,489
2111.11760	Automaton of molecular perceptions in biochemical reactions	Local interactions among biomolecules, and the role played by their environment, have gained increasing attention in modelling biochemical reactions. By defining the automaton of molecular perceptions, we explore an agent-based representation of the behaviour of biomolecules in living cells. Our approach considers the ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	267,766
2312.13597	Trochoid Search Optimization	This paper introduces the Trochoid Search Optimization Algorithm (TSO), a novel metaheuristic leveraging the mathematical properties of trochoid curves. The TSO algorithm employs a unique combination of simultaneous translational and rotational motions inherent in trochoids, fostering a refined equilibrium between expl...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	417,360
1601.04059	Parallel and Distributed Methods for Nonconvex Optimization--Part II: Applications	In Part I of this paper, we proposed and analyzed a novel algorithmic framework for the minimization of a nonconvex (smooth) objective function, subject to nonconvex constraints, based on inner convex approximations. This Part II is devoted to the application of the framework to some resource allocation problems in com...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	50,974
1612.08069	Secure Transmissions Using Artificial Noise in MIMO Wiretap Interference Channel: A Game Theoretic Approach	We consider joint optimization of artificial noise (AN) and information signals in a MIMO wiretap interference network, wherein the transmission of each link may be overheard by several MIMO-capable eavesdroppers. Each information signal is accompanied with AN, generated by the same user to confuse nearby eavesdroppers...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	66,024
1607.07495	Understanding Communication Patterns in MOOCs: Combining Data Mining and qualitative methods	Massive Open Online Courses (MOOCs) offer unprecedented opportunities to learn at scale. Within a few years, the phenomenon of crowd-based learning has gained enormous popularity with millions of learners across the globe participating in courses ranging from Popular Music to Astrophysics. They have captured the imagin...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	59,026
2111.11296	Improving Next-Application Prediction with Deep Personalized-Attention Neural Network	Recently, due to the ubiquity and supremacy of E-recruitment platforms, job recommender systems have been largely studied. In this paper, we tackle the next job application problem, which has many practical applications. In particular, we propose to leverage next-item recommendation approaches to consider better the jo...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	267,618
2306.01685	MKOR: Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 Updates	This work proposes a Momentum-Enabled Kronecker-Factor-Based Optimizer Using Rank-1 updates, called MKOR, that improves the training time and convergence properties of deep neural networks (DNNs). Second-order techniques, while enjoying higher convergence rates vs first-order counterparts, have cubic complexity with re...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	370,544
2210.08989	Finding community structure using the ordered random graph model	Visualization of the adjacency matrix enables us to capture macroscopic features of a network when the matrix elements are aligned properly. Community structure, a network consisting of several densely connected components, is a particularly important feature, and the structure can be identified through the adjacency m...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	324,376
2212.04765	Understanding Online Migration Decisions Following the Banning of Radical Communities	The proliferation of radical online communities and their violent offshoots has sparked great societal concern. However, the current practice of banning such communities from mainstream platforms has unintended consequences: (I) the further radicalization of their members in fringe platforms where they migrate; and (ii...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	335,568
1101.1057	Sparsity regret bounds for individual sequences in online linear regression	We consider the problem of online linear regression on arbitrary deterministic sequences when the ambient dimension d can be much larger than the number of time rounds T. We introduce the notion of sparsity regret bound, which is a deterministic online counterpart of recent risk bounds derived in the stochastic setting...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	8,738
2002.12327	A Primer in BERTology: What we know about how BERT works	Transformer-based models have pushed state of the art in many areas of NLP, but our understanding of what is behind their success is still limited. This paper is the first survey of over 150 studies of the popular BERT model. We review the current state of knowledge about how BERT works, what kind of information it lea...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	165,992
2209.03859	A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning	The analysis and control of large-population systems is of great interest to diverse areas of research and engineering, ranging from epidemiology over robotic swarms to economics and finance. An increasingly popular and effective approach to realizing sequential decision-making in multi-agent systems is through multi-a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	316,622
1811.02804	Image Smoothing via Unsupervised Learning	Image smoothing represents a fundamental component of many disparate computer vision and graphics applications. In this paper, we present a unified unsupervised (label-free) learning framework that facilitates generating flexible and high-quality smoothing effects by directly learning from data using deep convolutional...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	112,695
2202.13047	AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation	Crowdsourced dialogue corpora are usually limited in scale and topic coverage due to the expensive cost of data curation. This would hinder the generalization of downstream dialogue models to open-domain topics. In this work, we leverage large language models for dialogue augmentation in the task of emotional support c...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	282,455
2312.05289	Reddiment: Eine SvelteKit- und ElasticSearch-basierte Reddit Sentiment-Analyse	Reddiment is a web-based dashboard that links sentiment analysis of subreddit texts with share prices. The system consists of a backend, frontend and various services. The backend, in Node.js, manages the data and communicates with crawlers that collect Reddit comments and stock market data. Sentiment is analyzed with ...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	true	414,026
2109.04837	Human-Robot Interaction via a Joint-Initiative Supervised Autonomy (JISA) Framework	In this paper, we propose and validate a Joint-Initiative Supervised Autonomy (JISA) framework for Human-Robot Interaction (HRI), in which a robot maintains a measure of its self-confidence (SC) while performing a task, and only prompts the human supervisor for help when its SC drops. At the same time, during task exec...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	254,559
2311.12084	ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches	Adversarial attacks present a significant challenge to the dependable deployment of machine learning models, with patch-based attacks being particularly potent. These attacks introduce adversarial perturbations in localized regions of an image, deceiving even well-trained models. In this paper, we propose Outlier Detec...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	409,203
1808.08070	The Open Energy Modelling Framework (oemof) - A new approach to facilitate open science in energy system modelling	Energy system models have become indispensable tools for planning future energy systems by providing insights into different development trajectories. However, sustainable systems with high shares of renewable energy are characterized by growing cross-sectoral interdependencies and decentralized structures. To capture ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	105,866
1811.06042	Unsupervised domain adaptation for medical imaging segmentation with self-ensembling	Recent advances in deep learning methods have come to define the state-of-the-art for many medical imaging applications, surpassing even human judgment in several tasks. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,435
1508.05752	An evolutionary approach to the identification of Cellular Automata based on partial observations	In this paper we consider the identification problem of Cellular Automata (CAs). The problem is defined and solved in the context of partial observations with time gaps of unknown length, i.e. pre-recorded, partial configurations of the system at certain, unknown time steps. A solution method based on a modified varian...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	46,257
2408.11478	LAKD-Activation Mapping Distillation Based on Local Learning	Knowledge distillation is widely applied in various fundamental vision models to enhance the performance of compact models. Existing knowledge distillation methods focus on designing different distillation targets to acquire knowledge from teacher models. However, these methods often overlook the efficient utilization ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	482,312
2008.11151	FastSal: a Computationally Efficient Network for Visual Saliency Prediction	This paper focuses on the problem of visual saliency prediction, predicting regions of an image that tend to attract human visual attention, under a constrained computational budget. We modify and test various recent efficient convolutional neural network architectures like EfficientNet and MobileNetV2 and compare them...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	193,191
1612.03268	Generalized Deep Image to Image Regression	We present a Deep Convolutional Neural Network architecture which serves as a generic image-to-image regressor that can be trained end-to-end without any further machinery. Our proposed architecture: the Recursively Branched Deconvolutional Network (RBDN) develops a cheap multi-context image representation very early o...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	65,355
2304.05172	LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images	Deep learning based fusion methods have been achieving promising performance in image fusion tasks. This is attributed to the network architecture that plays a very important role in the fusion process. However, in general, it is hard to specify a good fusion architecture, and consequently, the design of fusion network...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	357,523
2005.01591	Characterizing capacity of flexible loads for providing grid support	Flexible loads are a resource for the Balancing Authority (BA) of the future to aid in the balance of supply and demand in the power grid. Consequently, it is of interest for a BA to know how much flexibility a collection of loads has, so to successfully incorporate flexible loads into grid level resource allocation. L...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	175,621
2402.13453	A rational logit dynamic for decision-making under uncertainty: well-posedness, vanishing-noise limit, and numerical approximation	The classical logit dynamic on a continuous action space for decision-making un-der uncertainty is generalized to the dynamic where the exponential function for the softmax part has been replaced by a rational one that includes the former as a special case. We call the new dynamic as the rational logit dynamic. The use...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	431,252
1905.01778	Same Influenza, Different Responses: Social Media Can Sense a Regional Spectrum of Symptoms	Influenza is an acute respiratory infection caused by a virus. It is highly contagious and rapidly mutative. However, its epidemiological characteristics are conventionally collected in terms of outpatient records. In fact, the subjective bias of the doctor emphasizes exterior signs, and the necessity of face-to-face i...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	129,814
2501.06187	Multi-subject Open-set Personalization in Video Generation	Video personalization methods allow us to synthesize videos with specific concepts such as people, pets, and places. However, existing methods often focus on limited domains, require time-consuming optimization per subject, or support only a single subject. We present Video Alchemist $-$ a video model with built-in mul...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	523,867
2206.07018	Turning a Curse into a Blessing: Enabling In-Distribution-Data-Free Backdoor Removal via Stabilized Model Inversion	Many backdoor removal techniques in machine learning models require clean in-distribution data, which may not always be available due to proprietary datasets. Model inversion techniques, often considered privacy threats, can reconstruct realistic training samples, potentially eliminating the need for in-distribution da...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	302,572
2411.01494	Finding NeMo: Negative-mined Mosaic Augmentation for Referring Image Segmentation	Referring Image Segmentation is a comprehensive task to segment an object referred by a textual query from an image. In nature, the level of difficulty in this task is affected by the existence of similar objects and the complexity of the referring expression. Recent RIS models still show a significant performance gap ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	505,100
0905.1235	The Modular Audio Recognition Framework (MARF) and its Applications: Scientific and Software Engineering Notes	MARF is an open-source research platform and a collection of voice/sound/speech/text and natural language processing (NLP) algorithms written in Java and arranged into a modular and extensible framework facilitating addition of new algorithms. MARF can run distributively over the network and may act as a library in app...	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	true	false	true	3,654
2010.14110	Full-Duplex Cell-Free mMIMO Systems: Analysis and Decentralized Optimization	Cell-free (CF) massive multiple-input-multiple-output (mMIMO) deployments are usually investigated with half-duplex nodes and high-capacity fronthaul links. To leverage the possible gains in throughput and energy efficiency (EE) of full-duplex (FD) communications, we consider a FD CF mMIMO system with practical limited...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	203,349
1805.03270	Continuous-time integral dynamics for monotone aggregative games with coupling constraints	We consider continuous-time equilibrium seeking in monotone aggregative games with coupling constraints. We propose semi-decentralized integral dynamics and prove their global convergence to a variational generalized aggregative or Nash equilibrium. The proof is based on Lyapunov arguments and invariance techniques for...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	97,012
2308.00014	A new mapping of technological interdependence	How does technological interdependence affect innovation? We address this question by examining the influence of neighbors' innovativeness and the structure of the innovators' network on a sector's capacity to develop new technologies. We study these two dimensions of technological interdependence by applying novel met...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	382,788
2010.06879	Semantic Segmentation for Partially Occluded Apple Trees Based on Deep Learning	Fruit tree pruning and fruit thinning require a powerful vision system that can provide high resolution segmentation of the fruit trees and their branches. However, recent works only consider the dormant season, where there are minimal occlusions on the branches or fit a polynomial curve to reconstruct branch shape and...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	200,633
1710.02513	A New Data Source for Inverse Dynamics Learning	Modern robotics is gravitating toward increasingly collaborative human robot interaction. Tools such as acceleration policies can naturally support the realization of reactive, adaptive, and compliant robots. These tools require us to model the system dynamics accurately -- a difficult task. The fundamental problem rem...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	82,176
2205.04641	On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models	Recent advancements in unsupervised domain adaptation (UDA) and semi-supervised learning (SSL), particularly incorporating causality, have led to significant methodological improvements in these learning problems. However, a formal theory that explains the role of causality in the generalization performance of UDA/SSL ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	295,700
2210.06789	Large-Scale Open-Set Classification Protocols for ImageNet	Open-Set Classification (OSC) intends to adapt closed-set classification models to real-world scenarios, where the classifier must correctly label samples of known classes while rejecting previously unseen unknown samples. Only recently, research started to investigate on algorithms that are able to handle these unknow...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	323,449
2210.04688	BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets	Reinforcement learning (RL) makes an agent learn from trial-and-error experiences gathered during the interaction with the environment. Recently, offline RL has become a popular RL paradigm because it saves the interactions with environments. In offline RL, data providers share large pre-collected datasets, and others ...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	322,549
2005.13778	Domain Knowledge Integration By Gradient Matching For Sample-Efficient Reinforcement Learning	Model-free deep reinforcement learning (RL) agents can learn an effective policy directly from repeated interactions with a black-box environment. However in practice, the algorithms often require large amounts of training experience to learn and generalize well. In addition, classic model-free learning ignores the dom...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	179,096
2202.11147	On the Rate of Convergence of Payoff-based Algorithms to Nash Equilibrium in Strongly Monotone Games	We derive the rate of convergence to Nash equilibria for the payoff-based algorithm proposed in \cite{tat_kam_TAC}. These rates are achieved under the standard assumption of convexity of the game, strong monotonicity and differentiability of the pseudo-gradient. In particular, we show the algorithm achieves $O(\frac{1}...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	281,781
2403.07413	Learning-Augmented Algorithms with Explicit Predictors	Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. These approaches have demonstrated an enhancement in performance when the predictions are accurate, while also ensuring robustness by providing worst-case guarantees when predictions fai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	436,876
1305.4537	Object Detection with Pixel Intensity Comparisons Organized in Decision Trees	We describe a method for visual object detection based on an ensemble of optimized decision trees organized in a cascade of rejectors. The trees use pixel intensity comparisons in their internal nodes and this makes them able to process image regions very fast. Experimental analysis is provided through a face detection...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	24,698
2204.00903	Safety Verification of Neural Feedback Systems Based on Constrained Zonotopes	Artificial neural networks have recently been utilized in many feedback control systems and introduced new challenges regarding the safety of such systems. This paper considers the safe verification problem for a dynamical system with a given feedforward neural network as the feedback controller by using a constrained ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	289,428
2003.01181	RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning	Almost all neural architecture search methods are evaluated in terms of performance (i.e. test accuracy) of the model structures that it finds. Should it be the only metric for a good autoML approach? To examine aspects beyond performance, we propose a set of criteria aimed at evaluating the core of autoML problem: the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	166,561
2009.02463	Unifying Clustered and Non-stationary Bandits	Non-stationary bandits and online clustering of bandits lift the restrictive assumptions in contextual bandits and provide solutions to many important real-world scenarios. Though the essence in solving these two problems overlaps considerably, they have been studied independently. In this paper, we connect these two s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	194,549
2406.18533	On Scaling Up 3D Gaussian Splatting Training	3D Gaussian Splatting (3DGS) is increasingly popular for 3D reconstruction due to its superior visual quality and rendering speed. However, 3DGS training currently occurs on a single GPU, limiting its ability to handle high-resolution and large-scale 3D reconstruction tasks due to memory constraints. We introduce Grend...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	468,042
1901.06013	FARSA: Fully Automated Roadway Safety Assessment	This paper addresses the task of road safety assessment. An emerging approach for conducting such assessments in the United States is through the US Road Assessment Program (usRAP), which rates roads from highest risk (1 star) to lowest (5 stars). Obtaining these ratings requires manual, fine-grained labeling of roadwa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	118,903
1409.5743	Neural Hypernetwork Approach for Pulmonary Embolism diagnosis	This work introduces an integrative approach based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The objective of the application of neural hyper-network to pulmonary embolism (PE) is to improve diagnose for reducing ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	36,189
2010.03497	Reconfigurable Cyber-Physical System for Lifestyle Video-Monitoring via Deep Learning	Indoor monitoring of people at their homes has become a popular application in Smart Health. With the advances in Machine Learning and hardware for embedded devices, new distributed approaches for Cyber-Physical Systems (CPSs) are enabled. Also, changing environments and need for cost reduction motivate novel reconfigu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	199,419
1905.06744	Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes	Wireless traffic prediction is a fundamental enabler to proactive network optimisation in beyond 5G. Forecasting extreme demand spikes and troughs due to traffic mobility is essential to avoiding outages and improving energy efficiency. Current state-of-the-art deep learning forecasting methods predominantly focus on o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	131,067
2312.08377	ALGNet: Attention Light Graph Memory Network for Medical Recommendation System	Medication recommendation is a vital task for improving patient care and reducing adverse events. However, existing methods often fail to capture the complex and dynamic relationships among patient medical records, drug efficacy and safety, and drug-drug interactions (DDI). In this paper, we propose ALGNet, a novel mod...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	415,282
1412.2601	Generalization of Clustering Agreements and Distances for Overlapping Clusters and Network Communities	A measure of distance between two clusterings has important applications, including clustering validation and ensemble clustering. Generally, such distance measure provides navigation through the space of possible clusterings. Mostly used in cluster validation, a normalized clustering distance, a.k.a. agreement measure...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	38,217
2309.06600	Narrative as a Dynamical System	There is increasing evidence that human activity in general, and narrative in particular, can be treated as a dynamical system in the physics sense; a system whose evolution is described by an action integral, such that the average of all possible paths from point A to point B is given by the extremum of the action. We...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	391,470
2109.00455	On the Tightness of Convex Optimal Power Flow Model Based on Power Loss Relaxation	Optimal power flow (OPF) is the fundamental mathematical model in power system operations. Improving the solution quality of OPF provide huge economic and engineering benefits. The convex reformulation of the original nonconvex alternating current OPF (ACOPF) model gives an efficient way to find the global optimal solu...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	253,107
2101.11003	FDApy: a Python package for functional data	We introduce FDApy, an open-source Python package for the analysis of functional data. The package provides tools for the representation of (multivariate) functional data defined on different dimensional domains and for functional data that is irregularly sampled. Additionally, dimension reduction techniques are implem...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	217,129
2004.05451	Understanding the Socio-Economic Disruption in the United States during COVID-19's Early Days	In this paper, we collect and study Twitter communications to understand the socio-economic impact of COVID-19 in the United States during the early days of the pandemic. Our analysis reveals that COVID-19 gripped the nation during this time as is evidenced by the significant number of trending hashtags. With infection...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	172,193
2311.02961	Adapting Pre-trained Generative Models for Extractive Question Answering	Pre-trained Generative models such as BART, T5, etc. have gained prominence as a preferred method for text generation in various natural language processing tasks, including abstractive long-form question answering (QA) and summarization. However, the potential of generative models in extractive QA tasks, where discrim...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	405,660
2009.12755	A Statistical Learning Assessment of Huber Regression	As one of the triumphs and milestones of robust statistics, Huber regression plays an important role in robust inference and estimation. It has also been finding a great variety of applications in machine learning. In a parametric setup, it has been extensively studied. However, in the statistical learning context wher...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	197,525
1309.4962	HOL(y)Hammer: Online ATP Service for HOL Light	HOL(y)Hammer is an online AI/ATP service for formal (computer-understandable) mathematics encoded in the HOL Light system. The service allows its users to upload and automatically process an arbitrary formal development (project) based on HOL Light, and to attack arbitrary conjectures that use the concepts defined in s...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	27,134
1802.06647	Benchmarking the performance of controllers for power grid transient stability	As the energy transition transforms power grids across the globe, it poses several challenges regarding grid design and control. In particular, high levels of intermittent renewable generation complicate the task of continuously balancing power supply and demand, requiring sufficient control actions. Although there exi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	90,721
2406.04303	Vision-LSTM: xLSTM as Generic Vision Backbone	Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing. Recently, the Long Short-Term Memory (LSTM) has been extended to a scalable and performant architecture - the xLSTM - which overcomes long-standing LSTM limitations via exponential gating ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	461,614
2311.05754	Deep Natural Language Feature Learning for Interpretable Prediction	We propose a general method to break down a main complex task into a set of intermediary easier sub-tasks, which are formulated in natural language as binary questions related to the final target task. Our method allows for representing each example by a vector consisting of the answers to these questions. We call this...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	406,688
1905.12280	Lifelong Bayesian Optimization	Automatic Machine Learning (Auto-ML) systems tackle the problem of automating the design of prediction models or pipelines for data science. In this paper, we present Lifelong Bayesian Optimization (LBO), an online, multitask Bayesian optimization (BO) algorithm designed to solve the problem of model selection for data...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,721
2109.00927	Autonomous Curiosity for Real-Time Training Onboard Robotic Agents	Learning requires both study and curiosity. A good learner is not only good at extracting information from the data given to it, but also skilled at finding the right new information to learn from. This is especially true when a human operator is required to provide the ground truth - such a source should only be queri...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	253,286

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