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
1309.3877	A Metric-learning based framework for Support Vector Machines and Multiple Kernel Learning	Most metric learning algorithms, as well as Fisher's Discriminant Analysis (FDA), optimize some cost function of different measures of within-and between-class distances. On the other hand, Support Vector Machines(SVMs) and several Multiple Kernel Learning (MKL) algorithms are based on the SVM large margin theory. Rece...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	27,054
2305.11130	SimOAP: Improve Coherence and Consistency in Persona-based Dialogue Generation via Over-sampling and Post-evaluation	Language models trained on large-scale corpora can generate remarkably fluent results in open-domain dialogue. However, for the persona-based dialogue generation task, consistency and coherence are also key factors, which are great challenges for language models. Existing works mainly focus on valuable data filtering, ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	365,397
1407.0118	A Characterization of the Minimal Average Data Rate that Guarantees a Given Closed-Loop Performance Level	This paper studies networked control systems closed over noiseless digital channels. By focusing on noisy LTI plants with scalar-valued control inputs and sensor outputs, we derive an absolute lower bound on the minimal average data rate that allows one to achieve a prescribed level of stationary performance under Gaus...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	34,300
2302.09808	RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operator	Perception of the full state is an essential technology to support the monitoring, analysis, and design of physical systems, one of whose challenges is to recover global field from sparse observations. Well-known for brilliant approximation ability, deep neural networks have been attractive to data-driven flow and heat...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	346,575
2206.12662	Synthesizing Personalized Non-speech Vocalization from Discrete Speech Representations	We formulated non-speech vocalization (NSV) modeling as a text-to-speech task and verified its viability. Specifically, we evaluated the phonetic expressivity of HUBERT speech units on NSVs and verified our model's ability to control over speaker timbre even though the training data is speaker few-shot. In addition, we...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	304,677
2112.09859	Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP	We introduce two new no-regret algorithms for the stochastic shortest path (SSP) problem with a linear MDP that significantly improve over the only existing results of (Vial et al., 2021). Our first algorithm is computationally efficient and achieves a regret bound $\widetilde{O}\left(\sqrt{d^3B_{\star}^2T_{\star} K}\r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	272,279
2403.03037	A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives	Human comprehension of a video stream is naturally broad: in a few instants, we are able to understand what is happening, the relevance and relationship of objects, and forecast what will follow in the near future, everything all at once. We believe that - to effectively transfer such an holistic perception to intellig...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	435,045
2206.01475	Functional Connectivity Methods for EEG-based Biometrics on a Large, Heterogeneous Dataset	This study examines the utility of functional connectivity (FC) and graph-based (GB) measures with a support vector machine classifier for use in electroencephalogram (EEG) based biometrics. Although FC-based features have been used in biometric applications, studies assessing the identification algorithms on heterogen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	300,491
2201.08429	A Visual Analytics Approach to Building Logistic Regression Models and its Application to Health Records	Multidimensional data analysis has become increasingly important in many fields, mainly due to current vast data availability and the increasing demand to extract knowledge from it. In most applications, the role of the final user is crucial to build proper machine learning models and to explain the patterns found in d...	true	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	276,330
1801.03546	Segment-based Methods for Facial Attribute Detection from Partial Faces	State-of-the-art methods of attribute detection from faces almost always assume the presence of a full, unoccluded face. Hence, their performance degrades for partially visible and occluded faces. In this paper, we introduce SPLITFACE, a deep convolutional neural network-based method that is explicitly designed to perf...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	88,108
1906.02885	Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions	Semantic segmentation and instance level segmentation made substantial progress in recent years due to the emergence of deep neural networks (DNNs). A number of deep architectures with Convolution Neural Networks (CNNs) were proposed that surpass the traditional machine learning approaches for segmentation by a large m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	134,209
1809.05380	KittingBot: A Mobile Manipulation Robot for Collaborative Kitting in Automotive Logistics	Individualized manufacturing of cars requires kitting: the collection of individual sets of part variants for each car. This challenging logistic task is frequently performed manually by warehouseman. We propose a mobile manipulation robotic system for autonomous kitting, building on the Kuka Miiwa platform which consi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	107,784
1611.01734	Deep Biaffine Attention for Neural Dependency Parsing	This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	63,432
2210.05828	AMICO: Amodal Instance Composition	Image composition aims to blend multiple objects to form a harmonized image. Existing approaches often assume precisely segmented and intact objects. Such assumptions, however, are hard to satisfy in unconstrained scenarios. We present Amodal Instance Composition for compositing imperfect -- potentially incomplete and/...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	323,014
2408.16765	A Score-Based Density Formula, with Applications in Diffusion Generative Models	Score-based generative models (SGMs) have revolutionized the field of generative modeling, achieving unprecedented success in generating realistic and diverse content. Despite empirical advances, the theoretical basis for why optimizing the evidence lower bound (ELBO) on the log-likelihood is effective for training dif...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	484,442
2106.04382	Proof methods for robust low-rank matrix recovery	Low-rank matrix recovery problems arise naturally as mathematical formulations of various inverse problems, such as matrix completion, blind deconvolution, and phase retrieval. Over the last two decades, a number of works have rigorously analyzed the reconstruction performance for such scenarios, giving rise to a rathe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	239,697
1510.08368	Switching control for incremental stabilization of nonlinear systems via contraction theory	In this paper we present a switching control strategy to incrementally stabilize a class of nonlinear dynamical systems. Exploiting recent results on contraction analysis of switched Filippov systems derived using regularization, sufficient conditions are presented to prove incremental stability of the closed-loop syst...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	48,278
2210.07083	Soundness and Completeness of SPARQL Query Containment Solver SpeCS	Tool SPECS implements an efficient automated approach for reasoning about the SPARQL query containment problem. In this paper, we prove the correctness of this approach. We give precise semantics of the core subset of SPARQL language. We briefly discuss the procedure used for reducing the query containment problem into...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	323,567
2011.11499	Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model	Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements over various cross-lingual and low-resource tasks. Through training on one hundred languages and terabytes of texts, cross-lingual language models have proven to be effecti...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	207,843
2411.11844	Generative World Explorer	Planning with partial observation is a central challenge in embodied AI. A majority of prior works have tackled this challenge by developing agents that physically explore their environment to update their beliefs about the world state. In contrast, humans can $\textit{imagine}$ unseen parts of the world through a ment...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	509,194
0712.2640	Optimal Memoryless Encoding for Low Power Off-Chip Data Buses	Off-chip buses account for a significant portion of the total system power consumed in embedded systems. Bus encoding schemes have been proposed to minimize power dissipation, but none has been demonstrated to be optimal with respect to any measure. In this paper, we give the first provably optimal and explicit (polyno...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	1,045
1612.06856	Temporal Feature Selection on Networked Time Series	This paper formulates the problem of learning discriminative features (\textit{i.e.,} segments) from networked time series data considering the linked information among time series. For example, social network users are considered to be social sensors that continuously generate social signals (tweets) represented as a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	65,872
1809.04783	Generative adversarial network-based image super-resolution using perceptual content losses	In this paper, we propose a deep generative adversarial network for super-resolution considering the trade-off between perception and distortion. Based on good performance of a recently developed model for super-resolution, i.e., deep residual network using enhanced upscale modules (EUSR), the proposed model is trained...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	107,655
2205.05928	Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches	Micro-Electro-Mechanical-Systems are complex structures, often involving nonlinearites of geometric and multiphysics nature, that are used as sensors and actuators in countless applications. Starting from full-order representations, we apply deep learning techniques to generate accurate, efficient and real-time reduced...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	296,091
1701.00609	Akid: A Library for Neural Network Research and Production from a Dataism Approach	Neural networks are a revolutionary but immature technique that is fast evolving and heavily relies on data. To benefit from the newest development and newly available data, we want the gap between research and production as small as possibly. On the other hand, differing from traditional machine learning models, neura...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	66,294
2107.03220	Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis	Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis. Recently, Graph Neural Networks (GNNs) have become a de facto model for analyzing graph-structured data. However, how to employ GNN...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	245,094
0909.1151	n-Opposition theory to structure debates	2007 was the first international congress on the ?square of oppositions?. A first attempt to structure debate using n-opposition theory was presented along with the results of a first experiment on the web. Our proposal for this paper is to define relations between arguments through a structure of opposition (square of...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	4,413
2408.13208	Temporal Fairness in Decision Making Problems	In this work we consider a new interpretation of fairness in decision making problems. Building upon existing fairness formulations, we focus on how to reason over fairness from a temporal perspective, taking into account the fairness of a history of past decisions. After introducing the concept of temporal fairness, w...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	483,050
1403.0801	Is getting the right answer just about choosing the right words? The role of syntactically-informed features in short answer scoring	Developments in the educational landscape have spurred greater interest in the problem of automatically scoring short answer questions. A recent shared task on this topic revealed a fundamental divide in the modeling approaches that have been applied to this problem, with the best-performing systems split between those...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	31,330
2403.10049	PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction	Click-through rate (CTR) prediction is a core task in recommender systems. Existing methods (IDRec for short) rely on unique identities to represent distinct users and items that have prevailed for decades. On one hand, IDRec often faces significant performance degradation on cold-start problem; on the other hand, IDRe...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	438,019
1703.05921	Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery	Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High annotation effort and the limitation to a vocabulary of known markers ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	70,149
2210.00353	Sustained oscillations in multi-topic belief dynamics over signed networks	We study the dynamics of belief formation on multiple interconnected topics in networks of agents with a shared belief system. We establish sufficient conditions and necessary conditions under which sustained oscillations of beliefs arise on the network in a Hopf bifurcation and characterize the role of the communicati...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	320,833
1705.06056	Target Type Identification for Entity-Bearing Queries	Identifying the target types of entity-bearing queries can help improve retrieval performance as well as the overall search experience. In this work, we address the problem of automatically detecting the target types of a query with respect to a type taxonomy. We propose a supervised learning approach with a rich varie...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	73,590
2405.13302	Accelerated Evaluation of Ollivier-Ricci Curvature Lower Bounds: Bridging Theory and Computation	Curvature serves as a potent and descriptive invariant, with its efficacy validated both theoretically and practically within graph theory. We employ a definition of generalized Ricci curvature proposed by Ollivier, which Lin and Yau later adapted to graph theory, known as Ollivier-Ricci curvature (ORC). ORC measures c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	455,885
2207.10455	Magic ELF: Image Deraining Meets Association Learning and Transformer	Convolutional neural network (CNN) and Transformer have achieved great success in multimedia applications. However, little effort has been made to effectively and efficiently harmonize these two architectures to satisfy image deraining. This paper aims to unify these two architectures to take advantage of their learnin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,274
1705.10574	Multi-Focus Image Fusion Using Sparse Representation and Coupled Dictionary Learning	We address the multi-focus image fusion problem, where multiple images captured with different focal settings are to be fused into an all-in-focus image of higher quality. Algorithms for this problem necessarily admit the source image characteristics along with focused and blurred features. However, most sparsity-based...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	74,427
2306.07601	Intrusion Detection: A Deep Learning Approach	Network intrusions are a significant problem in all industries today. A critical part of the solution is being able to effectively detect intrusions. With recent advances in artificial intelligence, current research has begun adopting deep learning approaches for intrusion detection. Current approaches for multi-class ...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	373,077
2211.11242	L-MAE: Masked Autoencoders are Semantic Segmentation Datasets Augmenter	Generating semantic segmentation datasets has consistently been laborious and time-consuming, particularly in the context of large models or specialized domains(i.e. Medical Imaging or Remote Sensing). Specifically, large models necessitate a substantial volume of data, while datasets in professional domains frequently...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	331,647
1803.02140	Conceptualization of Object Compositions Using Persistent Homology	A topological shape analysis is proposed and utilized to learn concepts that reflect shape commonalities. Our approach is two-fold: i) a spatial topology analysis of point cloud segment constellations within objects. Therein constellations are decomposed and described in an hierarchical manner - from single segments to...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	92,004
1705.03412	Nonconvex Generalization of Alternating Direction Method of Multipliers for Nonlinear Equality Constrained Problems	The classic Alternating Direction Method of Multipliers (ADMM) is a popular framework to solve linear-equality constrained problems. In this paper, we extend the ADMM naturally to nonlinear equality-constrained problems, called neADMM. The difficulty of neADMM is to solve nonconvex subproblems. We provide globally opti...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	73,178
2301.03914	Learning with minimal effort: leveraging in silico labeling for cell and nucleus segmentation	Deep learning provides us with powerful methods to perform nucleus or cell segmentation with unprecedented quality. However, these methods usually require large training sets of manually annotated images, which are tedious and expensive to generate. In this paper we propose to use In Silico Labeling (ISL) as a pretrain...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	339,917
2312.06029	Fast Classification of Large Time Series Datasets	Time series classification (TSC) is the most import task in time series mining as it has several applications in medicine, meteorology, finance cyber security, and many others. With the ever increasing size of time series datasets, several traditional TSC methods are no longer efficient enough to perform this task on s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	414,338
2310.14200	Dynamic Resource Management in CDRT Systems through Adaptive NOMA	This paper introduces a novel adaptive transmission scheme to amplify the prowess of coordinated direct and relay transmission (CDRT) systems rooted in non-orthogonal multiple access principles. Leveraging the maximum ratio transmission scheme, we seamlessly meet the prerequisites of CDRT while harnessing the potential...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	401,756
2310.09554	Neural network scoring for efficient computing	Much work has been dedicated to estimating and optimizing workloads in high-performance computing (HPC) and deep learning. However, researchers have typically relied on few metrics to assess the efficiency of those techniques. Most notably, the accuracy, the loss of the prediction, and the computational time with regar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	399,826
2401.01733	Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks	Leakages are a major risk in water distribution networks as they cause water loss and increase contamination risks. Leakage detection is a difficult task due to the complex dynamics of water distribution networks. In particular, small leakages are hard to detect. From a machine-learning perspective, leakages can be mod...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	419,476
2305.05374	HybridNet: Dual-Branch Fusion of Geometrical and Topological Views for VLSI Congestion Prediction	Accurate early congestion prediction can prevent unpleasant surprises at the routing stage, playing a crucial character in assisting designers to iterate faster in VLSI design cycles. In this paper, we introduce a novel strategy to fully incorporate topological and geometrical features of circuits by making several key...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	363,123
1901.01982	Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network	It remains challenging to automatically segment kidneys in clinical ultrasound images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance. In this study, we developed a novel boundary distance regression deep neural network to segment ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	118,093
2112.05534	An Embarrassingly Pragmatic Introduction to Vision-based Autonomous Robots	Autonomous robots are currently one of the most popular Artificial Intelligence problems, having experienced significant advances in the last decade, from Self-driving cars and humanoids to delivery robots and drones. Part of the problem is to get a robot to emulate the perception of human beings, our sense of sight, r...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	270,870
2002.04999	Differentiable Graph Module (DGM) for Graph Convolutional Networks	Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-Euclidean structured data. Such methods have shown promising results on a broad spectrum of applications ranging from social science, biomedicine, and particle physics to computer vision,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,759
1702.03695	On the Energy/Distortion Tradeoff in the IoT	The Internet of Things paradigm envisages the presence of many battery-powered sensors and this entails the design of energy-aware protocols. Source coding techniques allow to save some energy by compressing the packets sent over the network, but at the cost of a poorer accuracy in the representation of the data. This ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,173
2005.00992	Physical reservoir computing -- An introductory perspective	Understanding the fundamental relationships between physics and its information-processing capability has been an active research topic for many years. Physical reservoir computing is a recently introduced framework that allows one to exploit the complex dynamics of physical systems as information-processing devices. T...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	175,463
1307.5710	Saliency-Guided Perceptual Grouping Using Motion Cues in Region-Based Artificial Visual Attention	Region-based artificial attention constitutes a framework for bio-inspired attentional processes on an intermediate abstraction level for the use in computer vision and mobile robotics. Segmentation algorithms produce regions of coherently colored pixels. These serve as proto-objects on which the attentional processes ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	25,974
2102.11250	On Stability and Convergence of Distributed Filters	Recent years have bore witness to the proliferation of distributed filtering techniques, where a collection of agents communicating over an ad-hoc network aim to collaboratively estimate and track the state of a system. These techniques form the enabling technology of modern multi-agent systems and have gained great im...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	221,362
2105.05648	Look-Ahead Screening Rules for the Lasso	The lasso is a popular method to induce shrinkage and sparsity in the solution vector (coefficients) of regression problems, particularly when there are many predictors relative to the number of observations. Solving the lasso in this high-dimensional setting can, however, be computationally demanding. Fortunately, thi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	234,885
2403.08109	VANP: Learning Where to See for Navigation with Self-Supervised Vision-Action Pre-Training	Humans excel at efficiently navigating through crowds without collision by focusing on specific visual regions relevant to navigation. However, most robotic visual navigation methods rely on deep learning models pre-trained on vision tasks, which prioritize salient objects -- not necessarily relevant to navigation and ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	437,174
1710.10433	An Ontology to support automated negotiation	In this work we propose an ontology to support automated negotiation in multiagent systems. The ontology can be connected with some domain-specific ontologies to facilitate the negotiation in different domains, such as Intelligent Transportation Systems (ITS), e-commerce, etc. The specific negotiation rules for each ty...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	83,382
1511.08712	A stochastic evolutionary model generating a mixture of exponential distributions	Recent interest in human dynamics has stimulated the investigation of the stochastic processes that explain human behaviour in various contexts, such as mobile phone networks and social media. In this paper, we extend the stochastic urn-based model proposed in \cite{FENN15} so that it can generate mixture models,in par...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	49,570
2008.05558	On the complexity of finding a local minimizer of a quadratic function over a polytope	We show that unless P=NP, there cannot be a polynomial-time algorithm that finds a point within Euclidean distance $c^n$ (for any constant $c \ge 0$) of a local minimizer of an $n$-variate quadratic function over a polytope. This result (even with $c=0$) answers a question of Pardalos and Vavasis that appeared in 1992 ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	191,536
2310.16676	SSLCL: An Efficient Model-Agnostic Supervised Contrastive Learning Framework for Emotion Recognition in Conversations	Emotion recognition in conversations (ERC) is a rapidly evolving task within the natural language processing community, which aims to detect the emotions expressed by speakers during a conversation. Recently, a growing number of ERC methods have focused on leveraging supervised contrastive learning (SCL) to enhance the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	402,827
2409.16787	Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution	Research in Explainable Artificial Intelligence (XAI) is increasing, aiming to make deep learning models more transparent. Most XAI methods focus on justifying the decisions made by Artificial Intelligence (AI) systems in security-relevant applications. However, relatively little attention has been given to using these...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	491,508
2110.07130	Region Semantically Aligned Network for Zero-Shot Learning	Zero-shot learning (ZSL) aims to recognize unseen classes based on the knowledge of seen classes. Previous methods focused on learning direct embeddings from global features to the semantic space in hope of knowledge transfer from seen classes to unseen classes. However, an unseen class shares local visual features wit...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	260,871
2108.13217	Equivariant relative submajorization	We study a generalization of relative submajorization that compares pairs of positive operators on representation spaces of some fixed group. A pair equivariantly relatively submajorizes another if there is an equivariant subnormalized channel that takes the components of the first pair to a pair satisfying similar pos...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	252,745
2308.16491	In-class Data Analysis Replications: Teaching Students while Testing Science	Science is facing a reproducibility crisis. Previous work has proposed incorporating data analysis replications into classrooms as a potential solution. However, despite the potential benefits, it is unclear whether this approach is feasible, and if so, what the involved stakeholders-students, educators, and scientists...	false	false	false	true	true	false	true	false	false	false	false	false	false	true	false	false	false	false	389,008
2103.06709	Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices	Hyperdimensional computing (HDC) has emerged as a new light-weight learning algorithm with smaller computation and energy requirements compared to conventional techniques. In HDC, data points are represented by high-dimensional vectors (hypervectors), which are mapped to high-dimensional space (hyperspace). Typically, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	224,384
2406.05569	Do LLMs Recognize me, When I is not me: Assessment of LLMs Understanding of Turkish Indexical Pronouns in Indexical Shift Contexts	Large language models (LLMs) have shown impressive capabilities in tasks such as machine translation, text summarization, question answering, and solving complex mathematical problems. However, their primary training on data-rich languages like English limits their performance in low-resource languages. This study addr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	462,189
2304.04616	Automated Reading Passage Generation with OpenAI's Large Language Model	The widespread usage of computer-based assessments and individualized learning platforms has resulted in an increased demand for the rapid production of high-quality items. Automated item generation (AIG), the process of using item models to generate new items with the help of computer technology, was proposed to reduc...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	357,288
2407.01705	Optimized Learning for X-Ray Image Classification for Multi-Class Disease Diagnoses with Accelerated Computing Strategies	X-ray image-based disease diagnosis lies in ensuring the precision of identifying afflictions within the sample, a task fraught with challenges stemming from the occurrence of false positives and false negatives. False positives introduce the risk of erroneously identifying non-existent conditions, leading to misdiagno...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	469,419
1611.01030	Sparse Support Recovery with Non-smooth Loss Functions	In this paper, we study the support recovery guarantees of underdetermined sparse regression using the $\ell_1$-norm as a regularizer and a non-smooth loss function for data fidelity. More precisely, we focus in detail on the cases of $\ell_1$ and $\ell_\infty$ losses, and contrast them with the usual $\ell_2$ loss. Wh...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	63,309
2009.10325	Learning Image Labels On-the-fly for Training Robust Classification Models	Current deep learning paradigms largely benefit from the tremendous amount of annotated data. However, the quality of the annotations often varies among labelers. Multi-observer studies have been conducted to study these annotation variances (by labeling the same data for multiple times) and its effects on critical app...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	196,866
2410.10343	Locking Down the Finetuned LLMs Safety	Fine-tuning large language models (LLMs) on additional datasets is often necessary to optimize them for specific downstream tasks. However, existing safety alignment measures, which restrict harmful behavior during inference, are insufficient to mitigate safety risks during fine-tuning. Alarmingly, fine-tuning with jus...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	498,026
1910.02655	BERT for Evidence Retrieval and Claim Verification	Motivated by the promising performance of pre-trained language models, we investigate BERT in an evidence retrieval and claim verification pipeline for the FEVER fact extraction and verification challenge. To this end, we propose to use two BERT models, one for retrieving potential evidence sentences supporting or reje...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	148,307
1303.5729	Reasoning under Uncertainty: Some Monte Carlo Results	A series of monte carlo studies were performed to compare the behavior of some alternative procedures for reasoning under uncertainty. The behavior of several Bayesian, linear model and default reasoning procedures were examined in the context of increasing levels of calibration error. The most interesting result is th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,177
2411.17773	Efficient Multi-modal Large Language Models via Visual Token Grouping	The development of Multi-modal Large Language Models (MLLMs) enhances Large Language Models (LLMs) with the ability to perceive data formats beyond text, significantly advancing a range of downstream applications, such as visual question answering and image captioning. However, the substantial computational costs assoc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	511,597
2303.11019	A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images	Supervised deep learning methods have achieved considerable success in medical image analysis, owing to the availability of large-scale and well-annotated datasets. However, creating such datasets for whole slide images (WSIs) in histopathology is a challenging task due to their gigapixel size. In recent years, self-su...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	352,673
2408.05649	Advancing Pavement Distress Detection in Developing Countries: A Novel Deep Learning Approach with Locally-Collected Datasets	Road infrastructure maintenance in developing countries faces unique challenges due to resource constraints and diverse environmental factors. This study addresses the critical need for efficient, accurate, and locally-relevant pavement distress detection methods in these regions. We present a novel deep learning appro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	479,877
2305.16891	Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks	Recently, significant progress has been made in understanding the generalization of neural networks (NNs) trained by gradient descent (GD) using the algorithmic stability approach. However, most of the existing research has focused on one-hidden-layer NNs and has not addressed the impact of different network scaling pa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	368,307
1811.03076	Class-conditional embeddings for music source separation	Isolating individual instruments in a musical mixture has a myriad of potential applications, and seems imminently achievable given the levels of performance reached by recent deep learning methods. While most musical source separation techniques learn an independent model for each instrument, we propose using a common...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	112,751
2003.02609	UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning	Coverage path planning (CPP) is the task of designing a trajectory that enables a mobile agent to travel over every point of an area of interest. We propose a new method to control an unmanned aerial vehicle (UAV) carrying a camera on a CPP mission with random start positions and multiple options for landing positions ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	166,980
2303.09002	Imitation and Transfer Learning for LQG Control	In this paper we study an imitation and transfer learning setting for Linear Quadratic Gaussian (LQG) control, where (i) the system dynamics, noise statistics and cost function are unknown and expert data is provided (that is, sequences of optimal inputs and outputs) to learn the LQG controller, and (ii) multiple contr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	351,858
2411.07150	Variational Graph Contrastive Learning	Graph representation learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. Self-supervised learning (SSL) methods are widely used in GRL because they can avoid expensive human annotation. In this work, we propose a novel Subgraph ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	507,413
2312.06924	Safety Alignment in NLP Tasks: Weakly Aligned Summarization as an In-Context Attack	Recent developments in balancing the usefulness and safety of Large Language Models (LLMs) have raised a critical question: Are mainstream NLP tasks adequately aligned with safety consideration? Our study, focusing on safety-sensitive documents obtained through adversarial attacks, reveals significant disparities in th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	414,722
1408.4187	Closed-Form Delay-Optimal Power Control for Energy Harvesting Wireless System with Finite Energy Storage	In this paper, we consider delay-optimal power control for an energy harvesting wireless system with finite energy storage. The wireless system is powered solely by a renewable energy source with bursty data arrivals, and is characterized by a data queue and an energy queue. We consider a delay-optimal power control pr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	35,443
2403.18975	A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models	Medical imaging is critical to the diagnosis, surveillance, and treatment of many health conditions, including oncological, neurological, cardiovascular, and musculoskeletal disorders, among others. Radiologists interpret these complex, unstructured images and articulate their assessments through narrative reports that...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	442,150
2105.08016	StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects	We present StrobeNet, a method for category-level 3D reconstruction of articulating objects from one or more unposed RGB images. Reconstructing general articulating object categories % has important applications, but is challenging since objects can have wide variation in shape, articulation, appearance and topology. W...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	235,626
2303.11330	Legs as Manipulator: Pushing Quadrupedal Agility Beyond Locomotion	Locomotion has seen dramatic progress for walking or running across challenging terrains. However, robotic quadrupeds are still far behind their biological counterparts, such as dogs, which display a variety of agile skills and can use the legs beyond locomotion to perform several basic manipulation tasks like interact...	false	false	false	false	true	false	true	true	false	false	true	true	false	false	false	false	false	false	352,808
2308.04794	An Autonomous Hybrid Drone-Rover Vehicle for Weed Removal and Spraying Applications in Agriculture	The usage of drones and rovers helps to overcome the limitations of traditional agriculture which has been predominantly human-intensive, for carrying out tasks such as removal of weeds and spraying of fertilizers and pesticides. Drones and rovers are helping to realize precision agriculture and farmers with improved m...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	384,561
1802.06903	Generalization Error Bounds with Probabilistic Guarantee for SGD in Nonconvex Optimization	The success of deep learning has led to a rising interest in the generalization property of the stochastic gradient descent (SGD) method, and stability is one popular approach to study it. Existing works based on stability have studied nonconvex loss functions, but only considered the generalization error of the SGD in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	90,778
1409.5209	Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning	Many typical applications of object detection operate within a prescribed false-positive range. In this situation the performance of a detector should be assessed on the basis of the area under the ROC curve over that range, rather than over the full curve, as the performance outside the range is irrelevant. This measu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	36,145
1003.0470	Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels	Many popular linear classifiers, such as logistic regression, boosting, or SVM, are trained by optimizing a margin-based risk function. Traditionally, these risk functions are computed based on a labeled dataset. We develop a novel technique for estimating such risks using only unlabeled data and the marginal label dis...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	5,820
2302.00100	Physics-informed Reduced-Order Learning from the First Principles for Simulation of Quantum Nanostructures	Multi-dimensional direct numerical simulation (DNS) of the Schr\"odinger equation is needed for design and analysis of quantum nanostructures that offer numerous applications in biology, medicine, materials, electronic/photonic devices, etc. In large-scale nanostructures, extensive computational effort needed in DNS ma...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	343,094
1704.05754	A location-aware embedding technique for accurate landmark recognition	The current state of the research in landmark recognition highlights the good accuracy which can be achieved by embedding techniques, such as Fisher vector and VLAD. All these techniques do not exploit spatial information, i.e. consider all the features and the corresponding descriptors without embedding their location...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	72,069
2010.06285	Land Cover Semantic Segmentation Using ResUNet	In this paper we present our work on developing an automated system for land cover classification. This system takes a multiband satellite image of an area as input and outputs the land cover map of the area at the same resolution as the input. For this purpose convolutional machine learning models were trained in the ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	200,437
2406.10815	On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning	Supervised contrastive representation learning has been shown to be effective in various transfer learning scenarios. However, while asymmetric non-contrastive learning (ANCL) often outperforms its contrastive learning counterpart in self-supervised representation learning, the extension of ANCL to supervised scenarios...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	464,568
2009.14332	Multi-hop Attention Graph Neural Network	Self-attention mechanism in graph neural networks (GNNs) led to state-of-the-art performance on many graph representation learning tasks. Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes. However, such attention mechanism does no...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	197,993
2405.08097	A Galois theorem for machine learning: Functions on symmetric matrices and point clouds via lightweight invariant features	In this work, we present a mathematical formulation for machine learning of (1) functions on symmetric matrices that are invariant with respect to the action of permutations by conjugation, and (2) functions on point clouds that are invariant with respect to rotations, reflections, and permutations of the points. To ac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	453,983
2210.03073	Moving Virtual Agents Forward in Space and Time	This article proposes an adaptation from the model of Bianco for fast-forwarding agents in crowd simulation, which enables us to accurately fast forward agents in time. Besides being able to jump from one position to another, agents are able to stay inside their track, it means, the new position is calculated taking in...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	321,888
2412.12400	Using machine learning to inform harvest control rule design in complex fishery settings	In fishery science, harvest management of size-structured stochastic populations is a long-standing and difficult problem. Rectilinear precautionary policies based on biomass and harvesting reference points have now become a standard approach to this problem. While these standard feedback policies are adapted from anal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	517,850
1405.0546	Kaggle LSHTC4 Winning Solution	Our winning submission to the 2014 Kaggle competition for Large Scale Hierarchical Text Classification (LSHTC) consists mostly of an ensemble of sparse generative models extending Multinomial Naive Bayes. The base-classifiers consist of hierarchically smoothed models combining document, label, and hierarchy level Multi...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	32,775
2106.13871	Transflower: probabilistic autoregressive dance generation with multimodal attention	Dance requires skillful composition of complex movements that follow rhythmic, tonal and timbral features of music. Formally, generating dance conditioned on a piece of music can be expressed as a problem of modelling a high-dimensional continuous motion signal, conditioned on an audio signal. In this work we make two ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	243,211
2202.03393	Link Prediction of Artificial Intelligence Concepts using Low Computational Power	This paper presents an approach proposed for the Science4cast 2021 competition, organized by the Institute of Advanced Research in Artificial Intelligence, whose main goal was to predict the likelihood of future associations between machine learning concepts in a semantic network. The developed methodology corresponds ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,188

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