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
2002.09632	Using Single-Step Adversarial Training to Defend Iterative Adversarial Examples	Adversarial examples have become one of the largest challenges that machine learning models, especially neural network classifiers, face. These adversarial examples break the assumption of attack-free scenario and fool state-of-the-art (SOTA) classifiers with insignificant perturbations to human. So far, researchers ac...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	165,129
2310.18457	LLMSTEP: LLM proofstep suggestions in Lean	We present LLMSTEP, a tool for integrating a language model into the Lean proof assistant. LLMSTEP is a Lean 4 tactic that sends a user's proof state to a server hosting a language model. The language model generates suggestions, which are checked in Lean and displayed to a user in their development environment. We pro...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	403,551
1906.03647	A Variant of Gaussian Process Dynamical Systems	In order to better model high-dimensional sequential data, we propose a collaborative multi-output Gaussian process dynamical system (CGPDS), which is a novel variant of GPDSs. The proposed model assumes that the output on each dimension is controlled by a shared global latent process and a private local latent process...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,445
2201.02628	Attention Option-Critic	Temporal abstraction in reinforcement learning is the ability of an agent to learn and use high-level behaviors, called options. The option-critic architecture provides a gradient-based end-to-end learning method to construct options. We propose an attention-based extension to this framework, which enables the agent to...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	274,597
2410.09448	Solving the Challenge Set without Solving the Task: On Winograd Schemas as a Test of Pronominal Coreference Resolution	Challenge sets such as the Winograd Schema Challenge (WSC) are used to benchmark systems' ability to resolve ambiguities in natural language. If one assumes as in existing work that solving a given challenge set is at least as difficult as solving some more general task, then high performance on the challenge set shoul...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	497,599
2008.07112	AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems	Accurate channel state information (CSI) feedback plays a vital role in improving the performance gain of massive multiple-input multiple-output (m-MIMO) systems, where the dilemma is excessive CSI overhead versus limited feedback bandwith. By considering the noisy CSI due to imperfect channel estimation, we propose a ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	192,003
2107.12512	H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction	Recent learning approaches that implicitly represent surface geometry using coordinate-based neural representations have shown impressive results in the problem of multi-view 3D reconstruction. The effectiveness of these techniques is, however, subject to the availability of a large number (several tens) of input views...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	247,908
1906.06058	Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification	Classification of malignancy for breast cancer and other cancer types is usually tackled as an object detection problem: Individual lesions are first localized and then classified with respect to malignancy. However, the drawback of this approach is that abstract features incorporating several lesions and areas that ar...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	135,203
2202.01471	Variational integrators for non-autonomous systems with applications to stabilization of multi-agent formations	Numerical methods that preserve geometric invariants of the system, such as energy, momentum or the symplectic form, are called geometric integrators. Variational integrators are an important class of geometric integrators. The general idea for those variational integrators is to discretize Hamilton's principle rather ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	278,495
2311.10735	Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLA	Autonomous vehicles have the potential to revolutionize transportation, but they must be able to navigate safely in traffic before they can be deployed on public roads. The goal of this project is to train autonomous vehicles to make decisions to navigate in uncertain environments using deep reinforcement learning tech...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	408,624
2410.21629	OFER: Occluded Face Expression Reconstruction	Reconstructing 3D face models from a single image is an inherently ill-posed problem, which becomes even more challenging in the presence of occlusions. In addition to fewer available observations, occlusions introduce an extra source of ambiguity, where multiple reconstructions can be equally valid. Despite the ubiqui...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	503,323
2410.05289	MARS: A neurosymbolic approach for interpretable drug discovery	Neurosymbolic (NeSy) artificial intelligence describes the combination of logic or rule-based techniques with neural networks. Compared to neural approaches, NeSy methods often possess enhanced interpretability, which is particularly promising for biomedical applications like drug discovery. However, since interpretabi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	495,654
2004.08604	UDDSketch: Accurate Tracking of Quantiles in Data Streams	We present UDDSketch (Uniform DDSketch), a novel sketch for fast and accurate tracking of quantiles in data streams. This sketch is heavily inspired by the recently introduced DDSketch, and is based on a novel bucket collapsing procedure that allows overcoming the intrinsic limits of the corresponding DDSketch procedur...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	173,115
1605.04418	Efficient sequential compression of multi-channel biomedical signals	This work proposes lossless and near-lossless compression algorithms for multi-channel biomedical signals. The algorithms are sequential and efficient, which makes them suitable for low-latency and low-power signal transmission applications. We make use of information theory and signal processing tools (such as univers...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	55,863
2305.00358	Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition	Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the social and legal frameworks, the use and distribution of these datasets are being restricted and strongly questioned. These databases, which have...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	361,309
2203.13437	BCOT: A Markerless High-Precision 3D Object Tracking Benchmark	Template-based 3D object tracking still lacks a high-precision benchmark of real scenes due to the difficulty of annotating the accurate 3D poses of real moving video objects without using markers. In this paper, we present a multi-view approach to estimate the accurate 3D poses of real moving objects, and then use bin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	287,623
2303.10251	Conformal Generative Modeling on Triangulated Surfaces	We propose conformal generative modeling, a framework for generative modeling on 2D surfaces approximated by discrete triangle meshes. Our approach leverages advances in discrete conformal geometry to develop a map from a source triangle mesh to a target triangle mesh of a simple manifold such as a sphere. After accoun...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	352,365
2104.09696	X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answering	Multilingual models, such as M-BERT and XLM-R, have gained increasing popularity, due to their zero-shot cross-lingual transfer learning capabilities. However, their generalization ability is still inconsistent for typologically diverse languages and across different benchmarks. Recently, meta-learning has garnered att...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	231,318
2011.08518	DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition	Sequence-based place recognition methods for all-weather navigation are well-known for producing state-of-the-art results under challenging day-night or summer-winter transitions. These systems, however, rely on complex handcrafted heuristics for sequential matching - which are applied on top of a pre-computed pairwise...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	206,903
2007.02103	Discovering Drug-Drug and Drug-Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forests	Patients with Acute Kidney Injury (AKI) increase mortality, morbidity, and long-term adverse events. Therefore, early identification of AKI may improve renal function recovery, decrease comorbidities, and further improve patients' survival. To control certain risk factors and develop targeted prevention strategies are ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	185,639
1308.5273	CrowdGrader: Crowdsourcing the Evaluation of Homework Assignments	Crowdsourcing offers a practical method for ranking and scoring large amounts of items. To investigate the algorithms and incentives that can be used in crowdsourcing quality evaluations, we built CrowdGrader, a tool that lets students submit and collaboratively grade solutions to homework assignments. We present the a...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	26,616
1612.01840	FMA: A Dataset For Music Analysis	We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections. The community's growing interest in feature and end-to-end learning is however restrained by the limited avai...	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	65,149
cs/0501077	Ontology-Based Users & Requests Clustering in Customer Service Management System	Customer Service Management is one of major business activities to better serve company customers through the introduction of reliable processes and procedures. Today this kind of activities is implemented through e-services to directly involve customers into business processes. Traditionally Customer Service Managemen...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	538,516
2108.12104	Binocular Mutual Learning for Improving Few-shot Classification	Most of the few-shot learning methods learn to transfer knowledge from datasets with abundant labeled data (i.e., the base set). From the perspective of class space on base set, existing methods either focus on utilizing all classes under a global view by normal pretraining, or pay more attention to adopt an episodic m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	252,388
1508.00021	Artificial Neural Networks Applied to Taxi Destination Prediction	We describe our first-place solution to the ECML/PKDD discovery challenge on taxi destination prediction. The task consisted in predicting the destination of a taxi based on the beginning of its trajectory, represented as a variable-length sequence of GPS points, and diverse associated meta-information, such as the dep...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	45,619
2409.14273	Lidar Panoptic Segmentation in an Open World	Addressing Lidar Panoptic Segmentation (LPS ) is crucial for safe deployment of autonomous vehicles. LPS aims to recognize and segment lidar points w.r.t. a pre-defined vocabulary of semantic classes, including thing classes of countable objects (e.g., pedestrians and vehicles) and stuff classes of amorphous regions (e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	490,399
0812.1869	Convex Sparse Matrix Factorizations	We present a convex formulation of dictionary learning for sparse signal decomposition. Convexity is obtained by replacing the usual explicit upper bound on the dictionary size by a convex rank-reducing term similar to the trace norm. In particular, our formulation introduces an explicit trade-off between size and spar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	2,775
1404.1355	Studying Social Networks at Scale: Macroscopic Anatomy of the Twitter Social Graph	Twitter is one of the largest social networks using exclusively directed links among accounts. This makes the Twitter social graph much closer to the social graph supporting real life communications than, for instance, Facebook. Therefore, understanding the structure of the Twitter social graph is interesting not only ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	32,101
2010.07255	Robust path-following control design of heavy vehicles based on multiobjective evolutionary optimization	The ability to deal with systems parametric uncertainties is an essential issue for heavy self-driving vehicles in unconfined environments. In this sense, robust controllers prove to be efficient for autonomous navigation. However, uncertainty matrices for this class of systems are usually defined by algebraic methods ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	200,753
2409.02327	Generative Principal Component Regression via Variational Inference	The ability to manipulate complex systems, such as the brain, to modify specific outcomes has far-reaching implications, particularly in the treatment of psychiatric disorders. One approach to designing appropriate manipulations is to target key features of predictive models. While generative latent variable models, su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	485,651
2312.16895	RLPlanner: Reinforcement Learning based Floorplanning for Chiplets with Fast Thermal Analysis	Chiplet-based systems have gained significant attention in recent years due to their low cost and competitive performance. As the complexity and compactness of a chiplet-based system increase, careful consideration must be given to microbump assignments, interconnect delays, and thermal limitations during the floorplan...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	418,554
2411.02726	Elliptical Wishart distributions: information geometry, maximum likelihood estimator, performance analysis and statistical learning	This paper deals with Elliptical Wishart distributions - which generalize the Wishart distribution - in the context of signal processing and machine learning. Two algorithms to compute the maximum likelihood estimator (MLE) are proposed: a fixed point algorithm and a Riemannian optimization method based on the derived ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	505,633
2402.06683	Sound Source Separation Using Latent Variational Block-Wise Disentanglement	While neural network approaches have made significant strides in resolving classical signal processing problems, it is often the case that hybrid approaches that draw insight from both signal processing and neural networks produce more complete solutions. In this paper, we present a hybrid classical digital signal proc...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	428,395
2403.17379	Exploring and Applying Audio-Based Sentiment Analysis in Music	Sentiment analysis is a continuously explored area of text processing that deals with the computational analysis of opinions, sentiments, and subjectivity of text. However, this idea is not limited to text and speech, in fact, it could be applied to other modalities. In reality, humans do not express themselves in text...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	441,441
2402.04618	Multi-Scale Semantic Segmentation with Modified MBConv Blocks	Recently, MBConv blocks, initially designed for efficiency in resource-limited settings and later adapted for cutting-edge image classification performances, have demonstrated significant potential in image classification tasks. Despite their success, their application in semantic segmentation has remained relatively u...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	427,530
2412.00100	Steering Rectified Flow Models in the Vector Field for Controlled Image Generation	Diffusion models (DMs) excel in photorealism, image editing, and solving inverse problems, aided by classifier-free guidance and image inversion techniques. However, rectified flow models (RFMs) remain underexplored for these tasks. Existing DM-based methods often require additional training, lack generalization to pre...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	512,493
2010.14759	Fine-grained Information Status Classification Using Discourse Context-Aware BERT	Previous work on bridging anaphora recognition (Hou et al., 2013a) casts the problem as a subtask of learning fine-grained information status (IS). However, these systems heavily depend on many hand-crafted linguistic features. In this paper, we propose a simple discourse context-aware BERT model for fine-grained IS cl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	203,561
1409.2388	Black-box Integration of Heterogeneous Modeling Languages for Cyber-Physical Systems	Robots belong to a class of Cyber-Physical Systems where complex software as a mobile device has to full tasks in a complex environment. Modeling robotics applications for analysis and code generation requires modeling languages for the logical software architecture and the system behavior. The MontiArcAutomaton modeli...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	35,903
2411.18172	Enhancing Computer Vision with Knowledge: a Rummikub Case Study	Artificial Neural Networks excel at identifying individual components in an image. However, out-of-the-box, they do not manage to correctly integrate and interpret these components as a whole. One way to alleviate this weakness is to expand the network with explicit knowledge and a separate reasoning component. In this...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	511,760
2301.07414	A Smart Adaptively Reconfigurable DC Battery for Higher Efficiency of Electric Vehicle Drive Trains	This paper proposes a drive train topology with a dynamically reconfigurable dc battery, which breaks hard-wired batteries into smaller subunits. It can rapidly control the output voltage and even contribute to voltage shaping of the inverter. Based upon the rapid development of low-voltage transistors and modular circ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	340,912
1002.4040	Handwritten Bangla Basic and Compound character recognition using MLP and SVM classifier	A novel approach for recognition of handwritten compound Bangla characters, along with the Basic characters of Bangla alphabet, is presented here. Compared to English like Roman script, one of the major stumbling blocks in Optical Character Recognition (OCR) of handwritten Bangla script is the large number of complex s...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	5,750
2309.03648	Promoting Fairness in GNNs: A Characterization of Stability	The Lipschitz bound, a technique from robust statistics, can limit the maximum changes in the output concerning the input, taking into account associated irrelevant biased factors. It is an efficient and provable method for examining the output stability of machine learning models without incurring additional computati...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	390,452
2501.06491	Improving Requirements Classification with SMOTE-Tomek Preprocessing	This study emphasizes the domain of requirements engineering by applying the SMOTE-Tomek preprocessing technique, combined with stratified K-fold cross-validation, to address class imbalance in the PROMISE dataset. This dataset comprises 969 categorized requirements, classified into functional and non-functional types....	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	true	524,006
2311.08952	A novel concept for Titan robotic exploration based on soft morphing aerial robots	This work introduces a novel approach for Titan exploration based on soft morphing aerial robots leveraging the use of flexible adaptive materials. The controlled deformation of the multirotor arms, actuated by a combination of a pneumatic system and a tendon mechanism, provides the explorer robot with the ability to p...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	407,931
2209.07364	Continuous MDP Homomorphisms and Homomorphic Policy Gradient	Abstraction has been widely studied as a way to improve the efficiency and generalization of reinforcement learning algorithms. In this paper, we study abstraction in the continuous-control setting. We extend the definition of MDP homomorphisms to encompass continuous actions in continuous state spaces. We derive a pol...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	317,715
1402.5619	A Novel Histogram Based Robust Image Registration Technique	In this paper, a method for Automatic Image Registration (AIR) through histogram is proposed. Automatic image registration is one of the crucial steps in the analysis of remotely sensed data. A new acquired image must be transformed, using image registration techniques, to match the orientation and scale of previous re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	31,085
1907.12546	Diffusion Hypercontractivity via Generalized Density Manifold	We prove a one-parameter family of diffusion hypercontractivity and present the associated Log-Sobolev, Poincare and Talagrand inequalities. A mean-field type Bakry-Emery iterative calculus and volume measure based integration formula (Yano's formula) are presented. Our results are based on the interpolation among dive...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	140,148
2402.03830	OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving	With deep learning and computer vision technology development, autonomous driving provides new solutions to improve traffic safety and efficiency. The importance of building high-quality datasets is self-evident, especially with the rise of end-to-end autonomous driving algorithms in recent years. Data plays a core rol...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	427,214
2311.09984	A Framework for Modeling, Analyzing, and Decision-Making in Disease Spread Dynamics and Medicine/Vaccine Distribution	The challenges posed by epidemics and pandemics are immense, especially if the causes are novel. This article introduces a versatile open-source simulation framework designed to model intricate dynamics of infectious diseases across diverse population centres. Taking inspiration from historical precedents such as the S...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	408,376
1602.06967	Blind score normalization method for PLDA based speaker recognition	Probabilistic Linear Discriminant Analysis (PLDA) has become state-of-the-art method for modeling $i$-vector space in speaker recognition task. However the performance degradation is observed if enrollment data size differs from one speaker to another. This paper presents a solution to such problem by introducing new P...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	52,446
2207.09665	ExoSGAN and ExoACGAN: Exoplanet Detection using Adversarial Training Algorithms	Exoplanet detection opens the door to the discovery of new habitable worlds and helps us understand how planets were formed. With the objective of finding earth-like habitable planets, NASA launched Kepler space telescope and its follow up mission K2. The advancement of observation capabilities has increased the range ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	308,974
2012.14259	Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset	This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessio...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	213,456
2409.07652	Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networks	Uncertainty quantification plays a key role in the development of autonomous systems, decision-making, and tracking over wireless sensor networks (WSNs). However, there is a need of providing uncertainty confidence bounds, especially for distributed machine learning-based tracking, dealing with different volumes of dat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	487,602
2111.04394	Get a Model! Model Hijacking Attack Against Machine Learning Models	Machine learning (ML) has established itself as a cornerstone for various critical applications ranging from autonomous driving to authentication systems. However, with this increasing adoption rate of machine learning models, multiple attacks have emerged. One class of such attacks is training time attack, whereby an ...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	265,471
1808.07214	A Characterwise Windowed Approach to Hebrew Morphological Segmentation	This paper presents a novel approach to the segmentation of orthographic word forms in contemporary Hebrew, focusing purely on splitting without carrying out morphological analysis or disambiguation. Casting the analysis task as character-wise binary classification and using adjacent character and word-based lexicon-lo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	105,686
2412.20901	ILDiff: Generate Transparent Animated Stickers by Implicit Layout Distillation	High-quality animated stickers usually contain transparent channels, which are often ignored by current video generation models. To generate fine-grained animated transparency channels, existing methods can be roughly divided into video matting algorithms and diffusion-based algorithms. The methods based on video matti...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	521,396
2412.10659	MEATRD: Multimodal Anomalous Tissue Region Detection Enhanced with Spatial Transcriptomics	The detection of anomalous tissue regions (ATRs) within affected tissues is crucial in clinical diagnosis and pathological studies. Conventional automated ATR detection methods, primarily based on histology images alone, falter in cases where ATRs and normal tissues have subtle visual differences. The recent spatial tr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	517,039
2404.11184	FIZZ: Factual Inconsistency Detection by Zoom-in Summary and Zoom-out Document	Through the advent of pre-trained language models, there have been notable advancements in abstractive summarization systems. Simultaneously, a considerable number of novel methods for evaluating factual consistency in abstractive summarization systems has been developed. But these evaluation approaches incorporate sub...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	447,415
2406.00765	The Embodied World Model Based on LLM with Visual Information and Prediction-Oriented Prompts	In recent years, as machine learning, particularly for vision and language understanding, has been improved, research in embedded AI has also evolved. VOYAGER is a well-known LLM-based embodied AI that enables autonomous exploration in the Minecraft world, but it has issues such as underutilization of visual data and i...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	460,014
2408.16305	Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach	In recent years, the multimedia forensics and security community has seen remarkable progress in multitask learning for DeepFake (i.e., face forgery) detection. The prevailing strategy has been to frame DeepFake detection as a binary classification problem augmented by manipulation-oriented auxiliary tasks. This strate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,282
2407.17780	HF-Fed: Hierarchical based customized Federated Learning Framework for X-Ray Imaging	In clinical applications, X-ray technology is vital for noninvasive examinations like mammography, providing essential anatomical information. However, the radiation risk associated with X-ray procedures raises concerns. X-ray reconstruction is crucial in medical imaging for detailed visual representations of internal ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	476,111
2101.05950	Robusta: Robust AutoML for Feature Selection via Reinforcement Learning	Several AutoML approaches have been proposed to automate the machine learning (ML) process, such as searching for the ML model architectures and hyper-parameters. However, these AutoML pipelines only focus on improving the learning accuracy of benign samples while ignoring the ML model robustness under adversarial atta...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	215,558
2312.08829	When are selector control strategies optimal for constrained monotone systems?	This paper considers optimal control problems defined by a monotone dynamical system, a monotone cost, and monotone constraints. We identify families of such problems for which the optimal solution is bang-ride, i.e., always operates on the constraint boundaries, and prove that the optimal policy switches between a fin...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	415,466
2407.07713	Data-Driven Radio Environment Map Estimation Using Graph Neural Networks	Radio Environment Maps (REMs) are crucial for numerous applications in Telecom. The construction of accurate Radio Environment Maps (REMs) has become an important and challenging topic in recent decades. In this paper, we present a method to estimate REMs using Graph Neural Networks. This approach utilizes both physica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	471,869
1307.0032	Memory Limited, Streaming PCA	We consider streaming, one-pass principal component analysis (PCA), in the high-dimensional regime, with limited memory. Here, $p$-dimensional samples are presented sequentially, and the goal is to produce the $k$-dimensional subspace that best approximates these points. Standard algorithms require $O(p^2)$ memory; mea...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	25,517
2403.13010	A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats	In today's digital age, our dependence on IoT (Internet of Things) and IIoT (Industrial IoT) systems has grown immensely, which facilitates sensitive activities such as banking transactions and personal, enterprise data, and legal document exchanges. Cyberattackers consistently exploit weak security measures and tools....	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	439,439
2409.08769	Causal Transformer for Fusion and Pose Estimation in Deep Visual Inertial Odometry	In recent years, transformer-based architectures become the de facto standard for sequence modeling in deep learning frameworks. Inspired by the successful examples, we propose a causal visual-inertial fusion transformer (VIFT) for pose estimation in deep visual-inertial odometry. This study aims to improve pose estima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	488,051
2404.04681	Computation and Critical Transitions of Rate-Distortion-Perception Functions With Wasserstein Barycenter	The information rate-distortion-perception (RDP) function characterizes the three-way trade-off between description rate, average distortion, and perceptual quality measured by discrepancy between probability distributions and has been applied to emerging areas in communications empowered by generative modeling. We stu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	444,755
2310.05655	Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs	In the context of inferring a Bayesian network structure (directed acyclic graph, DAG for short), we devise a non-reversible continuous time Markov chain, the ``Causal Zig-Zag sampler'', that targets a probability distribution over classes of observationally equivalent (Markov equivalent) DAGs. The classes are represen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	398,237
2303.07272	Accounting for multiplicity in machine learning benchmark performance	Machine learning methods are commonly evaluated and compared by their performance on data sets from public repositories. This allows for multiple methods, oftentimes several thousands, to be evaluated under identical conditions and across time. The highest ranked performance on a problem is referred to as state-of-the-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	351,193
2412.20489	Low-Thrust Under-Actuated Satellite Formation Guidance and Control Strategies	This study presents autonomous guidance and control strategies for the purpose of reconfiguring close-range multi-satellite formations. The formation under consideration includes $N$ under-actuated deputy satellites and an uncontrolled virtual or physical chief spacecraft. The guidance problem is formulated as a trajec...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	521,244
1304.1526	Simulation Approaches to General Probabilistic Inference on Belief Networks	A number of algorithms have been developed to solve probabilistic inference problems on belief networks. These algorithms can be divided into two main groups: exact techniques which exploit the conditional independence revealed when the graph structure is relatively sparse, and probabilistic sampling techniques which e...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,559
1203.3815	Theory and Applications of Compressed Sensing	Compressed sensing is a novel research area, which was introduced in 2006, and since then has already become a key concept in various areas of applied mathematics, computer science, and electrical engineering. It surprisingly predicts that high-dimensional signals, which allow a sparse representation by a suitable basi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	14,995
2307.10854	BlendFace: Re-designing Identity Encoders for Face-Swapping	The great advancements of generative adversarial networks and face recognition models in computer vision have made it possible to swap identities on images from single sources. Although a lot of studies seems to have proposed almost satisfactory solutions, we notice previous methods still suffer from an identity-attrib...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	380,714
2203.10739	Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation	Sparsely annotated semantic segmentation (SASS) aims to train a segmentation network with coarse-grained (i.e., point-, scribble-, and block-wise) supervisions, where only a small proportion of pixels are labeled in each image. In this paper, we propose a novel tree energy loss for SASS by providing semantic guidance f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	286,663
2306.00988	Continual Learning for Abdominal Multi-Organ and Tumor Segmentation	The ability to dynamically extend a model to new data and classes is critical for multiple organ and tumor segmentation. However, due to privacy regulations, accessing previous data and annotations can be problematic in the medical domain. This poses a significant barrier to preserving the high segmentation accuracy of...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	370,250
2103.00053	PURSUhInT: In Search of Informative Hint Points Based on Layer Clustering for Knowledge Distillation	One of the most efficient methods for model compression is hint distillation, where the student model is injected with information (hints) from several different layers of the teacher model. Although the selection of hint points can drastically alter the compression performance, conventional distillation approaches ove...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	222,128
2312.17019	Efficient Learning of Long-Range and Equivariant Quantum Systems	In this work, we consider a fundamental task in quantum many-body physics - finding and learning ground states of quantum Hamiltonians and their properties. Recent works have studied the task of predicting the ground state expectation value of sums of geometrically local observables by learning from data. For short-ran...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	418,596
1812.06128	Machine learning approaches to understand the influence of urban environments on human's physiological response	This research proposes a framework for signal processing and information fusion of spatial-temporal multi-sensor data pertaining to understanding patterns of humans physiological changes in an urban environment. The framework includes signal frequency unification, signal pairing, signal filtering, signal quantification...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	116,545
1807.03043	Convolutional Recurrent Neural Networks for Glucose Prediction	Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with Type 1 diabetes mellitus (T1DM) such as the artificial pancreas and insulin bolus calculators leverage machine learning techniques for predicting subcutaneous glucose for improved control. Deep learni...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	102,412
1906.09832	A computational model of early language acquisition from audiovisual experiences of young infants	Earlier research has suggested that human infants might use statistical dependencies between speech and non-linguistic multimodal input to bootstrap their language learning before they know how to segment words from running speech. However, feasibility of this hypothesis in terms of real-world infant experiences has re...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	136,285
2410.12074	nvTorchCam: An Open-source Library for Camera-Agnostic Differentiable Geometric Vision	We introduce nvTorchCam, an open-source library under the Apache 2.0 license, designed to make deep learning algorithms camera model-independent. nvTorchCam abstracts critical camera operations such as projection and unprojection, allowing developers to implement algorithms once and apply them across diverse camera mod...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	498,843
2010.03152	Projection-Based Constrained Policy Optimization	We consider the problem of learning control policies that optimize a reward function while satisfying constraints due to considerations of safety, fairness, or other costs. We propose a new algorithm, Projection-Based Constrained Policy Optimization (PCPO). This is an iterative method for optimizing policies in a two-s...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	199,298
2106.15808	Optimal Epidemic Control as a Contextual Combinatorial Bandit with Budget	In light of the COVID-19 pandemic, it is an open challenge and critical practical problem to find a optimal way to dynamically prescribe the best policies that balance both the governmental resources and epidemic control in different countries and regions. To solve this multi-dimensional tradeoff of exploitation and ex...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	243,877
cs/0501025	A Logic for Non-Monotone Inductive Definitions	Well-known principles of induction include monotone induction and different sorts of non-monotone induction such as inflationary induction, induction over well-founded sets and iterated induction. In this work, we define a logic formalizing induction over well-founded sets and monotone and iterated induction. Just as t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	538,487
2002.09718	Safe Screening for the Generalized Conditional Gradient Method	The conditional gradient method (CGM) has been widely used for fast sparse approximation, having a low per iteration computational cost for structured sparse regularizers. We explore the sparsity acquiring properties of a generalized CGM (gCGM), where the constraint is replaced by a penalty function based on a gauge pe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	165,159
1712.05914	Cyberattack Detection in Mobile Cloud Computing: A Deep Learning Approach	With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry. However, mobile cloud applications are facing security issues such as data integrity, users' confidentiality, and service availability. A preventive approach to such pro...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	86,795
2010.15647	Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint	Delineating the brain tumor from magnetic resonance (MR) images is critical for the treatment of gliomas. However, automatic delineation is challenging due to the complex appearance and ambiguous outlines of tumors. Considering that multi-modal MR images can reflect different tumor biological properties, we develop a n...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	203,834
2212.06921	Losses over Labels: Weakly Supervised Learning via Direct Loss Construction	Owing to the prohibitive costs of generating large amounts of labeled data, programmatic weak supervision is a growing paradigm within machine learning. In this setting, users design heuristics that provide noisy labels for subsets of the data. These weak labels are combined (typically via a graphical model) to form ps...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	336,246
2201.01741	Understanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective	Entropy coding is the backbone data compression. Novel machine-learning based compression methods often use a new entropy coder called Asymmetric Numeral Systems (ANS) [Duda et al., 2015], which provides very close to optimal bitrates and simplifies [Townsend et al., 2019] advanced compression techniques such as bits-b...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	274,336
2101.00563	Learning Neural Networks on SVD Boosted Latent Spaces for Semantic Classification	The availability of large amounts of data and compelling computation power have made deep learning models much popular for text classification and sentiment analysis. Deep neural networks have achieved competitive performance on the above tasks when trained on naive text representations such as word count, term frequen...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	214,127
2310.04639	X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection	Generative adversarial networks (GANs) have remarkably advanced in diverse domains, especially image generation and editing. However, the misuse of GANs for generating deceptive images, such as face replacement, raises significant security concerns, which have gained widespread attention. Therefore, it is urgent to dev...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	397,745
2403.09157	VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation	In the field of medical image segmentation, models based on both CNN and Transformer have been thoroughly investigated. However, CNNs have limited modeling capabilities for long-range dependencies, making it challenging to exploit the semantic information within images fully. On the other hand, the quadratic computatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	437,651
2108.02274	LEO: Learning Energy-based Models in Factor Graph Optimization	We address the problem of learning observation models end-to-end for estimation. Robots operating in partially observable environments must infer latent states from multiple sensory inputs using observation models that capture the joint distribution between latent states and observations. This inference problem can be ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	249,265
2102.00319	Efficient CNN Building Blocks for Encrypted Data	Machine learning on encrypted data can address the concerns related to privacy and legality of sharing sensitive data with untrustworthy service providers. Fully Homomorphic Encryption (FHE) is a promising technique to enable machine learning and inferencing while providing strict guarantees against information leakage...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	217,741
1608.04414	Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back	In stochastic convex optimization the goal is to minimize a convex function $F(x) \doteq {\mathbf E}_{{\mathbf f}\sim D}[{\mathbf f}(x)]$ over a convex set $\cal K \subset {\mathbb R}^d$ where $D$ is some unknown distribution and each $f(\cdot)$ in the support of $D$ is convex over $\cal K$. The optimization is commonl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	59,821
1905.01546	Latent Unexpected and Useful Recommendation	Providing unexpected recommendations is an important task for recommender systems. To do this, we need to start from the expectations of users and deviate from these expectations when recommending items. Previously proposed approaches model user expectations in the feature space, making them limited to the items that t...	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	false	129,744
1911.09512	A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM	Machine and deep learning-based algorithms are the emerging approaches in addressing prediction problems in time series. These techniques have been shown to produce more accurate results than conventional regression-based modeling. It has been reported that artificial Recurrent Neural Networks (RNN) with memory, such a...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	154,542
2310.17493	A Hybrid Graph Network for Complex Activity Detection in Video	Interpretation and understanding of video presents a challenging computer vision task in numerous fields - e.g. autonomous driving and sports analytics. Existing approaches to interpreting the actions taking place within a video clip are based upon Temporal Action Localisation (TAL), which typically identifies short-te...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	403,157
2405.11242	Advancing fNIRS Neuroimaging through Synthetic Data Generation and Machine Learning Applications	This study presents an integrated approach for advancing functional Near-Infrared Spectroscopy (fNIRS) neuroimaging through the synthesis of data and application of machine learning models. By addressing the scarcity of high-quality neuroimaging datasets, this work harnesses Monte Carlo simulations and parametric head ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	455,061

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