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
1610.00321	Low-dose CT denoising with convolutional neural network	To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image quality. In this paper, we propose a noise reduction method for low-dose CT via deep neural network without accessing original projection data...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	61,820
1803.05123	Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training	Deep neural networks (DNNs) are known to be vulnerable to adversarial examples which contain human-imperceptible perturbations. A series of defending methods, either proactive defence or reactive defence, have been proposed in the recent years. However, most of the methods can only handle specific attacks. For example,...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	92,580
2209.08461	Random Fourier Features for Asymmetric Kernels	The random Fourier features (RFFs) method is a powerful and popular technique in kernel approximation for scalability of kernel methods. The theoretical foundation of RFFs is based on the Bochner theorem that relates symmetric, positive definite (PD) functions to probability measures. This condition naturally excludes ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	318,136
2410.09904	Equitable Access to Justice: Logical LLMs Show Promise	The costs and complexity of the American judicial system limit access to legal solutions for many Americans. Large language models (LLMs) hold great potential to improve access to justice. However, a major challenge in applying AI and LLMs in legal contexts, where consistency and reliability are crucial, is the need fo...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	true	497,822
2110.04667	Competitive Perimeter Defense of Conical Environments	We consider a perimeter defense problem in a planar conical environment in which a single vehicle, having a finite capture radius, aims to defend a concentric perimeter from mobile intruders. The intruders are arbitrarily released at the circumference of the environment and they move radially toward the perimeter with ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	259,991
2412.15525	Generalized Back-Stepping Experience Replay in Sparse-Reward Environments	Back-stepping experience replay (BER) is a reinforcement learning technique that can accelerate learning efficiency in reversible environments. BER trains an agent with generated back-stepping transitions of collected experiences and normal forward transitions. However, the original algorithm is designed for a dense-re...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	519,156
2408.01262	RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework	Retrieval-Augmented Generation (RAG) is a powerful approach that enables large language models (LLMs) to incorporate external knowledge. However, evaluating the effectiveness of RAG systems in specialized scenarios remains challenging due to the high costs of data construction and the lack of suitable evaluation metric...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	478,161
1504.08153	Principal Patterns on Graphs: Discovering Coherent Structures in Datasets	Graphs are now ubiquitous in almost every field of research. Recently, new research areas devoted to the analysis of graphs and data associated to their vertices have emerged. Focusing on dynamical processes, we propose a fast, robust and scalable framework for retrieving and analyzing recurring patterns of activity on...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	42,621
1901.07884	Rank consistent ordinal regression for neural networks with application to age estimation	In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy. Recently, the deep learning community adopted ordinal regression frameworks to take such ordering information into...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,328
2407.02362	Fast, Scalable, Energy-Efficient Non-element-wise Matrix Multiplication on FPGA	Modern Neural Network (NN) architectures heavily rely on vast numbers of multiply-accumulate arithmetic operations, constituting the predominant computational cost. Therefore, this paper proposes a high-throughput, scalable and energy efficient non-element-wise matrix multiplication unit on FPGAs as a basic component o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	469,696
2010.06261	Neighborhood Preserving Kernels for Attributed Graphs	We describe the design of a reproducing kernel suitable for attributed graphs, in which the similarity between the two graphs is defined based on the neighborhood information of the graph nodes with the aid of a product graph formulation. We represent the proposed kernel as the weighted sum of two other kernels of whic...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	200,428
2104.02987	Plinius: Secure and Persistent Machine Learning Model Training	With the increasing popularity of cloud based machine learning (ML) techniques there comes a need for privacy and integrity guarantees for ML data. In addition, the significant scalability challenges faced by DRAM coupled with the high access-times of secondary storage represent a huge performance bottleneck for ML sys...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	228,930
2312.17345	3VL: Using Trees to Improve Vision-Language Models' Interpretability	Vision-Language models (VLMs) have proven to be effective at aligning image and text representations, producing superior zero-shot results when transferred to many downstream tasks. However, these representations suffer from some key shortcomings in understanding Compositional Language Concepts (CLC), such as recognizi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	418,720
2409.19912	HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning	Data heterogeneity among Federated Learning (FL) users poses a significant challenge, resulting in reduced global model performance. The community has designed various techniques to tackle this issue, among which Knowledge Distillation (KD)-based techniques are common. While these techniques effectively improve perfo...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	492,905
2111.14690	DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion	A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization, and following re-identification (re-ID) for object association. This pipeline is partially motivated by recent progress in both object detection and re-ID, and partially motivated by biases in existing tracking datasets, wh...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,673
2305.14709	Regret Matching+: (In)Stability and Fast Convergence in Games	Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery. Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy st...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	367,201
2310.19232	Adapter Pruning using Tropical Characterization	Adapters are widely popular parameter-efficient transfer learning approaches in natural language processing that insert trainable modules in between layers of a pre-trained language model. Apart from several heuristics, however, there has been a lack of studies analyzing the optimal number of adapter parameters needed ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	403,909
1912.12096	Coverage Analysis of Relay Assisted Millimeter Wave Cellular Networks with Spatial Correlation	We propose a novel analytical framework for evaluating the coverage performance of a millimeter wave (mmWave) cellular network where idle user equipments (UEs) act as relays. In this network, the base station (BS) adopts either the direct mode to transmit to the destination UE, or the relay mode if the direct mode fail...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	158,751
2310.18877	Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion Recognition	Previous work has established that a person's demographics and speech style affect how well speech processing models perform for them. But where does this bias come from? In this work, we present the Speech Embedding Association Test (SpEAT), a method for detecting bias in one type of model used for many speech tasks: ...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	403,743
2406.13232	Towards Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in the Era of Large Language Models	Open Domain Question Answering (ODQA) within natural language processing involves building systems that answer factual questions using large-scale knowledge corpora. Recent advances stem from the confluence of several factors, such as large-scale training datasets, deep learning techniques, and the rise of large langua...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	465,762
1307.6883	A gradient descent technique coupled with a dynamic simulation to determine the near optimum orientation of floor plan designs	A prototype tool to assist architects during the early design stage of floor plans has been developed, consisting of an Evolutionary Program for the Space Allocation Problem (EPSAP), which generates sets of floor plan alternatives according to the architect's preferences; and a Floor Plan Performance Optimization Progr...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	26,051
2310.06218	SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration	The study of sparsity in Convolutional Neural Networks (CNNs) has become widespread to compress and accelerate models in environments with limited resources. By constraining N consecutive weights along the output channel to be group-wise non-zero, the recent network with 1$\times$N sparsity has received tremendous popu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	398,474
1708.04765	Dialogue Act Segmentation for Vietnamese Human-Human Conversational Texts	Dialog act identification plays an important role in understanding conversations. It has been widely applied in many fields such as dialogue systems, automatic machine translation, automatic speech recognition, and especially useful in systems with human-computer natural language dialogue interfaces such as virtual ass...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	79,017
1109.2048	An Expressive Language and Efficient Execution System for Software Agents	Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	12,074
1702.07942	BARCHAN: Blob Alignment for Robust CHromatographic ANalysis	Comprehensive Two dimensional gas chromatography (GCxGC) plays a central role into the elucidation of complex samples. The automation of the identification of peak areas is of prime interest to obtain a fast and repeatable analysis of chromatograms. To determine the concentration of compounds or pseudo-compounds, templ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	68,871
2402.05126	Graph Neural Network and NER-Based Text Summarization	With the abundance of data and information in todays time, it is nearly impossible for man, or, even machine, to go through all of the data line by line. What one usually does is to try to skim through the lines and retain the absolutely important information, that in a more formal term is called summarization. Text su...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	427,734
1802.09058	Seeing Small Faces from Robust Anchor's Perspective	This paper introduces a novel anchor design to support anchor-based face detection for superior scale-invariant performance, especially on tiny faces. To achieve this, we explicitly address the problem that anchor-based detectors drop performance drastically on faces with tiny sizes, e.g. less than 16x16 pixels. In thi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	91,254
2109.03814	Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers	Panoptic segmentation involves a combination of joint semantic segmentation and instance segmentation, where image contents are divided into two types: things and stuff. We present Panoptic SegFormer, a general framework for panoptic segmentation with transformers. It contains three innovative components: an efficient ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,180
2501.06740	Rice Leaf Disease Detection: A Comparative Study Between CNN, Transformer and Non-neural Network Architectures	In nations such as Bangladesh, agriculture plays a vital role in providing livelihoods for a significant portion of the population. Identifying and classifying plant diseases early is critical to prevent their spread and minimize their impact on crop yield and quality. Various computer vision techniques can be used for...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	524,107
2203.11997	Federated Self-Supervised Learning for Acoustic Event Classification	Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization. Federated learning (FL) is a compelling framework that decouples data collection and model training to enhance customer privacy. In this work, we investigate the feasibility of apply...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	287,105
2103.15947	Federated Learning with Taskonomy for Non-IID Data	Classical federated learning approaches incur significant performance degradation in the presence of non-IID client data. A possible direction to address this issue is forming clusters of clients with roughly IID data. Most solutions following this direction are iterative and relatively slow, also prone to convergence ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	227,385
2310.00906	A Decentralized Cooperative Navigation Approach for Visual Homing Networks	Visual homing is a lightweight approach to visual navigation. Given the stored information of an initial 'home' location, the navigation task back to this location is achieved from any other location by comparing the stored home information to the current image and extracting a motion vector. A challenge that constrain...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	396,203
2110.13670	W-Net: A Two-Stage Convolutional Network for Nucleus Detection in Histopathology Image	Pathological diagnosis is the gold standard for cancer diagnosis, but it is labor-intensive, in which tasks such as cell detection, classification, and counting are particularly prominent. A common solution for automating these tasks is using nucleus segmentation technology. However, it is hard to train a robust nucleu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	263,275
1506.00982	Game Theory for Signal Processing in Networks	In this tutorial, the basics of game theory are introduced along with an overview of its most recent and emerging applications in signal processing. One of the main features of this contribution is to gather in a single paper some fundamental game-theoretic notions and tools which, over the past few years, have become ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	43,737
2003.00187	Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation	Unpaired image-to-image (I2I) translation has received considerable attention in pattern recognition and computer vision because of recent advancements in generative adversarial networks (GANs). However, due to the lack of explicit supervision, unpaired I2I models often fail to generate realistic images, especially in ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	166,217
1802.09338	An Energy Balance Based Method for Parameter Identification of a Free-Flying Robot Grasping An Unknown Object	The estimation of inertial parameters of a robotic system is crucial for better trajectory tracking performance, specially when model-based controllers are used for carrying out precise tasks. In this paper, we consider the scenario of grasping an object of unknown properties by a free-flyer space robot with limited ac...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	91,309
2204.07096	Detection of Degraded Acacia tree species using deep neural networks on uav drone imagery	Deep-learning-based image classification and object detection has been applied successfully to tree monitoring. However, studies of tree crowns and fallen trees, especially on flood inundated areas, remain largely unexplored. Detection of degraded tree trunks on natural environments such as water, mudflats, and natural...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	291,562
2409.15904	Unimotion: Unifying 3D Human Motion Synthesis and Understanding	We introduce Unimotion, the first unified multi-task human motion model capable of both flexible motion control and frame-level motion understanding. While existing works control avatar motion with global text conditioning, or with fine-grained per frame scripts, none can do both at once. In addition, none of the exist...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	491,120
2305.15985	Resource Allocation in Cell-Free MU-MIMO Multicarrier System with Finite and Infinite Blocklength	The explosive growth of data results in more scarce spectrum resources. It is important to optimize the system performance under limited resources. In this paper, we investigate how to achieve weighted throughput (WTP) maximization for cell-free (CF) multiuser MIMO (MU-MIMO) multicarrier (MC) systems through resource a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	367,876
1911.03263	Estimating States for Nonlinear Systems Using the Particle Filter	Kalman filtering has been traditionally applied in three application areas of estimation, state estimation, parameter estimation (a.k.a. model updating), and dual estimation. However, Kalman filter is often not sufficient when experimenting with highly uncertain nonlinear dynamic systems. In this study, a nonlinear est...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	152,592
2311.13471	Comparative Analysis of Linear Regression, Gaussian Elimination, and LU Decomposition for CT Real Estate Purchase Decisions	This paper presents a comprehensive evaluation of three distinct computational algorithms applied to the decision-making process of real estate purchases. Specifically, we analyze the efficacy of Linear Regression from Scikit-learn library, Gaussian Elimination with partial pivoting, and LU Decomposition in predicting ...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	409,748
2204.09888	Fairness in Graph Mining: A Survey	Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most of these algorithms lack fairness considerations. As a consequence, they could lead to discrimination towards certain populations when exploi...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	292,597
2010.11026	A Large-Scale Analysis of IoT Firmware Version Distribution in the Wild	This paper examines the up-to-dateness of installed firmware versions of IoT devices accessible via public internet. It analyzes datasets of 1.06m devices collected from the IoT search engine Censys and maps the results against the latest firmware version each manufacturer offers. By applying the SEMMA data mining proc...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	202,095
1311.2123	Linear-Complexity Overhead-Optimized Random Linear Network Codes	Sparse random linear network coding (SRLNC) is an attractive technique proposed in the literature to reduce the decoding complexity of random linear network coding. Recognizing the fact that the existing SRLNC schemes are not efficient in terms of the required reception overhead, we consider the problem of designing ov...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,289
1607.08481	A Nonlocal Denoising Algorithm for Manifold-Valued Images Using Second Order Statistics	Nonlocal patch-based methods, in particular the Bayes' approach of Lebrun, Buades and Morel (2013), are considered as state-of-the-art methods for denoising (color) images corrupted by white Gaussian noise of moderate variance. This paper is the first attempt to generalize this technique to manifold-valued images. Such...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	59,166
2303.08128	ViperGPT: Visual Inference via Python Execution for Reasoning	Answering visual queries is a complex task that requires both visual processing and reasoning. End-to-end models, the dominant approach for this task, do not explicitly differentiate between the two, limiting interpretability and generalization. Learning modular programs presents a promising alternative, but has proven...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	351,520
1812.10859	Signal Classification under structure sparsity constraints	Object Classification is a key direction of research in signal and image processing, computer vision and artificial intelligence. The goal is to come up with algorithms that automatically analyze images and put them in predefined categories. This dissertation focuses on the theory and application of sparse signal proce...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	117,451
1802.06769	Technique for designing a domain ontology	The article describes the technique for designing a domain ontology, shows the flowchart of algorithm design and example of constructing a fragment of the ontology of the subject area of Computer Science is considered.	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	90,741
2405.10064	Meta results on data-driven control of nonlinear systems	This note aims to provide a systematic understanding of direct data-driven control, enriching the existing literature not by adding another isolated result, but rather by offering a comprehensive, versatile, and unifying framework that sets the stage for future explorations and applications in this domain. To this end,...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	454,636
2207.05855	A Conceptual Framework for Using Machine Learning to Support Child Welfare Decisions	Human services systems make key decisions that impact individuals in the society. The U.S. child welfare system makes such decisions, from screening-in hotline reports of suspected abuse or neglect for child protective investigations, placing children in foster care, to returning children to permanent home settings. Th...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	307,693
1711.00436	Hierarchical Representations for Efficient Architecture Search	We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human ex...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	83,716
2302.13577	DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving	Outdoor 3D object detection has played an essential role in the environment perception of autonomous driving. In complicated traffic situations, precise object recognition provides indispensable information for prediction and planning in the dynamic system, improving self-driving safety and reliability. However, with t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	348,000
1606.03860	Robust Probabilistic Modeling with Bayesian Data Reweighting	Probabilistic models analyze data by relying on a set of assumptions. Data that exhibit deviations from these assumptions can undermine inference and prediction quality. Robust models offer protection against mismatch between a model's assumptions and reality. We propose a way to systematically detect and mitigate mism...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	57,159
2003.08741	A Convolutional Neural Network-based Patent Image Retrieval Method for Design Ideation	The patent database is often used in searches of inspirational stimuli for innovative design opportunities because of its large size, extensive variety and rich design information in patent documents. However, most patent mining research only focuses on textual information and ignores visual information. Herein, we pro...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	168,830
1102.2986	Sidon Sequences and Doubly Periodic Two-Dimensional Synchronization Patterns	Sidon sequences and their generalizations have found during the years and especially recently various applications in coding theory. One of the most important applications of these sequences is in the connection of synchronization patterns. A few constructions of two-dimensional synchronization patterns are based on th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,198
2202.10075	ICSML: Industrial Control Systems ML Framework for native inference using IEC 61131-3 code	Industrial Control Systems (ICS) have played a catalytic role in enabling the 4th Industrial Revolution. ICS devices like Programmable Logic Controllers (PLCs), automate, monitor, and control critical processes in industrial, energy, and commercial environments. The convergence of traditional Operational Technology (OT...	false	false	false	false	false	false	true	false	false	false	true	false	true	false	false	false	false	false	281,412
2003.11540	Learning What to Learn for Video Object Segmentation	Video object segmentation (VOS) is a highly challenging problem, since the target object is only defined during inference with a given first-frame reference mask. The problem of how to capture and utilize this limited target information remains a fundamental research question. We address this by introducing an end-to-e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	169,637
1906.04649	`Project & Excite' Modules for Segmentation of Volumetric Medical Scans	Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging. Recently, squeeze and excitation (SE) modules and variations thereof have been introduced to recalibrate feature maps channel- and spatial-wise, which can boost performance while only minimally i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	134,785
2404.17967	SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images	Statistical shape modeling (SSM) is a powerful computational framework for quantifying and analyzing the geometric variability of anatomical structures, facilitating advancements in medical research, diagnostics, and treatment planning. Traditional methods for shape modeling from imaging data demand significant manual ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	450,075
2108.01265	Memorize, Factorize, or be Na\"ive: Learning Optimal Feature Interaction Methods for CTR Prediction	Click-through rate prediction is one of the core tasks in commercial recommender systems. It aims to predict the probability of a user clicking a particular item given user and item features. As feature interactions bring in non-linearity, they are widely adopted to improve the performance of CTR prediction models. The...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	248,974
2202.06771	DS4DH at TREC Health Misinformation 2021: Multi-Dimensional Ranking Models with Transfer Learning and Rank Fusion	This paper describes the work of the Data Science for Digital Health (DS4DH) group at the TREC Health Misinformation Track 2021. The TREC Health Misinformation track focused on the development of retrieval methods that provide relevant, correct and credible information for health related searches on the Web. In our met...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	280,334
2008.03483	Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis	Fusing multi-modality medical images, such as MR and PET, can provide various anatomical or functional information about human body. But PET data is always unavailable due to different reasons such as cost, radiation, or other limitations. In this paper, we propose a 3D end-to-end synthesis network, called Bidirectiona...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	190,918
1505.02619	A Graph Model for Opportunistic Network Coding	Recent advancements in graph-based analysis and solutions of instantly decodable network coding (IDNC) trigger the interest to extend them to more complicated opportunistic network coding (ONC) scenarios, with limited increase in complexity. In this paper, we design a simple IDNC-like graph model for a specific subclas...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	42,989
1405.2846	Introduction to Dynamic Unary Encoding	Dynamic unary encoding takes unary encoding to the next level. Every n-bit binary string is an encoding of dynamic unary and every n-bit binary string is encodable by dynamic unary. By utilizing both forms of unary code and a single bit of parity information dynamic unary encoding partitions 2^n non-negative integers i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	33,024
1302.4701	A Receiver-Centric OFCDM Approach with Subcarrier Grouping	In this letter, following a cross-layer design concept, we propose a novel subcarrier grouping technique for Orthogonal Frequency and Code Division Multiplexing (OFCDM) multiuser systems. We adopt a two dimensional (2D) spreading, so as to achieve both frequency- and time-domain channel gain. Furthermore, we enable a r...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	22,171
2202.11987	Predicting the impact of treatments over time with uncertainty aware neural differential equations	Predicting the impact of treatments from observational data only still represents a majorchallenge despite recent significant advances in time series modeling. Treatment assignments are usually correlated with the predictors of the response, resulting in a lack of data support for counterfactual predictions and therefo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,078
1108.0155	Reasoning in the OWL 2 Full Ontology Language using First-Order Automated Theorem Proving	OWL 2 has been standardized by the World Wide Web Consortium (W3C) as a family of ontology languages for the Semantic Web. The most expressive of these languages is OWL 2 Full, but to date no reasoner has been implemented for this language. Consistency and entailment checking are known to be undecidable for OWL 2 Full....	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	11,524
2106.12499	Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis	Self-training based unsupervised domain adaptation (UDA) has shown great potential to address the problem of domain shift, when applying a trained deep learning model in a source domain to unlabeled target domains. However, while the self-training UDA has demonstrated its effectiveness on discriminative tasks, such as ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	242,748
2305.02440	Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs	Large language models (LLMs) power many state-of-the-art systems in natural language processing. However, these models are extremely computationally expensive, even at inference time, raising the natural question: when is the extra cost of deploying a larger model worth the anticipated boost in capabilities? Better und...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	362,030
2010.05780	Capturing Dynamics of Time-Varying Data via Topology	One approach to understanding complex data is to study its shape through the lens of algebraic topology. While the early development of topological data analysis focused primarily on static data, in recent years, theoretical and applied studies have turned to data that varies in time. A time-varying collection of metri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	200,258
2303.17951	FP8 versus INT8 for efficient deep learning inference	Recently, the idea of using FP8 as a number format for neural network training has been floating around the deep learning world. Given that most training is currently conducted with entire networks in FP32, or sometimes FP16 with mixed-precision, the step to having some parts of a network run in FP8 with 8-bit weights ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	355,410
1808.01739	Concentration bounds for empirical conditional value-at-risk: The unbounded case	In several real-world applications involving decision making under uncertainty, the traditional expected value objective may not be suitable, as it may be necessary to control losses in the case of a rare but extreme event. Conditional Value-at-Risk (CVaR) is a popular risk measure for modeling the aforementioned objec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	104,638
2303.18176	Affective Computing for Human-Robot Interaction Research: Four Critical Lessons for the Hitchhiker	Social Robotics and Human-Robot Interaction (HRI) research relies on different Affective Computing (AC) solutions for sensing, perceiving and understanding human affective behaviour during interactions. This may include utilising off-the-shelf affect perception models that are pre-trained on popular affect recognition ...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	355,489
1906.03609	Distilling Object Detectors with Fine-grained Feature Imitation	State-of-the-art CNN based recognition models are often computationally prohibitive to deploy on low-end devices. A promising high level approach tackling this limitation is knowledge distillation, which let small student model mimic cumbersome teacher model's output to get improved generalization. However, related met...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	134,434
2208.04273	Improving performance in multi-objective decision-making in Bottles environments with soft maximin approaches	Balancing multiple competing and conflicting objectives is an essential task for any artificial intelligence tasked with satisfying human values or preferences. Conflict arises both from misalignment between individuals with competing values, but also between conflicting value systems held by a single human. Starting w...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	312,052
2207.03960	Detection of Furigana Text in Images	Furigana are pronunciation notes used in Japanese writing. Being able to detect these can help improve optical character recognition (OCR) performance or make more accurate digital copies of Japanese written media by correctly displaying furigana. This project focuses on detecting furigana in Japanese books and comics....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	307,032
1912.12712	Haptic communication optimises joint decisions and affords implicit confidence sharing	Group decisions can outperform the choices of the best individual group members. Previous research suggested that optimal group decisions require individuals to communicate explicitly (e.g., verbally) their confidence levels. Our study addresses the untested hypothesis that implicit communication using a sensorimotor c...	true	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	158,911
2411.03740	Human-in-the-Loop Feature Selection Using Interpretable Kolmogorov-Arnold Network-based Double Deep Q-Network	Feature selection is critical for improving the performance and interpretability of machine learning models, particularly in high-dimensional spaces where complex feature interactions can reduce accuracy and increase computational demands. Existing approaches often rely on static feature subsets or manual intervention,...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,019
1909.07877	Multi-mapping Image-to-Image Translation via Learning Disentanglement	Recent advances of image-to-image translation focus on learning the one-to-many mapping from two aspects: multi-modal translation and multi-domain translation. However, the existing methods only consider one of the two perspectives, which makes them unable to solve each other's problem. To address this issue, we propos...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	145,804
2104.09382	A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks	In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic. In particular, the edge server can use the existing image dataset to train the CNN in advance, which is further fine-tuned based on the limited datasets uploaded from the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	231,219
2412.02109	Direct Coloring for Self-Supervised Enhanced Feature Decoupling	The success of self-supervised learning (SSL) has been the focus of multiple recent theoretical and empirical studies, including the role of data augmentation (in feature decoupling) as well as complete and dimensional representation collapse. While complete collapse is well-studied and addressed, dimensional collapse ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	513,384
1912.05345	Severity Detection Tool for Patients with Infectious Disease	Hand, foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low and middle income countries. Tetanus in particular has a high mortality rate and its treatment is resource-demanding. Furthermore, HFMD often affects a large number of infants and young children. As a result, its treatment consumes e...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	157,086
1804.07209	NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations	This paper introduces Non-Autonomous Input-Output Stable Network(NAIS-Net), a very deep architecture where each stacked processing block is derived from a time-invariant non-autonomous dynamical system. Non-autonomy is implemented by skip connections from the block input to each of the unrolled processing stages and al...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	95,478
1702.05528	Degrees of Freedom in Cached MIMO Relay Networks With Multiple Base Stations	The ability of physical layer relay caching to increase the degrees of freedom (DoF) of a single cell was recently illustrated. In this paper, we extend this result to the case of multiple cells in which a caching relay is shared among multiple non-cooperative base stations (BSs). In particular, we show that a large Do...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,415
2202.08132	Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients	Pruning neural networks at initialization would enable us to find sparse models that retain the accuracy of the original network while consuming fewer computational resources for training and inference. However, current methods are insufficient to enable this optimization and lead to a large degradation in model perfor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	280,773
2111.03476	A Variational U-Net for Weather Forecasting	Not only can discovering patterns and insights from atmospheric data enable more accurate weather predictions, but it may also provide valuable information to help tackle climate change. Weather4cast is an open competition that aims to evaluate machine learning algorithms' capability to predict future atmospheric state...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	265,185
1607.06996	Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction	Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vectors. It has achieved great successes in many real-world applications. However, for large-scale problems involving a huge number of samples a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	58,960
1406.4567	Two Boolean functions with five-valued Walsh spectra and high nonlinearity	For cryptographic systems the method of confusion and diffusion is used as a fundamental technique to achieve security. Confusion is reflected in nonlinearity of certain Boolean functions describing the cryptographic transformation. In this paper, we present two balanced boolean functions which have low Walsh spectra a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	33,948
1610.03612	The Analysis of Local Motion and Deformation in Image Sequences Inspired by Physical Electromagnetic Interaction	In order to analyze the moving and deforming of the objects in image sequence, a novel way is presented to analyze the local changes of object edges between two related images (such as two adjacent frames in a video sequence), which is inspired by the physical electromagnetic interaction. The changes of edge between ad...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	62,270
2107.03356	M-FAC: Efficient Matrix-Free Approximations of Second-Order Information	Efficiently approximating local curvature information of the loss function is a key tool for optimization and compression of deep neural networks. Yet, most existing methods to approximate second-order information have high computational or storage costs, which can limit their practicality. In this work, we investigate...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	245,137
2412.08114	Modeling Latent Non-Linear Dynamical System over Time Series	We study the problem of modeling a non-linear dynamical system when given a time series by deriving equations directly from the data. Despite the fact that time series data are given as input, models for dynamics and estimation algorithms that incorporate long-term temporal dependencies are largely absent from existing...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	515,948
1511.02942	ExtraPush for convex smooth decentralized optimization over directed networks	In this note, we extend the algorithms Extra and subgradient-push to a new algorithm ExtraPush for consensus optimization with convex differentiable objective functions over a directed network. When the stationary distribution of the network can be computed in advance}, we propose a simplified algorithm called Normaliz...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	48,699
1301.7408	Context-Specific Approximation in Probabilistic Inference	There is evidence that the numbers in probabilistic inference don't really matter. This paper considers the idea that we can make a probabilistic model simpler by making fewer distinctions. Unfortunately, the level of a Bayesian network seems too coarse; it is unlikely that a parent will make little difference for all ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	21,641
2411.10048	Physics-informed neural networks need a physicist to be accurate: the case of mass and heat transport in Fischer-Tropsch catalyst particles	Physics-Informed Neural Networks (PINNs) have emerged as an influential technology, merging the swift and automated capabilities of machine learning with the precision and dependability of simulations grounded in theoretical physics. PINNs are often employed to solve algebraic or differential equations to replace some ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	508,474
2311.04066	Can CLIP Help Sound Source Localization?	Large-scale pre-trained image-text models demonstrate remarkable versatility across diverse tasks, benefiting from their robust representational capabilities and effective multimodal alignment. We extend the application of these models, specifically CLIP, to the domain of sound source localization. Unlike conventional ...	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	406,089
1410.4155	Access Policy Design for Cognitive Secondary Users under a Primary Type-I HARQ Process	In this paper, an underlay cognitive radio network that consists of an arbitrary number of secondary users (SU) is considered, in which the primary user (PU) employs Type-I Hybrid Automatic Repeat Request (HARQ). Exploiting the redundancy in PU retransmissions, each SU receiver applies forward interference cancelation ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	36,771
0810.1119	Gaussian Belief Propagation for Solving Systems of Linear Equations: Theory and Application	The canonical problem of solving a system of linear equations arises in numerous contexts in information theory, communication theory, and related fields. In this contribution, we develop a solution based upon Gaussian belief propagation (GaBP) that does not involve direct matrix inversion. The iterative nature of our ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,458
2210.17190	IITD at the WANLP 2022 Shared Task: Multilingual Multi-Granularity Network for Propaganda Detection	We present our system for the two subtasks of the shared task on propaganda detection in Arabic, part of WANLP'2022. Subtask 1 is a multi-label classification problem to find the propaganda techniques used in a given tweet. Our system for this task uses XLM-R to predict probabilities for the target tweet to use each of...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	327,602
2107.03227	Scalable Data Balancing for Unlabeled Satellite Imagery	Data imbalance is a ubiquitous problem in machine learning. In large scale collected and annotated datasets, data imbalance is either mitigated manually by undersampling frequent classes and oversampling rare classes, or planned for with imputation and augmentation techniques. In both cases balancing data requires labe...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	245,097
2305.01997	Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?	Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by experts (CAMUS being one of the largest public databases). However, these models are still considered unreliable by clinicians due to unresol...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	361,872

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