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
1410.8577	An Ensemble-based System for Microaneurysm Detection and Diabetic Retinopathy Grading	Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of mi...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	37,170
2312.06927	WE economy: Potential of mutual aid distribution based on moral responsibility and risk vulnerability	Reducing wealth inequality and disparity is a global challenge. The economic system is mainly divided into (1) gift and reciprocity, (2) power and redistribution, (3) market exchange, and (4) mutual aid without reciprocal obligations. The current inequality stems from a capitalist economy consisting of (2) and (3). To ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	414,725
2207.06985	ObjectBox: From Centers to Boxes for Anchor-Free Object Detection	We present ObjectBox, a novel single-stage anchor-free and highly generalizable object detection approach. As opposed to both existing anchor-based and anchor-free detectors, which are more biased toward specific object scales in their label assignments, we use only object center locations as positive samples and treat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,068
0911.4510	Bigraphical models for protein and membrane interactions	We present a bigraphical framework suited for modeling biological systems both at protein level and at membrane level. We characterize formally bigraphs corresponding to biologically meaningful systems, and bigraphic rewriting rules representing biologically admissible interactions. At the protein level, these bigraphi...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	5,004
2208.10379	Enhanced IoT Batteryless D2D Communications Using Reconfigurable Intelligent Surfaces	Recent research on reconfigurable intelligent surfaces (RIS) suggests that the RIS panel, containing passive elements, enhances channel performance for the internet of things (IoT) systems by reflecting transmitted signals to the receiving nodes. This paper investigates RIS panel assisted-wireless network to instigate ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	314,041
2408.00337	DistillGrasp: Integrating Features Correlation with Knowledge Distillation for Depth Completion of Transparent Objects	Due to the visual properties of reflection and refraction, RGB-D cameras cannot accurately capture the depth of transparent objects, leading to incomplete depth maps. To fill in the missing points, recent studies tend to explore new visual features and design complex networks to reconstruct the depth, however, these ap...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	477,799
2010.03161	Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control	We consider model-free reinforcement learning (RL) in non-stationary Markov decision processes. Both the reward functions and the state transition functions are allowed to vary arbitrarily over time as long as their cumulative variations do not exceed certain variation budgets. We propose Restarted Q-Learning with Uppe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	199,304
2103.14357	VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation	Domain adaptation aims to leverage a label-rich domain (the source domain) to help model learning in a label-scarce domain (the target domain). Most domain adaptation methods require the co-existence of source and target domain samples to reduce the distribution mismatch, however, access to the source domain samples ma...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	226,833
1910.06948	Data-Driven Deep Learning of Partial Differential Equations in Modal Space	We present a framework for recovering/approximating unknown time-dependent partial differential equation (PDE) using its solution data. Instead of identifying the terms in the underlying PDE, we seek to approximate the evolution operator of the underlying PDE numerically. The evolution operator of the PDE, defined in i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	149,485
2207.14200	CrAM: A Compression-Aware Minimizer	Deep neural networks (DNNs) often have to be compressed, via pruning and/or quantization, before they can be deployed in practical settings. In this work we propose a new compression-aware minimizer dubbed CrAM that modifies the optimization step in a principled way, in order to produce models whose local loss behavior...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	310,509
1708.06394	Expressions for the Entropy of Binomial-Type Distributions	We develop a general method for computing logarithmic and log-gamma expectations of distributions. As a result, we derive series expansions and integral representations of the entropy for several fundamental distributions, including the Poisson, binomial, beta-binomial, negative binomial, and hypergeometric distributio...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	79,311
1008.2005	Approximation Analysis of Influence Spread in Social Networks	In the context of influence propagation in a social graph, we can identify three orthogonal dimensions - the number of seed nodes activated at the beginning (known as budget), the expected number of activated nodes at the end of the propagation (known as expected spread or coverage), and the time taken for the propagat...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	7,253
1304.3856	Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence (1988)	This is the Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence, which was held in Minneapolis, MN, July 10-12, 1988	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,946
1705.02894	Geometric GAN	Generative Adversarial Nets (GANs) represent an important milestone for effective generative models, which has inspired numerous variants seemingly different from each other. One of the main contributions of this paper is to reveal a unified geometric structure in GAN and its variants. Specifically, we show that the ad...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	73,079
2109.06050	Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training	The goal of stance detection is to determine the viewpoint expressed in a piece of text towards a target. These viewpoints or contexts are often expressed in many different languages depending on the user and the platform, which can be a local news outlet, a social media platform, a news forum, etc. Most research in st...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	255,027
1804.04371	Image Correction via Deep Reciprocating HDR Transformation	Image correction aims to adjust an input image into a visually pleasing one. Existing approaches are proposed mainly from the perspective of image pixel manipulation. They are not effective to recover the details in the under/over exposed regions. In this paper, we revisit the image formation procedure and notice that ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	94,835
1306.4621	English Character Recognition using Artificial Neural Network	This work focuses on development of a Offline Hand Written English Character Recognition algorithm based on Artificial Neural Network (ANN). The ANN implemented in this work has single output neuron which shows whether the tested character belongs to a particular cluster or not. The implementation is carried out comple...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	25,322
1208.4662	Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection	We have developed an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells inspired by a multi-resolution community detection (MCD) based network segmentation method. The image processing problem is framed as identifying segments with respective average FLTs against a bac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	18,226
2311.01734	Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training	Contrastive learning has emerged as a promising paradigm for 3D open-world understanding, i.e., aligning point cloud representation to image and text embedding space individually. In this paper, we introduce MixCon3D, a simple yet effective method aiming to sculpt holistic 3D representation in contrastive language-imag...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	405,155
1907.06740	Real-time Hair Segmentation and Recoloring on Mobile GPUs	We present a novel approach for neural network-based hair segmentation from a single camera input specifically designed for real-time, mobile application. Our relatively small neural network produces a high-quality hair segmentation mask that is well suited for AR effects, e.g. virtual hair recoloring. The proposed mod...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	138,691
2307.07869	Custom DNN using Reward Modulated Inverted STDP Learning for Temporal Pattern Recognition	Temporal spike recognition plays a crucial role in various domains, including anomaly detection, keyword spotting and neuroscience. This paper presents a novel algorithm for efficient temporal spike pattern recognition on sparse event series data. The algorithm leverages a combination of reward-modulatory behavior, Heb...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	379,570
1703.02883	Memory Enriched Big Bang Big Crunch Optimization Algorithm for Data Clustering	Cluster analysis plays an important role in decision making process for many knowledge-based systems. There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models, and traditional hierarchical algorithms. In this paper, a novel heuristic approac...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	69,634
2308.04223	Real-Time Progressive Learning: Accumulate Knowledge from Control with Neural-Network-Based Selective Memory	Memory, as the basis of learning, determines the storage, update and forgetting of knowledge and further determines the efficiency of learning. Featured with the mechanism of memory, a radial basis function neural network based learning control scheme named real-time progressive learning (RTPL) is proposed to learn the...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	384,333
2410.03676	A quest through interconnected datasets: lessons from highly-cited ICASSP papers	As audio machine learning outcomes are deployed in societally impactful applications, it is important to have a sense of the quality and origins of the data used. Noticing that being explicit about this sense is not trivially rewarded in academic publishing in applied machine learning domains, and neither is included i...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	494,897
2006.06997	Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval	Despite the widespread use of gradient-based algorithms for optimizing high-dimensional non-convex functions, understanding their ability of finding good minima instead of being trapped in spurious ones remains to a large extent an open problem. Here we focus on gradient flow dynamics for phase retrieval from random me...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,644
1809.02393	Improving Neural Question Generation using Answer Separation	Neural question generation (NQG) is the task of generating a question from a given passage with deep neural networks. Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the generation of unintended questions. In this pape...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false	false	107,042
2501.00830	LLM+AL: Bridging Large Language Models and Action Languages for Complex Reasoning about Actions	Large Language Models (LLMs) have made significant strides in various intelligent tasks but still struggle with complex action reasoning tasks that require systematic search. To address this limitation, we propose a method that bridges the natural language understanding capabilities of LLMs with the symbolic reasoning ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	521,817
2110.14503	Simple data balancing achieves competitive worst-group-accuracy	We study the problem of learning classifiers that perform well across (known or unknown) groups of data. After observing that common worst-group-accuracy datasets suffer from substantial imbalances, we set out to compare state-of-the-art methods to simple balancing of classes and groups by either subsampling or reweigh...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	263,552
2008.08316	Data-Independent Structured Pruning of Neural Networks via Coresets	Model compression is crucial for deployment of neural networks on devices with limited computational and memory resources. Many different methods show comparable accuracy of the compressed model and similar compression rates. However, the majority of the compression methods are based on heuristics and offer no worst-ca...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	192,386
2312.04879	HC-Ref: Hierarchical Constrained Refinement for Robust Adversarial Training of GNNs	Recent studies have shown that attackers can catastrophically reduce the performance of GNNs by maliciously modifying the graph structure or node features on the graph. Adversarial training, which has been shown to be one of the most effective defense mechanisms against adversarial attacks in computer vision, holds gre...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	413,870
2207.14145	A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections	Pedestrian safety has become an important research topic among various studies due to the increased number of pedestrian-involved crashes. To evaluate pedestrian safety proactively, surrogate safety measures (SSMs) have been widely used in traffic conflict-based studies as they do not require historical crashes as inpu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	310,495
2007.11975	Online Boosting with Bandit Feedback	We consider the problem of online boosting for regression tasks, when only limited information is available to the learner. We give an efficient regret minimization method that has two implications: an online boosting algorithm with noisy multi-point bandit feedback, and a new projection-free online convex optimization...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	188,691
2001.00474	The Algebraic Theory of Fractional Jumps	In this paper we start by briefly surveying the theory of Fractional Jumps and transitive projective maps. Then, we give an efficient construction of a fractional jump of a projective map and we extend the compound generator construction for the Inversive Congruential Generator to Fractional jump sequences. In addition...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	159,224
2012.13779	Towards sample-efficient episodic control with DAC-ML	The sample-inefficiency problem in Artificial Intelligence refers to the inability of current Deep Reinforcement Learning models to optimize action policies within a small number of episodes. Recent studies have tried to overcome this limitation by adding memory systems and architectural biases to improve learning spee...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	213,328
1509.03870	The USFD Spoken Language Translation System for IWSLT 2014	The University of Sheffield (USFD) participated in the International Workshop for Spoken Language Translation (IWSLT) in 2014. In this paper, we will introduce the USFD SLT system for IWSLT. Automatic speech recognition (ASR) is achieved by two multi-pass deep neural network systems with adaptation and rescoring techni...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	46,870
1807.01069	Adversarial Robustness Toolbox v1.0.0	Adversarial Robustness Toolbox (ART) is a Python library supporting developers and researchers in defending Machine Learning models (Deep Neural Networks, Gradient Boosted Decision Trees, Support Vector Machines, Random Forests, Logistic Regression, Gaussian Processes, Decision Trees, Scikit-learn Pipelines, etc.) agai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	101,982
2111.11952	Leveraging Selective Prediction for Reliable Image Geolocation	Reliable image geolocation is crucial for several applications, ranging from social media geo-tagging to fake news detection. State-of-the-art geolocation methods surpass human performance on the task of geolocation estimation from images. However, no method assesses the suitability of an image for this task, which res...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	267,825
2206.00381	The statistical nature of h-index of a network node	Evaluating the importance of a network node is a crucial task in network science and graph data mining. H-index is a popular centrality measure for this task, however, there is still a lack of its interpretation from a rigorous statistical aspect. Here we show the statistical nature of h-index from the perspective of o...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	300,102
1802.03043	PoTrojan: powerful neural-level trojan designs in deep learning models	With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life. Neural network or artificial neural network (NN), the main technique behind DL, has been extensively studied to facilitate computer vision and natural language recognition. However, the more we rely on ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	89,882
2105.09679	Improved Neuronal Ensemble Inference with Generative Model and MCMC	Neuronal ensemble inference is a significant problem in the study of biological neural networks. Various methods have been proposed for ensemble inference from experimental data of neuronal activity. Among them, Bayesian inference approach with generative model was proposed recently. However, this method requires large...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	236,139
2105.13191	Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective	Compressive sensing (CS) is a mathematically elegant tool for reducing the sampling rate, potentially bringing context-awareness to a wider range of devices. Nevertheless, practical issues with the sampling and reconstruction algorithms prevent further proliferation of CS in real world domains, especially among heterog...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	237,230
0911.1386	Machine Learning: When and Where the Horses Went Astray?	Machine Learning is usually defined as a subfield of AI, which is busy with information extraction from raw data sets. Despite of its common acceptance and widespread recognition, this definition is wrong and groundless. Meaningful information does not belong to the data that bear it. It belongs to the observers of the...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	4,889
2411.09896	Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision	The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is dif...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	508,410
1604.04154	Robust Control Framework for Time-Varying Power-Sharing among Distributed Energy Resources	One of the most important challenges facing an electric grid is to incorporate renewables and distributed energy resources (DERs) to the grid. Because of the associated uncertainties in power generations and peak power demands, opportunities for improving the functioning and reliability of the grid lie in the design of...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	54,604
2210.17367	Analysis and Detection of Singing Techniques in Repertoires of J-POP Solo Singers	In this paper, we focus on singing techniques within the scope of music information retrieval research. We investigate how singers use singing techniques using real-world recordings of famous solo singers in Japanese popular music songs (J-POP). First, we built a new dataset of singing techniques. The dataset consists ...	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	327,660
2401.18085	Motion Guidance: Diffusion-Based Image Editing with Differentiable Motion Estimators	Diffusion models are capable of generating impressive images conditioned on text descriptions, and extensions of these models allow users to edit images at a relatively coarse scale. However, the ability to precisely edit the layout, position, pose, and shape of objects in images with diffusion models is still difficul...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	425,426
2412.16691	From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations	This research presents a three-step causal inference framework that integrates correlation analysis, machine learning-based causality discovery, and LLM-driven interpretations to identify socioeconomic factors influencing carbon emissions and contributing to climate change. The approach begins with identifying correlat...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	519,637
2109.02791	Safety-Critical Learning of Robot Control with Temporal Logic Specifications	Reinforcement learning (RL) is a promising approach. However, success is limited to real-world applications, because ensuring safe exploration and facilitating adequate exploitation is a challenge for controlling robotic systems with unknown models and measurement uncertainties. The learning problem becomes even more d...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	true	253,856
2107.02447	Complete weight enumerators for several classes of two-weight and three-weight linear codes	In this paper, for an odd prime $p$, by extending Li et al.'s construction \cite{CL2016}, several classes of two-weight and three-weight linear codes over the finite field $\mathbb{F}_p$ are constructed from a defining set, and then their complete weight enumerators are determined by using Weil sums. Furthermore, we sh...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	244,826
2501.13624	QMamba: Post-Training Quantization for Vision State Space Models	State Space Models (SSMs), as key components of Mamaba, have gained increasing attention for vision models recently, thanks to their efficient long sequence modeling capability. Given the computational cost of deploying SSMs on resource-limited edge devices, Post-Training Quantization (PTQ) is a technique with the pote...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	526,761
1808.00525	The impact of imbalanced training data on machine learning for author name disambiguation	In supervised machine learning for author name disambiguation, negative training data are often dominantly larger than positive training data. This paper examines how the ratios of negative to positive training data can affect the performance of machine learning algorithms to disambiguate author names in bibliographic ...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	true	104,405
1905.13125	Seeker: Real-Time Interactive Search	This paper introduces Seeker, a system that allows users to interactively refine search rankings in real time, through feedback in the form of likes and dislikes. When searching online, users may not know how to accurately describe their product of choice in words. An alternative approach is to search an embedding spac...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	132,994
2402.00065	A technical note for the 91-clauses SAT resolution with Indirect QAOA based approach	This paper addresses the resolution of the 3-SAT problem using a QAOA-like approach. The chosen principle involves modeling the solution ranks of the 3-SAT problem, which, in this particular case, directly represent a solution. This results in a highly compact circuit with few gates, enabling the modeling of large-size...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	425,466
1910.03515	Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Development	Artificial intelligence (AI) holds great promise to empower us with knowledge and augment our effectiveness. We can -- and must -- ensure that we keep humans safe and in control, particularly with regard to government and public sector applications that affect broad populations. How can AI development teams harness the...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	148,515
2412.15211	Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variation	Reconstructing the geometry and appearance of objects from photographs taken in different environments is difficult as the illumination and therefore the object appearance vary across captured images. This is particularly challenging for more specular objects whose appearance strongly depends on the viewing direction. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	518,985
2412.12626	Improving the Transferability of 3D Point Cloud Attack via Spectral-aware Admix and Optimization Designs	Deep learning models for point clouds have shown to be vulnerable to adversarial attacks, which have received increasing attention in various safety-critical applications such as autonomous driving, robotics, and surveillance. Existing 3D attackers generally design various attack strategies in the white-box setting, re...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	517,953
2209.09318	Guarding a Non-Maneuverable Translating Line with an Attached Defender	In this paper we consider a target-guarding differential game where the defender must protect a linearly translating line-segment by intercepting an attacker who tries to reach it. In contrast to common target-guarding problems, we assume that the defender is attached to the target and moves along with it. This assumpt...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	318,457
1608.06754	Resource Allocation in Dynamic TDD Heterogeneous Networks under Mixed Traffic	Recently, Dynamic Time Division Duplex (TDD) has been proposed to handle the asymmetry of traffic demand between DownLink (DL) and UpLink (UL) in Heterogeneous Networks (HetNets). However, for mixed traffic consisting of best effort traffic and soft Quality of Service (QoS) traffic, the resource allocation problem has ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	60,155
1805.04246	Convex Programming Based Spectral Clustering	Clustering is a fundamental task in data analysis, and spectral clustering has been recognized as a promising approach to it. Given a graph describing the relationship between data, spectral clustering explores the underlying cluster structure in two stages. The first stage embeds the nodes of the graph in real space, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	97,204
2501.19353	Do Large Multimodal Models Solve Caption Generation for Scientific Figures? Lessons Learned from SciCap Challenge 2023	Since the SciCap datasets launch in 2021, the research community has made significant progress in generating captions for scientific figures in scholarly articles. In 2023, the first SciCap Challenge took place, inviting global teams to use an expanded SciCap dataset to develop models for captioning diverse figure type...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	529,129
2306.15321	Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition	Graph convolutional networks have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the similarity of inter-class data. Moreover, the noisy data from pose extraction increases the challenge of fine-grained recognition. In this work,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	375,982
1908.09470	Local Graph Stability in Exponential Family Random Graph Models	Exponential family Random Graph Models (ERGMs) can be viewed as expressing a probability distribution on graphs arising from the action of competing social forces that make ties more or less likely, depending on the state of the rest of the graph. Such forces often lead to a complex pattern of dependence among edges, w...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	142,857
2204.09710	Complete identification of complex salt geometries from inaccurate migrated subsurface offset gathers using deep learning	Delimiting salt inclusions from migrated images is a time-consuming activity that relies on highly human-curated analysis and is subject to interpretation errors or limitations of the methods available. We propose to use migrated images produced from an inaccurate velocity model (with a reasonable approximation of sedi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	292,527
1901.07702	Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems	We cast visual retrieval as a regression problem by posing triplet loss as a regression loss. This enables epistemic uncertainty estimation using dropout as a Bayesian approximation framework in retrieval. Accordingly, Monte Carlo (MC) sampling is leveraged to boost retrieval performance. Our approach is evaluated on t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	119,277
2106.04372	Segmentation and ABCD rule extraction for skin tumors classification	During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer. In this work, we present an automated diagnosis system based on the ABCD ru...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	239,691
1905.04368	Digital Passport: A Novel Technological Strategy for Intellectual Property Protection of Convolutional Neural Networks	In order to prevent deep neural networks from being infringed by unauthorized parties, we propose a generic solution which embeds a designated digital passport into a network, and subsequently, either paralyzes the network functionalities for unauthorized usages or maintain its functionalities in the presence of a veri...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	130,439
2204.02969	Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)	In the current Industrial 4.0 revolution, Prognostics and Health Management (PHM) is an emerging field of research. The difficulty of obtaining data from electromechanical systems in an industrial setting increases proportionally with the scale and accessibility of the automated industry, resulting in a less interpolat...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	290,144
2307.12134	Modality Confidence Aware Training for Robust End-to-End Spoken Language Understanding	End-to-end (E2E) spoken language understanding (SLU) systems that generate a semantic parse from speech have become more promising recently. This approach uses a single model that utilizes audio and text representations from pre-trained speech recognition models (ASR), and outperforms traditional pipeline SLU systems i...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	381,148
2406.02559	ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer	Shadow-affected images often exhibit pronounced spatial discrepancies in color and illumination, consequently degrading various vision applications including object detection and segmentation systems. To effectively eliminate shadows in real-world images while preserving intricate details and producing visually compell...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	460,828
2308.07123	An Outlook into the Future of Egocentric Vision	What will the future be? We wonder! In this survey, we explore the gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with outward facing cameras and digital overlays, is expected to be integrated in our every day lives. To understand this gap, the article start...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	385,398
2001.09876	The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings	We introduce POLAR - a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials. Semantic differentials are a psychometric construct for measuring the semantics of a word by analysing its position on a scale between two polar opposites (e.g., cold -- hot, soft -- ha...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	161,686
2303.06550	Spatial Correspondence between Graph Neural Network-Segmented Images	Graph neural networks (GNNs) have been proposed for medical image segmentation, by predicting anatomical structures represented by graphs of vertices and edges. One such type of graph is predefined with fixed size and connectivity to represent a reference of anatomical regions of interest, thus known as templates. This...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	350,891
2206.11072	AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment	How to quickly and automatically mine effective information and serve investment decisions has attracted more and more attention from academia and industry. And new challenges have arisen with the global pandemic. This paper proposes a two-phase AlphaMLDigger that effectively finds excessive returns in a highly fluctua...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	304,144
2006.09655	Fairness-Oriented Semi-Chaotic Genetic Algorithm-Based Channel Assignment Technique for Nodes Starvation Problem in Wireless Mesh Network	Multi-Radio Multi-Channel Wireless Mesh Networks (WMNs) have emerged as a scalable, reliable, and agile wireless network that supports many types of innovative technologies such as the Internet of Things (IoT) and vehicular networks. Due to the limited number of orthogonal channels, interference between channels advers...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	182,615
2211.16750	Score-based Continuous-time Discrete Diffusion Models	Score-based modeling through stochastic differential equations (SDEs) has provided a new perspective on diffusion models, and demonstrated superior performance on continuous data. However, the gradient of the log-likelihood function, i.e., the score function, is not properly defined for discrete spaces. This makes it n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	333,735
1908.07422	Human Gait Symmetry Assessment using a Depth Camera and Mirrors	This paper proposes a reliable approach for human gait symmetry assessment using a depth camera and two mirrors. The input of our system is a sequence of 3D point clouds which are formed from a setup including a Time-of-Flight (ToF) depth camera and two mirrors. A cylindrical histogram is estimated for describing the p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	142,290
2009.02111	A nested genetic algorithm strategy for the optimal plastic design of frames	An innovative strategy for the optimal design of planar frames able to resist to seismic excitations is here proposed. The procedure is based on genetic algorithms (GA) which are performed according to a nested structure suitable to be implemented in parallel computing on several devices. In particular, this solution f...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	194,469
2208.12104	Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields	Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate data can be highly expensive. Here, machine learning (ML) models can help to be data economic as they can be successfully constrained using the underlying symmetry and conservation laws of physics. However, so far, every desc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	314,626
1902.04202	De-identification without losing faces	Training of deep learning models for computer vision requires large image or video datasets from real world. Often, in collecting such datasets, we need to protect the privacy of the people captured in the images or videos, while still preserve the useful attributes such as facial expressions. In this work, we describe...	false	false	false	false	false	false	true	false	false	false	false	true	false	true	false	false	false	false	121,284
2011.07035	Continual Learning with Deep Artificial Neurons	Neurons in real brains are enormously complex computational units. Among other things, they're responsible for transforming inbound electro-chemical vectors into outbound action potentials, updating the strengths of intermediate synapses, regulating their own internal states, and modulating the behavior of other nearby...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	206,422
2304.00983	Modelling Maritime SAR Effective Sweep Widths for Helicopters in VDM	Search and Rescue (SAR) is searching for and providing help to people in danger. In the UK, SAR teams are typically charities with limited resources, and SAR missions are time critical. Search managers need to objectively decide which search assets (e.g. helicopter vs drone) would be better. A key metric in the SAR com...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	355,884
1604.07625	Mutual Transformation of Information and Knowledge	Information and knowledge are transformable into each other. Information transformation into knowledge by the example of rule generation from OWL (Web Ontology Language) ontology has been shown during the development of the SWES (Semantic Web Expert System). The SWES is expected as an expert system for searching OWL on...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	55,114
1512.04376	Limits on the Capacity of In-Band Full Duplex Communication in Uplink Cellular Networks	Simultaneous co-channel transmission and reception, denoted as in-band full duplex (FD) communication, has been promoted as an attractive solution to improve the spectral efficiency of cellular networks. However, in addition to the self-interference problem, cross-mode interference (i.e., between uplink and downlink) i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	50,126
2005.14553	Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation	We address the problem of semantic nighttime image segmentation and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreover, we design a new evaluation framework to address the substantial uncertainty of semantics in nighttime images. Our central contributions...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	179,300
2001.09386	Generating Representative Headlines for News Stories	Millions of news articles are published online every day, which can be overwhelming for readers to follow. Grouping articles that are reporting the same event into news stories is a common way of assisting readers in their news consumption. However, it remains a challenging research problem to efficiently and effective...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	161,554
1901.09557	Out-of-Sample Testing for GANs	We propose a new method to evaluate GANs, namely EvalGAN. EvalGAN relies on a test set to directly measure the reconstruction quality in the original sample space (no auxiliary networks are necessary), and it also computes the (log)likelihood for the reconstructed samples in the test set. Further, EvalGAN is agnostic t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,783
2404.13880	Regional Style and Color Transfer	This paper presents a novel contribution to the field of regional style transfer. Existing methods often suffer from the drawback of applying style homogeneously across the entire image, leading to stylistic inconsistencies or foreground object twisted when applied to image with foreground elements such as person figur...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	448,484
2501.15273	Into the Void: Mapping the Unseen Gaps in High Dimensional Data	We present a comprehensive pipeline, augmented by a visual analytics system named ``GapMiner'', that is aimed at exploring and exploiting untapped opportunities within the empty areas of high-dimensional datasets. Our approach begins with an initial dataset and then uses a novel Empty Space Search Algorithm (ESA) to id...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	527,480
1412.6127	Multi-User Diversity with Optimal Power Allocation in Spectrum Sharing under Average Interference Power Constraint	In this paper, we investigate the performance of multi-user diversity (MUD) with optimal power allocation (OPA) in spectrum sharing (SS) under average interference power (AIP) constraint. In particular, OPA through average transmit power constraint in conjunction with the AIP constraint is assumed to maximize the ergod...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	38,575
1706.02766	Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results	In this report, we suggest nine test problems for multi-task multi-objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously. The relationship between tasks varies between different test problems, which would be helpful to have a comprehensive ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	75,028
1812.10071	Coupled Recurrent Network (CRN)	Many semantic video analysis tasks can benefit from multiple, heterogenous signals. For example, in addition to the original RGB input sequences, sequences of optical flow are usually used to boost the performance of human action recognition in videos. To learn from these heterogenous input sources, existing methods re...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	117,286
2010.09323	Multi-view Subspace Clustering Networks with Local and Global Graph Information	This study investigates the problem of multi-view subspace clustering, the goal of which is to explore the underlying grouping structure of data collected from different fields or measurements. Since data do not always comply with the linear subspace models in many real-world applications, most existing multi-view subs...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	201,495
1903.10713	Multiscale CNN based Deep Metric Learning for Bioacoustic Classification: Overcoming Training Data Scarcity Using Dynamic Triplet Loss	This paper proposes multiscale convolutional neural network (CNN)-based deep metric learning for bioacoustic classification, under low training data conditions. The proposed CNN is characterized by the utilization of four different filter sizes at each level to analyze input feature maps. This multiscale nature helps i...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	125,349
2206.01207	RACA: Relation-Aware Credit Assignment for Ad-Hoc Cooperation in Multi-Agent Deep Reinforcement Learning	In recent years, reinforcement learning has faced several challenges in the multi-agent domain, such as the credit assignment issue. Value function factorization emerges as a promising way to handle the credit assignment issue under the centralized training with decentralized execution (CTDE) paradigm. However, existin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	300,388
1804.06992	Infrared and Visible Image Fusion using a Deep Learning Framework	In recent years, deep learning has become a very active research tool which is used in many image processing fields. In this paper, we propose an effective image fusion method using a deep learning framework to generate a single image which contains all the features from infrared and visible images. First, the source i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	95,424
0902.0392	Tree Exploration for Bayesian RL Exploration	Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, where optimality improves with increased computational time. This is because the resulting planning task takes the form of a dynamic programmi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	3,098
2502.01776	Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity	Diffusion Transformers (DiTs) dominate video generation but their high computational cost severely limits real-world applicability, usually requiring tens of minutes to generate a few seconds of video even on high-performance GPUs. This inefficiency primarily arises from the quadratic computational complexity of 3D Ful...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	530,026
1711.09265	Predictive Learning: Using Future Representation Learning Variantial Autoencoder for Human Action Prediction	The unsupervised Pretraining method has been widely used in aiding human action recognition. However, existing methods focus on reconstructing the already present frames rather than generating frames which happen in future.In this paper, We propose an improved Variantial Autoencoder model to extract the features with a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,366
2306.14169	A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial Diversity	The recent 'Mpox' outbreak, formerly known as 'Monkeypox', has become a significant public health concern and has spread to over 110 countries globally. The challenge of clinically diagnosing mpox early on is due, in part, to its similarity to other types of rashes. Computer-aided screening tools have been proven valua...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	375,579
1610.05426	Critical Transitions in Public Opinion: A Case Study of American Presidential Election	At the tipping point, it is known that small incident can trigger dramatic societal shift. Getting early-warning signals for such changes are valuable to avoid detrimental outcomes such as riots or collapses of nations. However, it is notoriously hard to capture the processes of such transitions in the real-world. Here...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	62,508

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