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
1909.06030	Evaluating and Boosting Uncertainty Quantification in Classification	Emergence of artificial intelligence techniques in biomedical applications urges the researchers to pay more attention on the uncertainty quantification (UQ) in machine-assisted medical decision making. For classification tasks, prior studies on UQ are difficult to compare with each other, due to the lack of a unified ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	145,271
2007.05278	Product age based demand forecast model for fashion retail	Fashion retailers require accurate demand forecasts for the next season, almost a year in advance, for demand management and supply chain planning purposes. Accurate forecasts are important to ensure retailers' profitability and to reduce environmental damage caused by disposal of unsold inventory. It is challenging be...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,626
2407.17687	A Crowding Distance That Provably Solves the Difficulties of the NSGA-II in Many-Objective Optimization	Recent theoretical works have shown that the NSGA-II can have enormous difficulties to solve problems with more than two objectives. In contrast, algorithms like the NSGA-III or SMS-EMOA, differing from the NSGA-II only in the secondary selection criterion, provably perform well in these situations. To remedy this sh...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	476,072
1812.08196	RankGAN: A Maximum Margin Ranking GAN for Generating Faces	We present a new stage-wise learning paradigm for training generative adversarial networks (GANs). The goal of our work is to progressively strengthen the discriminator and thus, the generators, with each subsequent stage without changing the network architecture. We call this proposed method the RankGAN. We first prop...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	116,953
2107.07977	An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling	The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a cornerstone of biological age-research. However, Machine Learning models underlying the field do not consider uncertainty, thereby confounding r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	246,584
1609.09037	Control of Charging of Electric Vehicles through Menu-Based Pricing	We propose an online pricing mechanism for electric vehicle (EV) charging. A charging station decides prices for each arriving EV depending on the energy and the time within which the EV will be served (i.e. deadline). The user selects either one of the contracts by paying the prescribed price or rejects all depending ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	61,665
2206.05922	From Perception to Programs: Regularize, Overparameterize, and Amortize	Toward combining inductive reasoning with perception abilities, we develop techniques for neurosymbolic program synthesis where perceptual input is first parsed by neural nets into a low-dimensional interpretable representation, which is then processed by a synthesized program. We explore several techniques for relaxin...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	302,186
2210.15304	Explaining the Explainers in Graph Neural Networks: a Comparative Study	Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process. GNN explainers have started to emerge in recent years, with a multitude of meth...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	326,898
2206.10665	BOSS: A Benchmark for Human Belief Prediction in Object-context Scenarios	Humans with an average level of social cognition can infer the beliefs of others based solely on the nonverbal communication signals (e.g. gaze, gesture, pose and contextual information) exhibited during social interactions. This social cognitive ability to predict human beliefs and intentions is more important than ev...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	303,988
2402.13787	Fairness Rising from the Ranks: HITS and PageRank on Homophilic Networks	In this paper, we investigate the conditions under which link analysis algorithms prevent minority groups from reaching high ranking slots. We find that the most common link-based algorithms using centrality metrics, such as PageRank and HITS, can reproduce and even amplify bias against minority groups in networks. Yet...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	431,413
2404.18262	Generating Situated Reflection Triggers about Alternative Solution Paths: A Case Study of Generative AI for Computer-Supported Collaborative Learning	An advantage of Large Language Models (LLMs) is their contextualization capability - providing different responses based on student inputs like solution strategy or prior discussion, to potentially better engage students than standard feedback. We present a design and evaluation of a proof-of-concept LLM application to...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	450,195
2008.11451	Determinantal Point Process as an alternative to NMS	We present a determinantal point process (DPP) inspired alternative to non-maximum suppression (NMS) which has become an integral step in all state-of-the-art object detection frameworks. DPPs have been shown to encourage diversity in subset selection problems. We pose NMS as a subset selection problem and posit that d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	193,283
2006.00057	Simulation Framework for Mobile Robots in Planetary-Like Environments	In this paper we present a simulation framework for the evaluation of the navigation and localization metrological performances of a robotic platform. The simulator, based on ROS (Robot Operating System) Gazebo, is targeted to a planetary-like research vehicle which allows to test various perception and navigation appr...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	179,352
2410.13842	D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement	We introduce D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD). FDR transf...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	499,722
2402.12284	Refining Minimax Regret for Unsupervised Environment Design	In unsupervised environment design, reinforcement learning agents are trained on environment configurations (levels) generated by an adversary that maximises some objective. Regret is a commonly used objective that theoretically results in a minimax regret (MMR) policy with desirable robustness guarantees; in particula...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	430,789
2106.01481	Quantifying language changes surrounding mental health on Twitter	Mental health challenges are thought to afflict around 10% of the global population each year, with many going untreated due to stigma and limited access to services. Here, we explore trends in words and phrases related to mental health through a collection of 1- , 2-, and 3-grams parsed from a data stream of roughly 1...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	238,506
1505.02799	Sampling of stochastic operators	We develop sampling methodology aimed at determining stochastic operators that satisfy a support size restriction on the autocorrelation of the operators stochastic spreading function. The data that we use to reconstruct the operator (or, in some cases only the autocorrelation of the spreading function) is based on the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	43,001
2410.18580	Spatial-Temporal Search for Spiking Neural Networks	Spiking Neural Networks (SNNs) are considered as a potential candidate for the next generation of artificial intelligence with appealing characteristics such as sparse computation and inherent temporal dynamics. By adopting architectures of Artificial Neural Networks (ANNs), SNNs achieve competitive performances on ben...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	501,946
1902.10697	Integrated analysis of the urban water-electricity demand nexus in the Midwestern United States	Considering the interdependencies between water and electricity use is critical for ensuring conservation measures are successful in lowering the net water and electricity use in a city. This water-electricity demand nexus will become even more important as cities continue to grow, causing water and electricity utiliti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	122,744
2012.08419	Detecting Invisible People	Monocular object detection and tracking have improved drastically in recent years, but rely on a key assumption: that objects are visible to the camera. Many offline tracking approaches reason about occluded objects post-hoc, by linking together tracklets after the object re-appears, making use of reidentification (ReI...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,760
2310.04561	DragD3D: Realistic Mesh Editing with Rigidity Control Driven by 2D Diffusion Priors	Direct mesh editing and deformation are key components in the geometric modeling and animation pipeline. Mesh editing methods are typically framed as optimization problems combining user-specified vertex constraints with a regularizer that determines the position of the rest of the vertices. The choice of the regulariz...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	397,710
2003.00874	Weakly-supervised Object Localization for Few-shot Learning and Fine-grained Few-shot Learning	Few-shot learning (FSL) aims to learn novel visual categories from very few samples, which is a challenging problem in real-world applications. Many methods of few-shot classification work well on general images to learn global representation. However, they can not deal with fine-grained categories well at the same tim...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	166,472
2102.12923	Machine Learning-Based Optimal Mesh Generation in Computational Fluid Dynamics	Computational Fluid Dynamics (CFD) is a major sub-field of engineering. Corresponding flow simulations are typically characterized by heavy computational resource requirements. Often, very fine and complex meshes are required to resolve physical effects in an appropriate manner. Since all CFD algorithms scale at least ...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	221,888
2402.19470	Towards Generalizable Tumor Synthesis	Tumor synthesis enables the creation of artificial tumors in medical images, facilitating the training of AI models for tumor detection and segmentation. However, success in tumor synthesis hinges on creating visually realistic tumors that are generalizable across multiple organs and, furthermore, the resulting AI mode...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,816
2002.11905	Globally optimal consensus maximization for robust visual inertial localization in point and line map	Map based visual inertial localization is a crucial step to reduce the drift in state estimation of mobile robots. The underlying problem for localization is to estimate the pose from a set of 3D-2D feature correspondences, of which the main challenge is the presence of outliers, especially in changing environment. In ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	165,873
1810.04244	Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning	Teams of autonomous unmanned aircraft can be used to monitor wildfires, enabling firefighters to make informed decisions. However, controlling multiple autonomous fixed-wing aircraft to maximize forest fire coverage is a complex problem. The state space is high dimensional, the fire propagates stochastically, the senso...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	109,996
2501.12502	Sequence Spreading-Based Semantic Communication Under High RF Interference	In the evolving landscape of wireless communications, semantic communication (SemCom) has recently emerged as a 6G enabler that prioritizes the transmission of meaning and contextual relevance over conventional bit-centric metrics. However, the deployment of SemCom systems in industrial settings presents considerable c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	526,338
1405.0042	Learning with incremental iterative regularization	Within a statistical learning setting, we propose and study an iterative regularization algorithm for least squares defined by an incremental gradient method. In particular, we show that, if all other parameters are fixed a priori, the number of passes over the data (epochs) acts as a regularization parameter, and prov...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	32,733
2310.19548	Approximation Theory, Computing, and Deep Learning on the Wasserstein Space	The challenge of approximating functions in infinite-dimensional spaces from finite samples is widely regarded as formidable. We delve into the challenging problem of the numerical approximation of Sobolev-smooth functions defined on probability spaces. Our particular focus centers on the Wasserstein distance function,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	404,032
1909.00900	Metric Learning for Adversarial Robustness	Deep networks are well-known to be fragile to adversarial attacks. We conduct an empirical analysis of deep representations under the state-of-the-art attack method called PGD, and find that the attack causes the internal representation to shift closer to the "false" class. Motivated by this observation, we propose to ...	false	false	false	false	false	true	true	false	false	false	false	true	true	false	false	false	false	false	143,748
2108.05053	Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems	The industrial machine learning pipeline requires iterating on model features, training and deploying models, and monitoring deployed models at scale. Feature stores were developed to manage and standardize the engineer's workflow in this end-to-end pipeline, focusing on traditional tabular feature data. In recent year...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	250,195
2006.05624	Adjoined Networks: A Training Paradigm with Applications to Network Compression	Compressing deep neural networks while maintaining accuracy is important when we want to deploy large, powerful models in production and/or edge devices. One common technique used to achieve this goal is knowledge distillation. Typically, the output of a static pre-defined teacher (a large base network) is used as soft...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	181,144
2310.19060	TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding	Large-scale video-language pre-training has made remarkable strides in advancing video-language understanding tasks. However, the heavy computational burden of video encoding remains a formidable efficiency bottleneck, particularly for long-form videos. These videos contain massive visual tokens due to their inherent 3...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	403,832
2201.04543	Eigenvalue Distribution of Large Random Matrices Arising in Deep Neural Networks: Orthogonal Case	The paper deals with the distribution of singular values of the input-output Jacobian of deep untrained neural networks in the limit of their infinite width. The Jacobian is the product of random matrices where the independent rectangular weight matrices alternate with diagonal matrices whose entries depend on the corr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	275,128
2008.11376	Causal Adversarial Network for Learning Conditional and Interventional Distributions	We propose a generative Causal Adversarial Network (CAN) for learning and sampling from conditional and interventional distributions. In contrast to the existing CausalGAN which requires the causal graph to be given, our proposed framework learns the causal relations from the data and generates samples accordingly. The...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	193,259
2210.08251	Improving Your Graph Neural Networks: A High-Frequency Booster	Graph neural networks (GNNs) hold the promise of learning efficient representations of graph-structured data, and one of its most important applications is semi-supervised node classification. However, in this application, GNN frameworks tend to fail due to the following issues: over-smoothing and heterophily. The most...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	324,060
2401.11008	Helmholtz-Decomposition and Optical Flow: A new method to characterize GCamP recordings	During deep sleep and under anaesthesia spontaneous patterns of cortical activation frequently take the form of slow travelling waves. Slow wave sleep is an important cognitive state especially because of its relevance for memory consolidation. However, despite extensive research the exact mechanisms are still ill-unde...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	422,845
2403.18100	Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning	This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the network, the collected data is sent to the cloud for statistical analysis, prediction, and data ana...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	441,759
1903.10684	Probabilistic Load Forecasting via Point Forecast Feature Integration	Short-term load forecasting is a critical element of power systems energy management systems. In recent years, probabilistic load forecasting (PLF) has gained increased attention for its ability to provide uncertainty information that helps to improve the reliability and economics of system operation performances. This...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	125,341
2405.20333	SurgiTrack: Fine-Grained Multi-Class Multi-Tool Tracking in Surgical Videos	Accurate tool tracking is essential for the success of computer-assisted intervention. Previous efforts often modeled tool trajectories rigidly, overlooking the dynamic nature of surgical procedures, especially tracking scenarios like out-of-body and out-of-camera views. Addressing this limitation, the new CholecTrack2...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	459,297
1903.03008	Performance study of distributed Apriori-like frequent itemsets mining	In this article, we focus on distributed Apriori-based frequent itemsets mining. We present a new distributed approach which takes into account inherent characteristics of this algorithm. We study the distribution aspect of this algorithm and give a comparison of the proposed approach with a classical Apriori-like dist...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	123,618
1810.04320	Least Squares Normalized Cross Correlation	Direct methods are widely used for alignment of models to images, due to their accuracy, since they minimize errors in the domain of measurement noise. They have leveraged least squares minimizations, for simple, efficient, variational optimization, since the seminal 1981 work of Lucas & Kanade, and normalized cross co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	110,017
2012.08697	Two-Stage Copy-Move Forgery Detection with Self Deep Matching and Proposal SuperGlue	Copy-move forgery detection identifies a tampered image by detecting pasted and source regions in the same image. In this paper, we propose a novel two-stage framework specially for copy-move forgery detection. The first stage is a backbone self deep matching network, and the second stage is named as Proposal SuperGlue...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,837
2109.04565	TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems	Modern vehicles have multiple electronic control units (ECUs) that are connected together as part of a complex distributed cyber-physical system (CPS). The ever-increasing communication between ECUs and external electronic systems has made these vehicles particularly susceptible to a variety of cyber-attacks. In this w...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	254,444
2112.02039	Bridging the Gap: Point Clouds for Merging Neurons in Connectomics	In the field of Connectomics, a primary problem is that of 3D neuron segmentation. Although deep learning-based methods have achieved remarkable accuracy, errors still exist, especially in regions with image defects. One common type of defect is that of consecutive missing image sections. Here, data is lost along some ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	269,708
1909.09902	Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture	This paper presents a new neural architecture that combines a modulated Hebbian network (MOHN) with DQN, which we call modulated Hebbian plus Q network architecture (MOHQA). The hypothesis is that such a combination allows MOHQA to solve difficult partially observable Markov decision process (POMDP) problems which impa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,389
2207.03025	Enhancing a Student Productivity Model for Adaptive Problem-Solving Assistance	Research on intelligent tutoring systems has been exploring data-driven methods to deliver effective adaptive assistance. While much work has been done to provide adaptive assistance when students seek help, they may not seek help optimally. This had led to the growing interest in proactive adaptive assistance, where t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	306,691
1804.00576	Cooperative Localization in Visible Light Networks: Theoretical Limits and Distributed Algorithms	Light emitting diode (LED) based visible light positioning (VLP) networks can provide accurate location information in indoor environments. In this manuscript, we propose to employ cooperative localization for visible light networks by designing a VLP system configuration that involves multiple LED transmitters with kn...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	94,071
2404.17930	Multi-Stream Cellular Test-Time Adaptation of Real-Time Models Evolving in Dynamic Environments	In the era of the Internet of Things (IoT), objects connect through a dynamic network, empowered by technologies like 5G, enabling real-time data sharing. However, smart objects, notably autonomous vehicles, face challenges in critical local computations due to limited resources. Lightweight AI models offer a solution ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	450,062
2409.14411	Scaling Diffusion Policy in Transformer to 1 Billion Parameters for Robotic Manipulation	Diffusion Policy is a powerful technique tool for learning end-to-end visuomotor robot control. It is expected that Diffusion Policy possesses scalability, a key attribute for deep neural networks, typically suggesting that increasing model size would lead to enhanced performance. However, our observations indicate tha...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	490,457
1906.02429	Occluded Face Recognition Using Low-rank Regression with Generalized Gradient Direction	In this paper, a very effective method to solve the contiguous face occlusion recognition problem is proposed. It utilizes the robust image gradient direction features together with a variety of mapping functions and adopts a hierarchical sparse and low-rank regression model. This model unites the sparse representation...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	134,055
2012.03173	Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification	Deep AUC Maximization (DAM) is a new paradigm for learning a deep neural network by maximizing the AUC score of the model on a dataset. Most previous works of AUC maximization focus on the perspective of optimization by designing efficient stochastic algorithms, and studies on generalization performance of large-scale ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	210,017
2404.18669	Bootstrap 3D Reconstructed Scenes from 3D Gaussian Splatting	Recent developments in neural rendering techniques have greatly enhanced the rendering of photo-realistic 3D scenes across both academic and commercial fields. The latest method, known as 3D Gaussian Splatting (3D-GS), has set new benchmarks for rendering quality and speed. Nevertheless, the limitations of 3D-GS become...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	450,348
1902.06728	Jacobi Sums and Correlations of Sidelnikov Sequences	We consider the problem of determining the cross-correlation values of the sequences in the families comprised of constant multiples of $M$-ary Sidelnikov sequences over $\mathbb{F}_q$, where $q$ is a power of an odd prime $p$. We show that the cross-correlation values of pairs of sequences from such a family can be ex...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	121,826
2212.09271	Very Large Language Model as a Unified Methodology of Text Mining	Text data mining is the process of deriving essential information from language text. Typical text mining tasks include text categorization, text clustering, topic modeling, information extraction, and text summarization. Various data sets are collected and various algorithms are designed for the different types of tas...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	337,054
1802.01482	The Sea Exploration Problem: Data-driven Orienteering on a Continuous Surface	This paper describes a problem arising in sea exploration, where the aim is to schedule the expedition of a ship for collecting information about the resources on the seafloor. The aim is to collect data by probing on a set of carefully chosen locations, so that the information available is optimally enriched. This pro...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	89,613
1612.09087	Efficient isogeometric thin shell formulations for soft biological materials	This paper presents three different constitutive approaches to model thin rotation-free shells based on the Kirchhoff-Love hypothesis. One approach is based on numerical integration through the shell thickness while the other two approaches do not need any numerical integration and so they are computationally more effi...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	66,150
2108.02423	Automatic Rail Component Detection Based on AttnConv-Net	The automatic detection of major rail components using railway images is beneficial to ensure the rail transport safety. In this paper, we propose an attention-powered deep convolutional network (AttnConv-net) to detect multiple rail components including the rail, clips, and bolts. The proposed method consists of a dee...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	249,321
1411.0591	Bayesian feature selection with strongly-regularizing priors maps to the Ising Model	Identifying small subsets of features that are relevant for prediction and/or classification tasks is a central problem in machine learning and statistics. The feature selection task is especially important, and computationally difficult, for modern datasets where the number of features can be comparable to, or even ex...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	37,265
2202.10571	Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks	In the deep learning era, long video generation of high-quality still remains challenging due to the spatio-temporal complexity and continuity of videos. Existing prior works have attempted to model video distribution by representing videos as 3D grids of RGB values, which impedes the scale of generated videos and negl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	281,570
2203.13501	Cooperative Path-following Control of Remotely Operated Underwater Robots for Human Visual Inspection Task	Remotely operated vehicles (ROVs) have drawn much attention to underwater tasks, such as the inspection and maintenance of infrastructure. The workload of ROV operators tends to be high, even for the skilled ones. Therefore, assistance methods for the operators are desired. This study focuses on a task in which a human...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	287,654
2310.09795	AFLOW: Developing Adversarial Examples under Extremely Noise-limited Settings	Extensive studies have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial attacks. Despite the significant progress in the attack success rate that has been made recently, the adversarial noise generated by most of the existing attack methods is still too conspicuous to the human eyes and prove...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	399,953
2407.19133	Network-Based Epidemic Control Through Optimal Travel and Quarantine Management	Motivated by the swift global transmission of infectious diseases, we present a comprehensive framework for network-based epidemic control. Our aim is to curb epidemics using two different approaches. In the first approach, we introduce an optimization strategy that optimally reduces travel rates. We analyze the conver...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	476,657
2011.01518	ShaneRun System Description to VoxCeleb Speaker Recognition Challenge 2020	In this report, we describe the submission of ShaneRun's team to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020. We use ResNet-34 as encoder to extract the speaker embeddings, which is referenced from the open-source voxceleb-trainer. We also provide a simple method to implement optimum fusion using t-SNE nor...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	204,615
2301.12876	Guiding Online Reinforcement Learning with Action-Free Offline Pretraining	Offline RL methods have been shown to reduce the need for environment interaction by training agents using offline collected episodes. However, these methods typically require action information to be logged during data collection, which can be difficult or even impossible in some practical cases. In this paper, we inv...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	342,703
2203.09619	The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detection	Anomaly detection in process mining focuses on identifying anomalous cases or events in process executions. The resulting diagnostics are used to provide measures to prevent fraudulent behavior, as well as to derive recommendations for improving process compliance and security. Most existing techniques focus on detecti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	286,214
1507.01030	Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey	We survey incremental methods for minimizing a sum $\sum_{i=1}^mf_i(x)$ consisting of a large number of convex component functions $f_i$. Our methods consist of iterations applied to single components, and have proved very effective in practice. We introduce a unified algorithmic framework for a variety of such methods...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	44,813
2407.16252	LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation	Legal Large Language Models (LLMs) have shown promise in providing legal consultations to non-experts. However, most existing Chinese legal consultation models are based on single-agent systems, which differ from real-world legal consultations, where multiple professionals collaborate to offer more tailored responses. ...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	475,526
2202.10841	Cyber-Physical Risk Assessment for False Data Injection Attacks Considering Moving Target Defences	In this paper, we examine the factors that influence the success of false data injection (FDI) attacks in the context of both cyber and physical styles of reinforcement. Many works consider the FDI attack in the context of the ability to change a measurement in a static system only. However, successful attacks will req...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	281,685
2209.09195	Constrained Sampling for Class-Agnostic Weakly Supervised Object Localization	Self-supervised vision transformers can generate accurate localization maps of the objects in an image. However, since they decompose the scene into multiple maps containing various objects, and they do not rely on any explicit supervisory signal, they cannot distinguish between the object of interest from other object...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	318,420
2305.18423	On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences	We consider the class of noisy multi-layered sigmoid recurrent neural networks with $w$ (unbounded) weights for classification of sequences of length $T$, where independent noise distributed according to $\mathcal{N}(0,\sigma^2)$ is added to the output of each neuron in the network. Our main result shows that the sampl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	369,040
1411.4464	Fully Convolutional Neural Networks for Crowd Segmentation	In this paper, we propose a fast fully convolutional neural network (FCNN) for crowd segmentation. By replacing the fully connected layers in CNN with 1 by 1 convolution kernels, FCNN takes whole images as inputs and directly outputs segmentation maps by one pass of forward propagation. It has the property of translati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	37,641
2412.08563	Physics Based Differentiable Rendering for Inverse Problems and Beyond	Physics-based differentiable rendering (PBDR) has become an efficient method in computer vision, graphics, and machine learning for addressing an array of inverse problems. PBDR allows patterns to be generated from perceptions which can be applied to enhance object attributes like geometry, substances, and lighting by ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	516,142
2303.11184	Conversation Modeling to Predict Derailment	Conversations among online users sometimes derail, i.e., break down into personal attacks. Such derailment has a negative impact on the healthy growth of cyberspace communities. The ability to predict whether ongoing conversations are likely to derail could provide valuable real-time insight to interlocutors and modera...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	352,744
2411.18513	Enhancing weed detection performance by means of GenAI-based image augmentation	Precise weed management is essential for sustaining crop productivity and ecological balance. Traditional herbicide applications face economic and environmental challenges, emphasizing the need for intelligent weed control systems powered by deep learning. These systems require vast amounts of high-quality training dat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	511,897
2109.13333	Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients	In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstrations. This is achieved by building a differentiable data-driven simulator on top of perception outputs and high-fidelity HD maps of the area. I...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	257,594
1707.06607	Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments	The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots). Two divergent approaches, e.g. centralized and decentralized, are surveyed and analyzed. Decentralized planner - MAPP is described and applied to the task of finding trajectories for d...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	77,454
2007.05923	Shortened linear codes from APN and PN functions	Linear codes generated by component functions of perfect nonlinear (PN) and almost perfect nonlinear (APN) functions and the first-order Reed-Muller codes have been an object of intensive study in coding theory. The objective of this paper is to investigate some binary shortened codes of two families of linear codes fr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	186,833
2406.09761	Towards Full Integration of Artificial Intelligence in Colon Capsule Endoscopy's Pathway	Despite recent surge of interest in deploying colon capsule endoscopy (CCE) for early diagnosis of colorectal diseases, there remains a large gap between the current state of CCE in clinical practice, and the state of its counterpart optical colonoscopy (OC). Our study is aimed at closing this gap, by focusing on the f...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	464,071
2209.14272	Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results	Humor is a substantial element of human social behavior, affect, and cognition. Its automatic understanding can facilitate a more naturalistic human-AI interaction. Current methods of humor detection have been exclusively based on staged data, making them inadequate for "real-world" applications. We contribute to addre...	false	false	true	false	false	false	true	false	true	false	false	true	false	false	false	false	false	true	320,197
1808.05850	Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling $(1+\lambda)$ EA Variants on OneMax and LeadingOnes	Theoretical and empirical research on evolutionary computation methods complement each other by providing two fundamentally different approaches towards a better understanding of black-box optimization heuristics. In discrete optimization, both streams developed rather independently of each other, but we observe today ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	105,423
2309.15431	Local Compressed Video Stream Learning for Generic Event Boundary Detection	Generic event boundary detection aims to localize the generic, taxonomy-free event boundaries that segment videos into chunks. Existing methods typically require video frames to be decoded before feeding into the network, which contains significant spatio-temporal redundancy and demands considerable computational power...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	394,966
1107.3522	What Trends in Chinese Social Media	There has been a tremendous rise in the growth of online social networks all over the world in recent times. While some networks like Twitter and Facebook have been well documented, the popular Chinese microblogging social network Sina Weibo has not been studied. In this work, we examine the key topics that trend on Si...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	11,344
1908.11230	Defeating Misclassification Attacks Against Transfer Learning	Transfer learning is prevalent as a technique to efficiently generate new models (Student models) based on the knowledge transferred from a pre-trained model (Teacher model). However, Teacher models are often publicly available for sharing and reuse, which inevitably introduces vulnerability to trigger severe attacks a...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	143,326
2010.11985	MTAG: Modal-Temporal Attention Graph for Unaligned Human Multimodal Language Sequences	Human communication is multimodal in nature; it is through multiple modalities such as language, voice, and facial expressions, that opinions and emotions are expressed. Data in this domain exhibits complex multi-relational and temporal interactions. Learning from this data is a fundamentally challenging research probl...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	true	202,503
2309.11841	Semi-Supervised Variational Inference over Nonlinear Channels	Deep learning methods for communications over unknown nonlinear channels have attracted considerable interest recently. In this paper, we consider semi-supervised learning methods, which are based on variational inference, for decoding unknown nonlinear channels. These methods, which include Monte Carlo expectation max...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	393,554
1910.14124	Bayesian causal inference via probabilistic program synthesis	Causal inference can be formalized as Bayesian inference that combines a prior distribution over causal models and likelihoods that account for both observations and interventions. We show that it is possible to implement this approach using a sufficiently expressive probabilistic programming language. Priors are repre...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	151,563
2209.11739	Adversarial Catoptric Light: An Effective, Stealthy and Robust Physical-World Attack to DNNs	Deep neural networks (DNNs) have demonstrated exceptional success across various tasks, underscoring the need to evaluate the robustness of advanced DNNs. However, traditional methods using stickers as physical perturbations to deceive classifiers present challenges in achieving stealthiness and suffer from printing lo...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	319,278
2110.03623	From Contraction Theory to Fixed Point Algorithms on Riemannian and Non-Euclidean Spaces	The design of fixed point algorithms is at the heart of monotone operator theory, convex analysis, and of many modern optimization problems arising in machine learning and control. This tutorial reviews recent advances in understanding the relationship between Demidovich conditions, one-sided Lipschitz conditions, and ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	259,568
2006.04082	End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera	Inter-vehicle distance and relative velocity estimations are two basic functions for any ADAS (Advanced driver-assistance systems). In this paper, we propose a monocular camera-based inter-vehicle distance and relative velocity estimation method based on end-to-end training of a deep neural network. The key novelty of ...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	180,553
2402.08172	A Projection-Based Time-Segmented Reduced Order Model for Fluid-Structure Interactions	In this paper, a type of novel projection-based, time-segmented reduced order model (ROM) is proposed for dynamic fluid-structure interaction (FSI) problems based upon the arbitrary Lagrangian--Eulerian (ALE)-finite element method (FEM) in a monolithic frame, where spatially, each variable is separated from others in t...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	428,988
2211.11534	Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection	The robustness of recommender systems under node injection attacks has garnered significant attention. Recently, GraphRfi, a GNN-based recommender system, was proposed and shown to effectively mitigate the impact of injected fake users. However, we demonstrate that GraphRfi remains vulnerable to attacks due to the supe...	false	false	false	false	true	true	true	false	false	false	false	false	true	false	false	false	false	false	331,770
2107.05166	Stateful Detection of Model Extraction Attacks	Machine-Learning-as-a-Service providers expose machine learning (ML) models through application programming interfaces (APIs) to developers. Recent work has shown that attackers can exploit these APIs to extract good approximations of such ML models, by querying them with samples of their choosing. We propose VarDetect...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	245,690
1904.00825	Simple unity among the fundamental equations of science	The Price equation describes the change in populations. Change concerns some value, such as biological fitness, information or physical work. The Price equation reveals universal aspects for the nature of change, independently of the meaning ascribed to values. By understanding those universal aspects, we can see more ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	125,995
2203.06673	FlexBlock: A Flexible DNN Training Accelerator with Multi-Mode Block Floating Point Support	Training deep neural networks (DNNs) is a computationally expensive job, which can take weeks or months even with high performance GPUs. As a remedy for this challenge, community has started exploring the use of more efficient data representations in the training process, e.g., block floating point (BFP). However, prio...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	285,195
2410.14136	Coded Water-Filling for Multi-User Interference Cancellation	In this paper, we study the system-level advantages provided by rateless coding, early termination and power allocation strategy for multiple users distributed across multiple cells. In a multi-cell scenario, the early termination of coded transmission not only reduces finite-length loss akin to the single-user scenari...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	499,883
2402.03893	Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiency	Predicting the movement of other road users is beneficial for improving automated vehicle (AV) performance. However, the relationship between the time horizon associated with these predictions and AV performance remains unclear. Despite the existence of numerous trajectory prediction algorithms, no studies have been co...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	427,236
1507.04540	Learning to classify with possible sensor failures	In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set. The goal is to minimize the generalization error of the classifier on non-corrupted measurements while controlling th...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	45,186
1305.0513	Limiting the Neighborhood: De-Small-World Network for Outbreak Prevention	In this work, we study a basic and practically important strategy to help prevent and/or delay an outbreak in the context of network: limiting the contact between individuals. In this paper, we introduce the average neighborhood size as a new measure for the degree of being small-world and utilize it to formally define...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	24,354
2201.05771	KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics	We present an expanded version of our previously released Kazakh text-to-speech (KazakhTTS) synthesis corpus. In the new KazakhTTS2 corpus, the overall size has increased from 93 hours to 271 hours, the number of speakers has risen from two to five (three females and two males), and the topic coverage has been diversif...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	275,494

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