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
1509.01693	The Economic Dispatch for Integrated Wind Power Systems Using Particle Swarm Optimization	The economic dispatch of wind power units is quite different from that in conventional thermal units, since the adopted model should take into consideration the intermittency nature of wind speed as well. Therefore, this paper uses a model that takes into account the aforementioned consideration in addition to whether ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	46,641
2409.07869	Learning Rules from KGs Guided by Language Models	Advances in information extraction have enabled the automatic construction of large knowledge graphs (e.g., Yago, Wikidata or Google KG), which are widely used in many applications like semantic search or data analytics. However, due to their semi-automatic construction, KGs are often incomplete. Rule learning methods,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	487,693
1604.03689	Modeling and Analysis of Cellular Networks using Stochastic Geometry: A Tutorial	This paper presents a tutorial on stochastic geometry (SG) based analysis for cellular networks. This tutorial is distinguished by its depth with respect to wireless communication details and its focus on cellular networks. The paper starts by modeling and analyzing the baseband interference in a basic cellular network...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	54,552
1908.07600	Personalizing Search Results Using Hierarchical RNN with Query-aware Attention	Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical interests, query click entropy and so on to tailor the original ranking. However, few studies have taken into account the sequential information u...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	142,322
2110.06688	DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning	Automatic font generation based on deep learning has aroused a lot of interest in the last decade. However, only a few recently-reported approaches are capable of directly generating vector glyphs and their results are still far from satisfactory. In this paper, we propose a novel method, DeepVecFont, to effectively re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	260,712
2311.07616	ReIDTracker Sea: the technical report of BoaTrack and SeaDronesSee-MOT challenge at MaCVi of WACV24	Multi-Object Tracking is one of the most important technologies in maritime computer vision. Our solution tries to explore Multi-Object Tracking in maritime Unmanned Aerial vehicles (UAVs) and Unmanned Surface Vehicles (USVs) usage scenarios. Most of the current Multi-Object Tracking algorithms require complex associat...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	407,412
2407.10049	AutoGRAMS: Autonomous Graphical Agent Modeling Software	We introduce the AutoGRAMS framework for programming multi-step interactions with language models. AutoGRAMS represents AI agents as a graph, where each node can execute either a language modeling instruction or traditional code. Likewise, transitions in the graph can be governed by either language modeling decisions o...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	472,818
2312.11735	Multiple Hypothesis Dropout: Estimating the Parameters of Multi-Modal Output Distributions	In many real-world applications, from robotics to pedestrian trajectory prediction, there is a need to predict multiple real-valued outputs to represent several potential scenarios. Current deep learning techniques to address multiple-output problems are based on two main methodologies: (1) mixture density networks, wh...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,692
2406.02163	Pairwise Ranking Loss for Multi-Task Learning in Recommender Systems	Multi-Task Learning (MTL) plays a crucial role in real-world advertising applications such as recommender systems, aiming to achieve robust representations while minimizing resource consumption. MTL endeavors to simultaneously optimize multiple tasks to construct a unified model serving diverse objectives. In online ad...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	460,644
2110.10724	Semi-supervised physics guided deep learning framework for predicting the I-V characteristics of GAN HEMT	This letter proposes a novel deep learning framework (DLF) that addresses two major hurdles in the adoption of deep learning techniques for solving physics-based problems: 1) requirement of the large dataset for training the DL model, 2) consistency of the DL model with the physics of the phenomenon. The framework is g...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	262,237
1808.08279	Nuclei Detection Using Mixture Density Networks	Nuclei detection is an important task in the histology domain as it is a main step toward further analysis such as cell counting, cell segmentation, study of cell connections, etc. This is a challenging task due to the complex texture of histology image, variation in shape, and touching cells. To tackle these hurdles, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	105,906
2012.06749	Less Is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging	End-to-end models that condition the output label sequence on all previously predicted labels have emerged as popular alternatives to conventional systems for automatic speech recognition (ASR). Since unique label histories correspond to distinct models states, such models are decoded using an approximate beam-search p...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	211,211
2109.05826	Variational Disentanglement for Domain Generalization	Domain generalization aims to learn an invariant model that can generalize well to the unseen target domain. In this paper, we propose to tackle the problem of domain generalization by delivering an effective framework named Variational Disentanglement Network (VDN), which is capable of disentangling the domain-specifi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,966
1408.3661	Overhead Performance Tradeoffs - A Resource Allocation Perspective	A key aspect of many resource allocation problems is the need for the resource controller to compute a function, such as the max or arg max, of the competing users metrics. Information must be exchanged between the competing users and the resource controller in order for this function to be computed. In many practical ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	35,396
2308.08702	Finding a Second Wind: Speeding Up Graph Traversal Queries in RDBMSs Using Column-Oriented Processing	Recursive queries and recursive derived tables constitute an important part of the SQL standard. Their efficient processing is important for many real-life applications that rely on graph or hierarchy traversal. Position-enabled column-stores offer a novel opportunity to improve run times for this type of queries. Such...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	385,993
2008.09569	Revisiting Process versus Product Metrics: a Large Scale Analysis	Numerous methods can build predictive models from software data. However, what methods and conclusions should we endorse as we move from analytics in-the-small (dealing with a handful of projects) to analytics in-the-large (dealing with hundreds of projects)? To answer this question, we recheck prior small-scale resu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	192,753
1910.00314	BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions	This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019. Research Domain Criteria (RDoC) construct is a multi-dimensional and broad framework to describe mental health disorders by combining knowledge from genomics to behaviour. Non-availability of RDoC labelled dataset ...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	147,634
2410.20035	Training the Untrainable: Introducing Inductive Bias via Representational Alignment	We demonstrate that architectures which traditionally are considered to be ill-suited for a task can be trained using inductive biases from another architecture. Networks are considered untrainable when they overfit, underfit, or converge to poor results even when tuning their hyperparameters. For example, plain fully ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	502,619
2301.08742	Unifying Consciousness and Time to Enhance Artificial Intelligence	Consciousness is a sequential process of awareness which can focus on one piece of information at a time. This process of awareness experiences causation which underpins the notion of time while it interplays with matter and energy, forming reality. The study of Consciousness, time and reality is complex and evolving f...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	341,270
2007.12858	Modal Uncertainty Estimation via Discrete Latent Representation	Many important problems in the real world don't have unique solutions. It is thus important for machine learning models to be capable of proposing different plausible solutions with meaningful probability measures. In this work we introduce such a deep learning framework that learns the one-to-many mappings between the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	188,943
1503.08395	Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition	Symmetric positive semi-definite (SPSD) matrix approximation methods have been extensively used to speed up large-scale eigenvalue computation and kernel learning methods. The standard sketch based method, which we call the prototype model, produces relatively accurate approximations, but is inefficient on large square...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	41,584
2104.06819	Short-term bus travel time prediction for transfer synchronization with intelligent uncertainty handling	This paper presents two novel approaches for uncertainty estimation adapted and extended for the multi-link bus travel time problem. The uncertainty is modeled directly as part of recurrent artificial neural networks, but using two fundamentally different approaches: one based on Deep Quantile Regression (DQR) and the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	230,210
2404.14850	Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models	Fine-tuning Pre-trained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing Parameter-Efficient Fine-Tuning technique...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	448,844
2309.09977	A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning	Communication efficiency is a major challenge in federated learning (FL). In client-server schemes, the server constitutes a bottleneck, and while decentralized setups spread communications, they do not necessarily reduce them due to slower convergence. We propose Multi-Token Coordinate Descent (MTCD), a communication-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	392,815
2302.02033	An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem	We study the convex hull membership (CHM) problem in the pure exploration setting where one aims to efficiently and accurately determine if a given point lies in the convex hull of means of a finite set of distributions. We give a complete characterization of the sample complexity of the CHM problem in the one-dimensio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	343,826
2106.14986	Multitask Learning for Scalable and Dense Multilayer Bayesian Map Inference	This article presents a novel and flexible multitask multilayer Bayesian mapping framework with readily extendable attribute layers. The proposed framework goes beyond modern metric-semantic maps to provide even richer environmental information for robots in a single mapping formalism while exploiting intralayer and in...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	243,570
2107.04512	Using Machine Translation to Localize Task Oriented NLG Output	One of the challenges in a task oriented natural language application like the Google Assistant, Siri, or Alexa is to localize the output to many languages. This paper explores doing this by applying machine translation to the English output. Using machine translation is very scalable, as it can work with any English o...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	245,481
2401.02962	Automated Localization of Blood Vessels in Retinal Images	Vessel structure is one of the most important parts of the retina which physicians can detect many diseases by analysing its features. Localization of blood vessels in retina images is an important process in medical image analysis. This process is also more challenging with the presence of bright and dark lesions. In ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	419,907
2107.09591	Hybrid neural network reduced order modelling for turbulent flows with geometric parameters	Geometrically parametrized Partial Differential Equations are nowadays widely used in many different fields as, for example, shape optimization processes or patient specific surgery studies. The focus of this work is on some advances for this topic, capable of increasing the accuracy with respect to previous approaches...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	247,070
2408.17108	Sparse Uncertainty-Informed Sampling from Federated Streaming Data	We present a numerically robust, computationally efficient approach for non-I.I.D. data stream sampling in federated client systems, where resources are limited and labeled data for local model adaptation is sparse and expensive. The proposed method identifies relevant stream observations to optimize the underlying cli...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	484,580
2012.01075	Iterative Detection and Decoding of Finite-Length Polar Codes in Gaussian Multiple Access Channels	We consider the usage of finite-length polar codes for the Gaussian multiple access channel (GMAC) with a finite number of users. Based on the interleave-division multipleaccess (IDMA) concept, we implement an iterative detection and decoding non-orthogonal multiple access (NOMA) receiver that benefits from a low compl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	209,319
2001.11115	Multichannel ALOHA with Exploration Phase	In this paper, we consider exploration for multichannel ALOHA by transmitting preambles before transmitting data packets and show that the maximum throughput can be improved by a factor of 2 - exp(-1) = 1.632, which can be seen as the gain of exploration. In the proposed approach, a base station (BS) needs to send the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	161,977
1812.04120	Deep Learning Based Joint Pilot Design and Channel Estimation for Multiuser MIMO Channels	In this paper, we propose a joint pilot design and channel estimation scheme based on the deep learning (DL) technique for multiuser multiple-input multiple output (MIMO) channels. To this end, we construct a pilot designer using two-layer neural networks (TNNs) and a channel estimator using deep neural networks (DNNs)...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	116,145
2102.12898	ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning	Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to characterise the microstructure of the nervous tissue, e.g. to delineate brain white matter connections in a non-invasive manner via fibre tracking. Magnetic Resonance Imaging (MRI) in high spatial resolution would play an important role in visualisi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	221,878
1806.06778	BinGAN: Learning Compact Binary Descriptors with a Regularized GAN	In this paper, we propose a novel regularization method for Generative Adversarial Networks, which allows the model to learn discriminative yet compact binary representations of image patches (image descriptors). We employ the dimensionality reduction that takes place in the intermediate layers of the discriminator net...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	100,762
2309.10399	Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Results	We present a novel technique to discover and exploit weak causal signals directly from images via neural networks for classification purposes. This way, we model how the presence of a feature in one part of the image affects the appearance of another feature in a different part of the image. Our method consists of a co...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	392,991
2409.17886	Upper-Body Pose-based Gaze Estimation for Privacy-Preserving 3D Gaze Target Detection	Gaze Target Detection (GTD), i.e., determining where a person is looking within a scene from an external viewpoint, is a challenging task, particularly in 3D space. Existing approaches heavily rely on analyzing the person's appearance, primarily focusing on their face to predict the gaze target. This paper presents a n...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	492,013
2309.08873	X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs	Understanding when two pieces of text convey the same information is a goal touching many subproblems in NLP, including textual entailment and fact-checking. This problem becomes more complex when those two pieces of text are in different languages. Here, we introduce X-PARADE (Cross-lingual Paragraph-level Analysis of...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	392,359
1105.2416	PAC-Bayesian Analysis of Martingales and Multiarmed Bandits	We present two alternative ways to apply PAC-Bayesian analysis to sequences of dependent random variables. The first is based on a new lemma that enables to bound expectations of convex functions of certain dependent random variables by expectations of the same functions of independent Bernoulli random variables. This ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	10,338
2207.01516	Learning state machines via efficient hashing of future traces	State machines are popular models to model and visualize discrete systems such as software systems, and to represent regular grammars. Most algorithms that passively learn state machines from data assume all the data to be available from the beginning and they load this data into memory. This makes it hard to apply the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	306,205
2406.17973	Koopman-LQR Controller for Quadrotor UAVs from Data	Quadrotor systems are common and beneficial for many fields, but their intricate behavior often makes it challenging to design effective and optimal control strategies. Some traditional approaches to nonlinear control often rely on local linearizations or complex nonlinear models, which can be inaccurate or computation...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	467,816
2401.15902	A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving	Depth completion is a crucial task in autonomous driving, aiming to convert a sparse depth map into a dense depth prediction. Due to its potentially rich semantic information, RGB image is commonly fused to enhance the completion effect. Image-guided depth completion involves three key challenges: 1) how to effectively...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	424,639
0708.0607	Real-time control and monitoring system for LIPI's Public Cluster	We have developed a monitoring and control system for LIPI's Public Cluster. The system consists of microcontrollers and full web-based user interfaces for daily operation. It is argued that, due to its special natures, the cluster requires fully dedicated and self developed control and monitoring system. We discuss th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	523
2402.12179	Examining Monitoring System: Detecting Abnormal Behavior In Online Examinations	Cheating in online exams has become a prevalent issue over the past decade, especially during the COVID-19 pandemic. To address this issue of academic dishonesty, our "Exam Monitoring System: Detecting Abnormal Behavior in Online Examinations" is designed to assist proctors in identifying unusual student behavior. Our ...	false	false	false	false	true	false	false	false	false	false	false	true	false	true	false	false	false	false	430,738
2206.03196	Improving Image Captioning with Control Signal of Sentence Quality	In the dataset of image captioning, each image is aligned with several descriptions. Despite the fact that the quality of these descriptions varies, existing captioning models treat them equally in the training process. In this paper, we propose a new control signal of sentence quality, which is taken as an additional ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	301,177
1609.04747	An overview of gradient descent optimization algorithms	Gradient descent optimization algorithms, while increasingly popular, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This article aims to provide the reader with intuitions with regard to the behaviour of different algorithms that will allow her ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	61,028
1811.11969	Traffic Danger Recognition With Surveillance Cameras Without Training Data	We propose a traffic danger recognition model that works with arbitrary traffic surveillance cameras to identify and predict car crashes. There are too many cameras to monitor manually. Therefore, we developed a model to predict and identify car crashes from surveillance cameras based on a 3D reconstruction of the road...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	114,894
2107.07557	OdoViz: A 3D Odometry Visualization and Processing Tool	OdoViz is a reactive web-based tool for 3D visualization and processing of autonomous vehicle datasets designed to support common tasks in visual place recognition research. The system includes functionality for loading, inspecting, visualizing, and processing GPS/INS poses, point clouds and camera images. It supports ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	246,455
1701.00505	Statistical inference for network samples using subgraph counts	We consider that a network is an observation, and a collection of observed networks forms a sample. In this setting, we provide methods to test whether all observations in a network sample are drawn from a specified model. We achieve this by deriving, under the null of the graphon model, the joint asymptotic properties...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	66,282
1503.02821	Multiuser Scheduling for Simultaneous Wireless Information and Power Transfer Systems	In this thesis, we study the downlink multiuser scheduling and power allocation problem for systems with simultaneous wireless information and power transfer (SWIPT). In the first part of the thesis, we focus on multiuser scheduling. We design optimal scheduling algorithms that maximize the long-term average system thr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	40,976
2412.05520	More than Marketing? On the Information Value of AI Benchmarks for Practitioners	Public AI benchmark results are widely broadcast by model developers as indicators of model quality within a growing and competitive market. However, these advertised scores do not necessarily reflect the traits of interest to those who will ultimately apply AI models. In this paper, we seek to understand if and how AI...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	514,851
2304.01052	Investigation of risk-aware MDP and POMDP contingency management autonomy for UAS	Unmanned aircraft systems (UAS) are being increasingly adopted for various applications. The risk UAS poses to people and property must be kept to acceptable levels. This paper proposes risk-aware contingency management autonomy to prevent an accident in the event of component malfunction, specifically propulsion unit ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	355,915
2005.01815	The effect of social balance on social fragmentation	With the availability of cell phones, internet, social media etc. the interconnectedness of people within most societies has increased drastically over the past three decades. Across the same timespan, we are observing the phenomenon of increasing levels of fragmentation in society into relatively small and isolated gr...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	175,680
2207.03182	Chilled Sampling for Uncertainty Quantification: A Motivation From A Meteorological Inverse Problem	Atmospheric motion vectors (AMVs) extracted from satellite imagery are the only wind observations with good global coverage. They are important features for feeding numerical weather prediction (NWP) models. Several Bayesian models have been proposed to estimate AMVs. Although critical for correct assimilation into NWP...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	306,758
2412.01718	HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving	In the past few decades, autonomous driving algorithms have made significant progress in perception, planning, and control. However, evaluating individual components does not fully reflect the performance of entire systems, highlighting the need for more holistic assessment methods. This motivates the development of HU...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	513,232
2502.11897	DLFR-VAE: Dynamic Latent Frame Rate VAE for Video Generation	In this paper, we propose the Dynamic Latent Frame Rate VAE (DLFR-VAE), a training-free paradigm that can make use of adaptive temporal compression in latent space. While existing video generative models apply fixed compression rates via pretrained VAE, we observe that real-world video content exhibits substantial temp...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	534,593
1302.4976	On the Testability of Causal Models with Latent and Instrumental Variables	Certain causal models involving unmeasured variables induce no independence constraints among the observed variables but imply, nevertheless, inequality contraints on the observed distribution. This paper derives a general formula for such instrumental variables, that is, exogenous variables that directly affect some v...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	22,250
2112.10529	Ultra-Reliable and Low-Latency Short-Packet Communications for Multihop MIMO Relaying	This work considers the multihop multiple-input multiple-output relay network under short-packet communications to facilitate not only ultra-reliability but also low-latency communications. We assume that the transmit antenna selection (TAS) scheme is utilized at the transmit side, whereas either selection combining (S...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	272,462
2412.03601	Relations between average shortest path length and another centralities in graphs	Relations between average shortest path length and average clustering coefficient, radiality, closeness and stress centralities were obtained for simple graphs.	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	514,032
2412.10623	Ares: Approximate Representations via Efficient Sparsification -- A Stateless Approach through Polynomial Homomorphism	The increasing prevalence of high-dimensional data demands efficient and scalable compression methods to support modern applications. However, existing techniques like PCA and Autoencoders often rely on auxiliary metadata or intricate architectures, limiting their practicality for streaming or infinite datasets. In thi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	517,019
2403.11032	FH-TabNet: Multi-Class Familial Hypercholesterolemia Detection via a Multi-Stage Tabular Deep Learning	Familial Hypercholesterolemia (FH) is a genetic disorder characterized by elevated levels of Low-Density Lipoprotein (LDL) cholesterol or its associated genes. Early-stage and accurate categorization of FH is of significance allowing for timely interventions to mitigate the risk of life-threatening conditions. Conventi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	438,487
2209.14915	Spiking Neural Networks for event-based action recognition: A new task to understand their advantage	Spiking Neural Networks (SNN) are characterised by their unique temporal dynamics, but the properties and advantages of such computations are still not well understood. In order to provide answers, in this work we demonstrate how Spiking neurons can enable temporal feature extraction in feed-forward neural networks wit...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	320,393
1901.10517	Reparameterizable Subset Sampling via Continuous Relaxations	Many machine learning tasks require sampling a subset of items from a collection based on a parameterized distribution. The Gumbel-softmax trick can be used to sample a single item, and allows for low-variance reparameterized gradients with respect to the parameters of the underlying distribution. However, stochastic o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,037
2002.05057	Passivity Conditions for Plug-and-Play Operation of Nonlinear Static AC Loads	The complexity arising in AC microgrids from multiple interacting distributed generation units (DGUs) with intermittent supply behavior requires local voltage-source inverters (VSIs) to be controlled in a distributed or decentralized manner at primary level. In (Strehle et al., 2019), we use passivity theory to design ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	163,776
2009.04719	Learning Behavioral Representations of Human Mobility	In this paper, we investigate the suitability of state-of-the-art representation learning methods to the analysis of behavioral similarity of moving individuals, based on CDR trajectories. The core of the contribution is a novel methodological framework, mob2vec, centered on the combined use of a recent symbolic trajec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	195,129
2108.10382	Learning Sparse Analytic Filters for Piano Transcription	In recent years, filterbank learning has become an increasingly popular strategy for various audio-related machine learning tasks. This is partly due to its ability to discover task-specific audio characteristics which can be leveraged in downstream processing. It is also a natural extension of the nearly ubiquitous de...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,878
2404.13752	Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models	Since the rapid development of Large Language Models (LLMs) has achieved remarkable success, understanding and rectifying their internal complex mechanisms has become an urgent issue. Recent research has attempted to interpret their behaviors through the lens of inner representation. However, developing practical and e...	false	false	false	false	true	false	true	false	true	false	false	false	true	false	false	false	false	false	448,427
2206.05066	Experimental Evaluation of Visual-Inertial Odometry Systems for Arable Farming	The farming industry constantly seeks the automation of different processes involved in agricultural production, such as sowing, harvesting and weed control. The use of mobile autonomous robots to perform those tasks is of great interest. Arable lands present hard challenges for Simultaneous Localization and Mapping (S...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	301,880
cmp-lg/9610005	Learning string edit distance	In many applications, it is necessary to determine the similarity of two strings. A widely-used notion of string similarity is the edit distance: the minimum number of insertions, deletions, and substitutions required to transform one string into the other. In this report, we provide a stochastic model for string edit ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,671
2403.06007	Invariant Properties of Linear-Iterative Distributed Averaging Algorithms and Application to Error Detection	We consider the problem of average consensus in a distributed system comprising a set of nodes that can exchange information among themselves. We focus on a class of algorithms for solving such a problem whereby each node maintains a state and updates it iteratively as a linear combination of the states maintained by i...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	436,250
2201.12611	Learning Stochastic Graph Neural Networks with Constrained Variance	Stochastic graph neural networks (SGNNs) are information processing architectures that learn representations from data over random graphs. SGNNs are trained with respect to the expected performance, which comes with no guarantee about deviations of particular output realizations around the optimal expectation. To overc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	277,712
2203.16626	DDNeRF: Depth Distribution Neural Radiance Fields	In recent years, the field of implicit neural representation has progressed significantly. Models such as neural radiance fields (NeRF), which uses relatively small neural networks, can represent high-quality scenes and achieve state-of-the-art results for novel view synthesis. Training these types of networks, however...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	288,852
2110.11401	Trajectory Prediction using Generative Adversarial Network in Multi-Class Scenarios	Predicting traffic agents' trajectories is an important task for auto-piloting. Most previous work on trajectory prediction only considers a single class of road agents. We use a sequence-to-sequence model to predict future paths from observed paths and we incorporate class information into the model by concatenating e...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	262,466
2112.05399	A Generative Car-following Model Conditioned On Driving Styles	Car-following (CF) modeling, an essential component in simulating human CF behaviors, has attracted increasing research interest in the past decades. This paper pushes the state of the art by proposing a novel generative hybrid CF model, which achieves high accuracy in characterizing dynamic human CF behaviors and is a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	270,833
2401.18063	AoII-Optimum Sampling of CTMC Information Sources Under Sampling Rate Constraints	We consider a sensor that samples an $N$-state continuous-time Markov chain (CTMC)-based information source process, and transmits the observed state of the source, to a remote monitor tasked with timely tracking of the source process. The mismatch between the source and monitor processes is quantified by age of incorr...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	425,416
1901.06491	Guaranteeing Recoverability via Partially Constrained Transaction Logs	Transaction logging is an essential constituent to guarantee the atomicity and durability in online transaction processing (OLTP) systems. It always has a considerable impact on performance, especially in an in-memory database system. Conventional implementations of logging rely heavily on a centralized design, which g...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	119,014
2201.05206	Reproducible, incremental representation learning with Rosetta VAE	Variational autoencoders are among the most popular methods for distilling low-dimensional structure from high-dimensional data, making them increasingly valuable as tools for data exploration and scientific discovery. However, unlike typical machine learning problems in which a single model is trained once on a single...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	275,311
1911.10527	Merging Deterministic Policy Gradient Estimations with Varied Bias-Variance Tradeoff for Effective Deep Reinforcement Learning	Deep reinforcement learning (DRL) on Markov decision processes (MDPs) with continuous action spaces is often approached by directly training parametric policies along the direction of estimated policy gradients (PGs). Previous research revealed that the performance of these PG algorithms depends heavily on the bias-var...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	154,863
2110.00621	Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing	Semantic parsing provides a way to extract the semantic structure of a text that could be understood by machines. It is utilized in various NLP applications that require text comprehension such as summarization and question answering. Graph-based representation is one of the semantic representation approaches to expres...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	258,457
2405.20620	"Forgetting" in Machine Learning and Beyond: A Survey	This survey investigates the multifaceted nature of forgetting in machine learning, drawing insights from neuroscientific research that posits forgetting as an adaptive function rather than a defect, enhancing the learning process and preventing overfitting. This survey focuses on the benefits of forgetting and its app...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	459,437
1909.13158	Accelerating the Computation of UCB and Related Indices for Reinforcement Learning	In this paper we derive an efficient method for computing the indices associated with an asymptotically optimal upper confidence bound algorithm (MDP-UCB) of Burnetas and Katehakis (1997) that only requires solving a system of two non-linear equations with two unknowns, irrespective of the cardinality of the state spac...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	147,346
1908.11472	Kinematic Single Vehicle Trajectory Prediction Baselines and Applications with the NGSIM Dataset	In the recent vehicle trajectory prediction literature, the most common baselines are briefly introduced without the necessary information to reproduce it. In this article we produce reproducible vehicle prediction results from simple models. For that purpose, the process is explicit, and the code is available. Those b...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	143,385
2411.19103	VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models	In this paper, we introduce an open-source Korean-English vision-language model (VLM), VARCO-VISION. We incorporate a step-by-step training strategy that allows a model learn both linguistic and visual information while preserving the backbone model's knowledge. Our model demonstrates outstanding performance in diverse...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	512,121
1408.1692	When do Numbers Really Matter?	Common wisdom has it that small distinctions in the probabilities quantifying a Bayesian network do not matter much for the resultsof probabilistic queries. However, one can easily develop realistic scenarios under which small variations in network probabilities can lead to significant changes in computed queries. A pe...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	35,201
2103.10625	On the Value of Preview Information For Safety Control	Incorporating predictions of external inputs, which can otherwise be treated as disturbances, has been widely studied in control and computer science communities. These predictions are commonly referred to as preview in optimal control and lookahead in temporal logic synthesis. However, little work has been done for an...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	225,516
2312.14129	WellFactor: Patient Profiling using Integrative Embedding of Healthcare Data	In the rapidly evolving healthcare industry, platforms now have access to not only traditional medical records, but also diverse data sets encompassing various patient interactions, such as those from healthcare web portals. To address this rich diversity of data, we introduce WellFactor: a method that derives patient ...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	417,507
2108.09899	Rate distortion comparison of a few gradient quantizers	This article is in the context of gradient compression. Gradient compression is a popular technique for mitigating the communication bottleneck observed when training large machine learning models in a distributed manner using gradient-based methods such as stochastic gradient descent. In this article, assuming a Gauss...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	251,738
2204.06917	Global Counterfactual Explanations: Investigations, Implementations and Improvements	Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding. However, the major shortcoming associated with these methods is their inability to provide explanations beyond the local or instance-level. While ...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	291,492
2408.01609	Fed-RD: Privacy-Preserving Federated Learning for Financial Crime Detection	We introduce Federated Learning for Relational Data (Fed-RD), a novel privacy-preserving federated learning algorithm specifically developed for financial transaction datasets partitioned vertically and horizontally across parties. Fed-RD strategically employs differential privacy and secure multiparty computation to g...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	478,298
2303.15662	ChatGPT4PCG Competition: Character-like Level Generation for Science Birds	This paper presents the first ChatGPT4PCG Competition at the 2023 IEEE Conference on Games. The objective of this competition is for participants to create effective prompts for ChatGPT--enabling it to generate Science Birds levels with high stability and character-like qualities--fully using their creativity as well a...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	354,570
2304.04333	Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection	The successful implementation of vision-based navigation in agricultural fields hinges upon two critical components: 1) the accurate identification of key components within the scene, and 2) the identification of lanes through the detection of boundary lines that separate the crops from the traversable ground. We propo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	357,184
2211.12000	ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic - English	We present our work on collecting ArzEn-ST, a code-switched Egyptian Arabic - English Speech Translation Corpus. This corpus is an extension of the ArzEn speech corpus, which was collected through informal interviews with bilingual speakers. In this work, we collect translations in both directions, monolingual Egyptian...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	331,954
2310.11117	USDC: Unified Static and Dynamic Compression for Visual Transformer	Visual Transformers have achieved great success in almost all vision tasks, such as classification, detection, and so on. However, the model complexity and the inference speed of the visual transformers hinder their deployments in industrial products. Various model compression techniques focus on directly compressing t...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	400,529
cs/0207071	A Polynomial Translation of Logic Programs with Nested Expressions into Disjunctive Logic Programs: Preliminary Report	Nested logic programs have recently been introduced in order to allow for arbitrarily nested formulas in the heads and the bodies of logic program rules under the answer sets semantics. Nested expressions can be formed using conjunction, disjunction, as well as the negation as failure operator in an unrestricted fashio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	537,662
1004.4601	Data Stream Algorithms for Codeword Testing	Motivated by applications in storage systems and property testing, we study data stream algorithms for local testing and tolerant testing of codes. Ideally, we would like to know whether there exist asymptotically good codes that can be local/tolerant tested with one-pass, poly-log space data stream algorithms. We show...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,289
2309.08423	A Simple Method for the Performance Analysis of Fluid Antenna Systems under Correlated Nakagami-$m$ Fading	By recognizing the tremendous flexibility of the emerging fluid antenna system (FAS), which allows dynamic reconfigurability of the location of the antenna within a given space, this paper investigates the performance of a single-antenna FAS over spatially correlated Nakagami-$m$ fading channels. Specifically, simple a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	392,166
2005.00050	UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection	We apply contextualised word embeddings to lexical semantic change detection in the SemEval-2020 Shared Task 1. This paper focuses on Subtask 2, ranking words by the degree of their semantic drift over time. We analyse the performance of two contextualising architectures (BERT and ELMo) and three change detection algor...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	175,104
1909.04149	A Simple Galerkin Meshless Method, the Fragile Points Method (FPM) Using Point Stiffness Matrices, for 2D Linear Elastic Problems in Complex Domains with Crack and Rupture Propagation	The Fragile Points Method (FPM) is an elementarily simple Galerkin meshless method, employing Point-based discontinuous trial and test functions only, without using element-based trial and test functions. In this study, the algorithmic formulations of FPM for linear elasticity are given in detail, by exploring the conc...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	144,711
1110.1073	Active Learning with Multiple Views	Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which is sufficient to lear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	12,499
2405.12443	FFCL: Forward-Forward Net with Cortical Loops, Training and Inference on Edge Without Backpropagation	The Forward-Forward Learning (FFL) algorithm is a recently proposed solution for training neural networks without needing memory-intensive backpropagation. During training, labels accompany input data, classifying them as positive or negative inputs. Each layer learns its response to these inputs independently. In this...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	455,522

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