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
2210.14896	DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models	With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language. However, generating images with desired details requires proper prompts, and it is often unclear how a model reacts to different prompts or what the best prompts are. To help researchers tac...	true	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	326,724
2310.09827	VFLAIR: A Research Library and Benchmark for Vertical Federated Learning	Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that allows participants with different features of the same group of users to accomplish cooperative training without exposing their raw data or model parameters. VFL has gained significant attention for its research potential and real-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	399,964
2402.14874	Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation	We propose a straightforward approach called Distillation Contrastive Decoding (DCD) to enhance the reasoning capabilities of Large Language Models (LLMs) during inference. In contrast to previous approaches that relied on smaller amateur models or analysis of hidden state differences, DCD employs Contrastive Chain-of-...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	431,891
2010.10295	Fisheye lens distortion correction	A new distortion correction algorithm for fisheye lens with equidistant mapping function is considered in the present study. The algorithm is much more data lossless and accurate than such a classical approach like Brown-Conrady model	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	201,847
2412.05850	Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agents	Text-to-SQL task aims to automatically yield SQL queries according to user text questions. To address this problem, we propose a Cooperative SQL Generation framework based on Multi-functional Agents (CSMA) through information interaction among large language model (LLM) based agents who own part of the database schema ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	515,000
1907.09438	Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks	Lane detection plays an important role in a self-driving vehicle. Several studies leverage a semantic segmentation network to extract robust lane features, but few of them can distinguish different types of lanes. In this paper, we focus on the problem of multi-class lane semantic segmentation. Based on the observation...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	139,356
2412.12486	Boosting Long-Context Management via Query-Guided Activation Refilling	Processing long contexts poses a significant challenge for large language models (LLMs) due to their inherent context-window limitations and the computational burden of extensive key-value (KV) activations, which severely impact efficiency. For information-seeking tasks, full context perception is often unnecessary, as...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	517,886
2201.01190	Two-level Graph Neural Network	Graph Neural Networks (GNNs) are recently proposed neural network structures for the processing of graph-structured data. Due to their employed neighbor aggregation strategy, existing GNNs focus on capturing node-level information and neglect high-level information. Existing GNNs therefore suffer from representational ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	274,176
1802.00168	Deep Neural Nets with Interpolating Function as Output Activation	We replace the output layer of deep neural nets, typically the softmax function, by a novel interpolating function. And we propose end-to-end training and testing algorithms for this new architecture. Compared to classical neural nets with softmax function as output activation, the surrogate with interpolating function...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	89,361
2105.07730	The State of Infodemic on Twitter	Following the wave of misinterpreted, manipulated and malicious information growing on the Internet, the misinformation surrounding COVID-19 has become a paramount issue. In the context of the current COVID-19 pandemic, social media posts and platforms are at risk of rumors and misinformation in the face of the serious...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	235,537
2210.11019	Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer	Image super-resolution reconstruction is an important task in the field of image processing technology, which can restore low resolution image to high quality image with high resolution. In recent years, deep learning has been applied in the field of image super-resolution reconstruction. With the continuous developmen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	325,150
2011.08333	2D+3D Facial Expression Recognition via Discriminative Dynamic Range Enhancement and Multi-Scale Learning	In 2D+3D facial expression recognition (FER), existing methods generate multi-view geometry maps to enhance the depth feature representation. However, this may introduce false estimations due to local plane fitting from incomplete point clouds. In this paper, we propose a novel Map Generation technique from the viewpoi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	206,835
1812.04072	PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image	This paper proposes a deep neural architecture, PlaneRCNN, that detects and reconstructs piecewise planar surfaces from a single RGB image. PlaneRCNN employs a variant of Mask R-CNN to detect planes with their plane parameters and segmentation masks. PlaneRCNN then jointly refines all the segmentation masks with a nove...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	116,132
2412.06717	Toward Non-Invasive Diagnosis of Bankart Lesions with Deep Learning	Bankart lesions, or anterior-inferior glenoid labral tears, are diagnostically challenging on standard MRIs due to their subtle imaging features-often necessitating invasive MRI arthrograms (MRAs). This study develops deep learning (DL) models to detect Bankart lesions on both standard MRIs and MRAs, aiming to improve ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	515,346
2406.18464	Bayesian inverse Navier-Stokes problems: joint flow field reconstruction and parameter learning	We formulate and solve a Bayesian inverse Navier-Stokes (N-S) problem that assimilates velocimetry data in order to jointly reconstruct a 3D flow field and learn the unknown N-S parameters, including the boundary position. By hardwiring a generalised N-S problem, and regularising its unknown parameters using Gaussian p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	468,020
1712.06021	Bendable Cuboid Robot Path Planning with Collision Avoidance using Generalized $L_p$ Norms	Optimal path planning problems for rigid and deformable (bendable) cuboid robots are considered by providing an analytic safety constraint using generalized $L_p$ norms. For regular cuboid robots, level sets of weighted $L_p$ norms generate implicit approximations of their surfaces. For bendable cuboid robots a weighte...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	86,815
2104.02214	Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques	Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for improved thermal comfort. Reducing the associated energy consumption while mainta...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	228,632
2109.05687	Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning	Recent pretrained language models extend from millions to billions of parameters. Thus the need to fine-tune an extremely large pretrained model with a limited training corpus arises in various downstream tasks. In this paper, we propose a straightforward yet effective fine-tuning technique, Child-Tuning, which updates...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	254,896
1810.09155	A Simple Baseline Algorithm for Graph Classification	Graph classification has recently received a lot of attention from various fields of machine learning e.g. kernel methods, sequential modeling or graph embedding. All these approaches offer promising results with different respective strengths and weaknesses. However, most of them rely on complex mathematics and requir...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	111,002
2011.11263	Evaluating Input Representation for Language Identification in Hindi-English Code Mixed Text	Natural language processing (NLP) techniques have become mainstream in the recent decade. Most of these advances are attributed to the processing of a single language. More recently, with the extensive growth of social media platforms focus has shifted to code-mixed text. The code-mixed text comprises text written in m...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	207,780
2006.11558	Seq2Seq and Joint Learning Based Unix Command Line Prediction System	Despite being an open-source operating system pioneered in the early 90s, UNIX based platforms have not been able to garner an overwhelming reception from amateur end users. One of the rationales for under popularity of UNIX based systems is the steep learning curve corresponding to them due to extensive use of command...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	183,283
2101.09634	Chance-Constrained Covariance Steering in a Gaussian Random Field via Successive Convex Programming	The problem of optimizing affine feedback laws that explicitly steer the mean and covariance of an uncertain system state in the presence of a Gaussian random field is considered. Spatially-dependent disturbances are successively approximated with respect to a nominal trajectory by a sequence of jointly Gaussian random...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	216,658
2304.09246	Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning	The proper enforcement of motorcycle helmet regulations is crucial for ensuring the safety of motorbike passengers and riders, as roadway cyclists and passengers are not likely to abide by these regulations if no proper enforcement systems are instituted. This paper presents the development and evaluation of a real-tim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	358,987
2402.11842	CodeArt: Better Code Models by Attention Regularization When Symbols Are Lacking	Transformer based code models have impressive performance in many software engineering tasks. However, their effectiveness degrades when symbols are missing or not informative. The reason is that the model may not learn to pay attention to the right correlations/contexts without the help of symbols. We propose a new me...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	430,601
1101.3220	Decision-Feedback Differential Detection in Impulse-Radio Ultra-Wideband Systems	In this paper we present decision-feedback differential detection (DF-DD) schemes for autocorrelation-based detection in impulse-radio ultra-wideband (IR-UWB) systems, a signaling scheme regarded as a promising candidate in particular for low-complexity wireless sensor networks. To this end, we first discuss ideal nonc...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	8,836
1409.1556	Very Deep Convolutional Networks for Large-Scale Image Recognition	In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improveme...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	35,839
1809.03672	Deep Interest Evolution Network for Click-Through Rate Prediction	Click-through rate~(CTR) prediction, whose goal is to estimate the probability of the user clicks, has become one of the core tasks in advertising systems. For CTR prediction model, it is necessary to capture the latent user interest behind the user behavior data. Besides, considering the changing of the external envir...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	107,383
2405.20337	OccSora: 4D Occupancy Generation Models as World Simulators for Autonomous Driving	Understanding the evolution of 3D scenes is important for effective autonomous driving. While conventional methods mode scene development with the motion of individual instances, world models emerge as a generative framework to describe the general scene dynamics. However, most existing methods adopt an autoregressive ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	459,301
2008.09863	A Discrete-Time Matching Filtering Differentiator	This paper presents a time discretization of the robust exact filtering differentiator, a sliding mode differentiator coupled to filter, which provides a suitable approximation to the derivatives of some noisy signals. This proposal takes advantage of the homogeneity of the differentiator, allowing the use of similar t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	192,836
2211.03733	An Iterative Bidirectional Gradient Boosting Approach for CVR Baseline Estimation	This paper presents a novel Iterative Bidirectional Gradient Boosting Model (IBi-GBM) for estimating the baseline of Conservation Voltage Reduction (CVR) programs. In contrast to many existing methods, we treat CVR baseline estimation as a missing data retrieval problem. The approach involves dividing the load and its ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	329,023
1302.4107	Using Complex Networks to Quantify Consistency in the Use of Words	In this paper we quantify the consistency of word usage in written texts represented by complex networks, where words were taken as nodes, by measuring the degree of preservation of the node neighborhood.} Words were considered highly consistent if the authors used them with the same neighborhood. When ranked according...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	22,120
2409.12366	Bilevel Optimization for Real-Time Control with Application to Locomotion Gait Generation	Model Predictive Control (MPC) is a common tool for the control of nonlinear, real-world systems, such as legged robots. However, solving MPC quickly enough to enable its use in real-time is often challenging. One common solution is given by real-time iterations, which does not solve the MPC problem to convergence, but...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	489,546
1502.02179	Optimal Multiuser Scheduling Schemes for Simultaneous Wireless Information and Power Transfer	In this paper, we study the downlink multiuser scheduling problem for systems with simultaneous wireless information and power transfer (SWIPT). We design optimal scheduling algorithms that maximize the long-term average system throughput under different fairness requirements, such as proportional fairness and equal th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	40,007
2002.01322	Training Keyword Spotters with Limited and Synthesized Speech Data	With the rise of low power speech-enabled devices, there is a growing demand to quickly produce models for recognizing arbitrary sets of keywords. As with many machine learning tasks, one of the most challenging parts in the model creation process is obtaining a sufficient amount of training data. In this paper, we exp...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	162,626
1812.04128	Probabilistic Model Checking of Robots Deployed in Extreme Environments	Robots are increasingly used to carry out critical missions in extreme environments that are hazardous for humans. This requires a high degree of operational autonomy under uncertain conditions, and poses new challenges for assuring the robot's safety and reliability. In this paper, we develop a framework for probabili...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	116,146
1905.10289	MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching	Text matching is the core problem in many natural language processing (NLP) tasks, such as information retrieval, question answering, and conversation. Recently, deep leaning technology has been widely adopted for text matching, making neural text matching a new and active research domain. With a large number of neural...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	132,009
2204.13730	Direct Air-to-Underwater Optical Wireless Communication: Statistical Characterization and Outage Performance	In general, a buoy relay is used to connect the underwater communication to the terrestrial network over a radio or optical wireless communication (OWC) link. The use of relay deployment may pose security and deployment issues. This paper investigates the feasibility of direct air-to-underwater (A2UW) communication fro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	293,915
2308.14087	A comprehensive review on Plant Leaf Disease detection using Deep learning	Leaf disease is a common fatal disease for plants. Early diagnosis and detection is necessary in order to improve the prognosis of leaf diseases affecting plant. For predicting leaf disease, several automated systems have already been developed using different plant pathology imaging modalities. This paper provides a s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	388,184
2410.07505	CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression	Post-Training Quantization (PTQ) is an effective technique for compressing Large Language Models (LLMs). While many studies focus on quantizing both weights and activations, it is still a challenge to maintain the accuracy of LLM after activating quantization. To investigate the primary cause, we extend the concept of ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	496,655
2310.05719	Transformer Fusion with Optimal Transport	Fusion is a technique for merging multiple independently-trained neural networks in order to combine their capabilities. Past attempts have been restricted to the case of fully-connected, convolutional, and residual networks. This paper presents a systematic approach for fusing two or more transformer-based networks ex...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	398,266
2403.14626	ODTFormer: Efficient Obstacle Detection and Tracking with Stereo Cameras Based on Transformer	Obstacle detection and tracking represent a critical component in robot autonomous navigation. In this paper, we propose ODTFormer, a Transformer-based model to address both obstacle detection and tracking problems. For the detection task, our approach leverages deformable attention to construct a 3D cost volume, which...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	440,164
2409.01175	Logit Scaling for Out-of-Distribution Detection	The safe deployment of machine learning and AI models in open-world settings hinges critically on the ability to detect out-of-distribution (OOD) data accurately, data samples that contrast vastly from what the model was trained with. Current approaches to OOD detection often require further training the model, and/or ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	485,243
2501.18315	Surface Defect Identification using Bayesian Filtering on a 3D Mesh	This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygon...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	528,656
1911.08551	Prediction Focused Topic Models for Electronic Health Records	Electronic Health Record (EHR) data can be represented as discrete counts over a high dimensional set of possible procedures, diagnoses, and medications. Supervised topic models present an attractive option for incorporating EHR data as features into a prediction problem: given a patient's record, we estimate a set of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	154,215
1508.02138	A Generalized Multiscale Finite Element Method for Poroelasticity Problems II: Nonlinear Coupling	In this paper, we consider the numerical solution of some nonlinear poroelasticity problems that are of Biot type and develop a general algorithm for solving nonlinear coupled systems. We discuss the difficulties associated with flow and mechanics in heterogenous media with nonlinear coupling. The central issue being h...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	45,867
1809.07615	Lessons learned in multilingual grounded language learning	Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language. Here, we investigate in detail which conditions affect the performance of this type of grounded language learning model. We show that multilingual training improves over bilingual training, a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	108,314
2304.12778	Loss- and Reward-Weighting for Efficient Distributed Reinforcement Learning	This paper introduces two learning schemes for distributed agents in Reinforcement Learning (RL) environments, namely Reward-Weighted (R-Weighted) and Loss-Weighted (L-Weighted) gradient merger. The R/L weighted methods replace standard practices for training multiple agents, such as summing or averaging the gradients....	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	360,348
1807.02740	Data-driven Upsampling of Point Clouds	High quality upsampling of sparse 3D point clouds is critically useful for a wide range of geometric operations such as reconstruction, rendering, meshing, and analysis. In this paper, we propose a data-driven algorithm that enables an upsampling of 3D point clouds without the need for hard-coded rules. Our approach us...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	102,337
1602.04032	A Truthful Mechanism with Biparameter Learning for Online Crowdsourcing	We study a problem of allocating divisible jobs, arriving online, to workers in a crowdsourcing setting which involves learning two parameters of strategically behaving workers. Each job is split into a certain number of tasks that are then allocated to workers. Each arriving job has to be completed within a deadline a...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	52,080
2409.10959	Leveraging Reviewer Experience in Code Review Comment Generation	Modern code review is a ubiquitous software quality assurance process aimed at identifying potential issues within newly written code. Despite its effectiveness, the process demands large amounts of effort from the human reviewers involved. To help alleviate this workload, researchers have trained deep learning models ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	488,950
2012.02310	BoxInst: High-Performance Instance Segmentation with Box Annotations	We present a high-performance method that can achieve mask-level instance segmentation with only bounding-box annotations for training. While this setting has been studied in the literature, here we show significantly stronger performance with a simple design (e.g., dramatically improving previous best reported mask AP...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	209,726
2410.01910	Is uniform expressivity too restrictive? Towards efficient expressivity of graph neural networks	Uniform expressivity guarantees that a Graph Neural Network (GNN) can express a query without the parameters depending on the size of the input graphs. This property is desirable in applications in order to have number of trainable parameters that is independent of the size of the input graphs. Uniform expressivity of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	494,007
1906.05721	Visual Wake Words Dataset	The emergence of Internet of Things (IoT) applications requires intelligence on the edge. Microcontrollers provide a low-cost compute platform to deploy intelligent IoT applications using machine learning at scale, but have extremely limited on-chip memory and compute capability. To deploy computer vision on such devic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	135,100
1805.08297	Character-based Neural Networks for Sentence Pair Modeling	Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference. Most state-of-the-art neural models for these tasks rely on pretrained word embedding and compose sentence-level semantics in varied ways; however, few works have attemp...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	98,092
2308.09829	Learning from A Single Graph is All You Need for Near-Shortest Path Routing in Wireless Networks	We propose a learning algorithm for local routing policies that needs only a few data samples obtained from a single graph while generalizing to all random graphs in a standard model of wireless networks. We thus solve the all-pairs near-shortest path problem by training deep neural networks (DNNs) that efficiently and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	386,437
2401.11492	Edge-Enabled Real-time Railway Track Segmentation	Accurate and rapid railway track segmentation can assist automatic train driving and is a key step in early warning to fixed or moving obstacles on the railway track. However, certain existing algorithms tailored for track segmentation often struggle to meet the requirements of real-time and efficiency on resource-cons...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	423,027
2105.00329	ECNNs: Ensemble Learning Methods for Improving Planar Grasp Quality Estimation	We present an ensemble learning methodology that combines multiple existing robotic grasp synthesis algorithms and obtain a success rate that is significantly better than the individual algorithms. The methodology treats the grasping algorithms as "experts" providing grasp "opinions". An Ensemble Convolutional Neural N...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	233,178
2302.10895	CQnet: convex-geometric interpretation and constraining neural-network trajectories	We introduce CQnet, a neural network with origins in the CQ algorithm for solving convex split-feasibility problems and forward-backward splitting. CQnet's trajectories are interpretable as particles that are tracking a changing constraint set via its point-to-set distance function while being elements of another const...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	347,001
2102.02970	Optimizing RRH Placement Under a Noise-Limited Point-to-Point Wireless Backhaul	In this paper, we study the deployment decisions and location optimization for the remote radio heads (RRHs) in coordinated distributed networks in the presence of a wireless backhaul. We implement a scheme where the RRHs use zero-forcing beamforming (ZF-BF) for the access channel to jointly serve multiple users, while...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	218,584
1706.03847	Recurrent Neural Networks with Top-k Gains for Session-based Recommendations	RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner. The use of RNNs provides impressive performance benefits over classical methods in session-based recommendations. In this work we introduce novel ranking loss functions tailo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	75,231
1506.05937	A Tight Runtime Analysis of the $(1+(\lambda, \lambda))$ Genetic Algorithm on OneMax	Understanding how crossover works is still one of the big challenges in evolutionary computation research, and making our understanding precise and proven by mathematical means might be an even bigger one. As one of few examples where crossover provably is useful, the $(1+(\lambda, \lambda))$ Genetic Algorithm (GA) was...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	44,359
1803.08086	Influence of augmented humans in online interactions during voting events	The advent of the digital era provided a fertile ground for the development of virtual societies, complex systems influencing real-world dynamics. Understanding online human behavior and its relevance beyond the digital boundaries is still an open challenge. Here we show that online social interactions during a massive...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	93,191
2301.02021	Dynamic Sizing of Frequency Control Ancillary Service Requirements for a Philippine Grid	Sizing frequency control ancillary service (FCAS) requirements is crucial for the reliable operation of power systems amid a continuous influx of variable renewable energy (VRE) generation. Reserve sizing is especially pertinent for the Philippine grids due to an expected transition to new FCAS classifications establis...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	339,391
2210.09503	Towards Fair Classification against Poisoning Attacks	Fair classification aims to stress the classification models to achieve the equality (treatment or prediction quality) among different sensitive groups. However, fair classification can be under the risk of poisoning attacks that deliberately insert malicious training samples to manipulate the trained classifiers' perf...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	324,556
2406.11231	Enabling robots to follow abstract instructions and complete complex dynamic tasks	Completing complex tasks in unpredictable settings like home kitchens challenges robotic systems. These challenges include interpreting high-level human commands, such as "make me a hot beverage" and performing actions like pouring a precise amount of water into a moving mug. To address these challenges, we present a n...	false	false	false	false	true	false	true	true	true	false	false	false	false	false	false	false	false	false	464,778
1709.06428	Sensor Assignment Algorithms to Improve Observability while Tracking Targets	We study two sensor assignment problems for multi-target tracking with the goal of improving the observability of the underlying estimator. We consider various measures of the observability matrix as the assignment value function. We first study the general version where the sensors must form teams to track individual ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	81,097
2303.13971	Optimal Transport for Offline Imitation Learning	With the advent of large datasets, offline reinforcement learning (RL) is a promising framework for learning good decision-making policies without the need to interact with the real environment. However, offline RL requires the dataset to be reward-annotated, which presents practical challenges when reward engineering ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	353,903
2408.05896	Scalable recommender system based on factor analysis	Recommender systems have become crucial in the modern digital landscape, where personalized content, products, and services are essential for enhancing user experience. This paper explores statistical models for recommender systems, focusing on crossed random effects models and factor analysis. We extend the crossed ra...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	479,990
1810.08126	KTAN: Knowledge Transfer Adversarial Network	To reduce the large computation and storage cost of a deep convolutional neural network, the knowledge distillation based methods have pioneered to transfer the generalization ability of a large (teacher) deep network to a light-weight (student) network. However, these methods mostly focus on transferring the probabili...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	110,761
2112.03010	Optimized Deployment of Unmanned Aerial Vehicles for Wildfire Detection and Monitoring	In recent years, increased wildfires have caused irreversible damage to forest resources worldwide, threatening wildlives and human living conditions. The lack of accurate frontline information in real-time can pose great risks to firefighters. Though a plethora of machine learning algorithms have been developed to det...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	270,052
1907.11457	Two-hidden-layer Feedforward Neural Networks are Universal Approximators: A Constructive Approach	It is well known that Artificial Neural Networks are universal approximators. The classical result proves that, given a continuous function on a compact set on an n-dimensional space, then there exists a one-hidden-layer feedforward network which approximates the function. Such result proves the existence, but it does ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	139,851
2009.07646	Eating Habits Discovery in Egocentric Photo-streams	Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioural pattern discove...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	196,004
1905.11075	Machine Learning for Fluid Mechanics	The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,325
2407.08238	Integrated User Matching and Pricing in Round-Trip Car-Sharing	Traditional round-trip car rental systems mandate users to return vehicles to their point of origin, limiting the system adaptability to meet diverse mobility demands. This constraint often leads to fleet under-utilization and incurs high parking costs for idle vehicles. To address this inefficiency, we propose a N-use...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	472,080
2405.06907	AIOS Compiler: LLM as Interpreter for Natural Language Programming and Flow Programming of AI Agents	Since their inception, programming languages have trended towards greater readability and lower barriers for programmers. Following this trend, natural language can be a promising type of programming language that provides great flexibility and usability and helps towards the democracy of programming. However, the inhe...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	453,495
2208.04517	Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning	In recent years, image generation has made great strides in improving the quality of images, producing high-fidelity ones. Also, quite recently, there are architecture designs, which enable GAN to unsupervisedly learn the semantic attributes represented in different layers. However, there is still a lack of research on...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	312,135
2305.17498	A Model-Based Method for Minimizing CVaR and Beyond	We develop a variant of the stochastic prox-linear method for minimizing the Conditional Value-at-Risk (CVaR) objective. CVaR is a risk measure focused on minimizing worst-case performance, defined as the average of the top quantile of the losses. In machine learning, such a risk measure is useful to train more robust ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	368,623
2410.12107	Just-In-Time Software Defect Prediction via Bi-modal Change Representation Learning	For predicting software defects at an early stage, researchers have proposed just-in-time defect prediction (JIT-DP) to identify potential defects in code commits. The prevailing approaches train models to represent code changes in history commits and utilize the learned representations to predict the presence of defec...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	498,855
2203.06463	A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets	Computer vision-based parking lot management methods have been extensively researched upon owing to their flexibility and cost-effectiveness. To evaluate such methods authors often employ publicly available parking lot image datasets. In this study, we surveyed and compared robust publicly available image datasets spec...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	285,125
2311.01875	Enhancing Functional Data Analysis with Sequential Neural Networks: Advantages and Comparative Study	Functional Data Analysis (FDA) is a statistical domain developed to handle functional data characterized by high dimensionality and complex data structures. Sequential Neural Networks (SNNs) are specialized neural networks capable of processing sequence data, a fundamental aspect of functional data. Despite their great...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	405,212
2204.08200	Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond	The vast majority of existing algorithms for unsupervised domain adaptation (UDA) focus on adapting from a labeled source domain to an unlabeled target domain directly in a one-off way. Gradual domain adaptation (GDA), on the other hand, assumes a path of $(T-1)$ unlabeled intermediate domains bridging the source and t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	292,005
2310.02638	P2CADNet: An End-to-End Reconstruction Network for Parametric 3D CAD Model from Point Clouds	Computer Aided Design (CAD), especially the feature-based parametric CAD, plays an important role in modern industry and society. However, the reconstruction of featured CAD model is more challenging than the reconstruction of other CAD models. To this end, this paper proposes an end-to-end network to reconstruct featu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	396,935
1904.05374	Searching Heterogeneous Personal Digital Traces	Digital traces of our lives are now constantly produced by various connected devices, internet services and interactions. Our actions result in a multitude of heterogeneous data objects, or traces, kept in various locations in the cloud or on local devices. Users have very few tools to organize, understand, and search ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	127,294
0710.1469	Weight Distributions of Hamming Codes (II)	In a previous paper, we derived a recursive formula determining the weight distributions of the [n=(q^m-1)/(q-1)] Hamming code H(m,q), when (m,q-1)=1. Here q is a prime power. We note here that the formula actually holds for any positive integer m and any prime power q, without the restriction (m, q-1)=1.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	751
0903.0548	On the 3-Receiver Broadcast Channel with Degraded Message Sets and Confidential Messages	In this paper, bounds to the rate-equivocation region for the general 3-receiver broadcast channel (BC) with degraded message sets, are presented for confidential messages to be kept secret from one of the receivers. This model is more general than the 2-receiver BCs with confidential messages with an external wiretapp...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,275
cs/0312058	Acquiring Lexical Paraphrases from a Single Corpus	This paper studies the potential of identifying lexical paraphrases within a single corpus, focusing on the extraction of verb paraphrases. Most previous approaches detect individual paraphrase instances within a pair (or set) of comparable corpora, each of them containing roughly the same information, and rely on the ...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	538,077
1902.03122	Fully Convolutional Neural Network for Semantic Segmentation of Anatomical Structure and Pathologies in Colour Fundus Images Associated with Diabetic Retinopathy	Diabetic retinopathy (DR) is the most common form of diabetic eye disease. Retinopathy can affect all diabetic patients and becomes particularly dangerous, increasing the risk of blindness, if it is left untreated. The success rate of its curability solemnly depends on diagnosis at an early stage. The development of au...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,030
2502.09768	Complex Network Modelling with Power-law Activating Patterns and Its Evolutionary Dynamics	Complex network theory provides a unifying framework for the study of structured dynamic systems. The current literature emphasizes a widely reported phenomenon of intermittent interaction among network vertices. In this paper, we introduce a complex network model that considers the stochastic switching of individuals ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	533,589
2303.03919	Data Portraits: Recording Foundation Model Training Data	Foundation models are trained on increasingly immense and opaque datasets. Even while these models are now key in AI system building, it can be difficult to answer the straightforward question: has the model already encountered a given example during training? We therefore propose a widespread adoption of Data Portrait...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	349,893
2001.10719	Query-Sequence Optimization on a Reconfigurable Hardware-Accelerated System	Hardware acceleration of database query processing can be done with the help of FPGAs. In particular, they are partially reconfigurable during runtime, which allows for the runtime adaption of the hardware to a variety of queries. Reconfiguration itself, however, takes some time. As the affected area of the FPGA is not...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	161,890
2407.16309	A new visual quality metric for Evaluating the performance of multidimensional projections	Multidimensional projections (MP) are among the most essential approaches in the visual analysis of multidimensional data. It transforms multidimensional data into two-dimensional representations that may be shown as scatter plots while preserving their similarity with the original data. Human visual perception is freq...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	475,552
2009.11937	daVinciNet: Joint Prediction of Motion and Surgical State in Robot-Assisted Surgery	This paper presents a technique to concurrently and jointly predict the future trajectories of surgical instruments and the future state(s) of surgical subtasks in robot-assisted surgeries (RAS) using multiple input sources. Such predictions are a necessary first step towards shared control and supervised autonomy of s...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	197,283
1508.03664	Rethinking the Intercept Probability of Random Linear Network Coding	This letter considers a network comprising a transmitter, which employs random linear network coding to encode a message, a legitimate receiver, which can recover the message if it gathers a sufficient number of linearly independent coded packets, and an eavesdropper. Closed-form expressions for the probability of the ...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	true	46,024
2111.02545	Multi-task Learning of Order-Consistent Causal Graphs	We consider the problem of discovering $K$ related Gaussian directed acyclic graphs (DAGs), where the involved graph structures share a consistent causal order and sparse unions of supports. Under the multi-task learning setting, we propose a $l_1/l_2$-regularized maximum likelihood estimator (MLE) for learning $K$ lin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,900
2405.19712	HINT: Learning Complete Human Neural Representations from Limited Viewpoints	No augmented application is possible without animated humanoid avatars. At the same time, generating human replicas from real-world monocular hand-held or robotic sensor setups is challenging due to the limited availability of views. Previous work showed the feasibility of virtual avatars but required the presence of 3...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	459,025
1703.05452	Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting	We consider the optimal value of information (VoI) problem, where the goal is to sequentially select a set of tests with a minimal cost, so that one can efficiently make the best decision based on the observed outcomes. Existing algorithms are either heuristics with no guarantees, or scale poorly (with exponential run ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	70,083
2501.06505	Online Algorithm for Aggregating Experts' Predictions with Unbounded Quadratic Loss	We consider the problem of online aggregation of expert predictions with the quadratic loss function. We propose an algorithm for aggregating expert predictions which does not require a prior knowledge of the upper bound on the losses. The algorithm is based on the exponential reweighing of expert losses.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	524,012
2407.00478	Beyond Scaleup: Knowledge-aware Parsimony Learning from Deep Networks	The brute-force scaleup of training datasets, learnable parameters and computation power, has become a prevalent strategy for developing more robust learning models. However, due to bottlenecks in data, computation, and trust, the sustainability of this strategy is a serious concern. In this paper, we attempt to addres...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	468,876
2307.13427	Comprehensive Review on Semantic Information Retrieval and Ontology Engineering	Situation awareness is a crucial cognitive skill that enables individuals to perceive, comprehend, and project the current state of their environment accurately. It involves being conscious of relevant information, understanding its meaning, and using that understanding to make well-informed decisions. Awareness system...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	381,585
1308.5133	Performance Measurement Under Increasing Environmental Uncertainty In The Context of Interval Type-2 Fuzzy Logic Based Robotic Sailing	Performance measurement of robotic controllers based on fuzzy logic, operating under uncertainty, is a subject area which has been somewhat ignored in the current literature. In this paper standard measures such as RMSE are shown to be inappropriate for use under conditions where the environmental uncertainty changes s...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	true	false	false	26,600

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