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
2103.15216	A Bulk-Controlled Low-Voltage CMOS Quadrature Oscillator	In this paper, an schema for controlling the oscillation frequency of a quadrature oscillator is proposed. The method involves controlling the threshold voltage of the PMOS transistors in the inverter through control of the bulk bias voltage. Results obtained using HSPICE simulation are presented in a technology of 0.3...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	227,119
2302.09448	GRAFS: Graphical Faceted Search System to Support Conceptual Understanding in Exploratory Search	When people search for information about a new topic within large document collections, they implicitly construct a mental model of the unfamiliar information space to represent what they currently know and guide their exploration into the unknown. Building this mental model can be challenging as it requires not only f...	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	346,429
2412.09569	JuStRank: Benchmarking LLM Judges for System Ranking	Given the rapid progress of generative AI, there is a pressing need to systematically compare and choose between the numerous models and configurations available. The scale and versatility of such evaluations make the use of LLM-based judges a compelling solution for this challenge. Crucially, this approach requires fi...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	516,532
1803.06354	Serverless Data Analytics with Flint	Serverless architectures organized around loosely-coupled function invocations represent an emerging design for many applications. Recent work mostly focuses on user-facing products and event-driven processing pipelines. In this paper, we explore a completely different part of the application space and examine the feas...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	92,814
2411.07506	FlowTS: Time Series Generation via Rectified Flow	Diffusion-based models have significant achievements in time series generation but suffer from inefficient computation: solving high-dimensional ODEs/SDEs via iterative numerical solvers demands hundreds to thousands of drift function evaluations per sample, incurring prohibitive costs. To resolve this, we propose Flow...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	507,555
2411.05340	Improving Multi-Domain Task-Oriented Dialogue System with Offline Reinforcement Learning	Task-oriented dialogue (TOD) system is designed to accomplish user-defined tasks through dialogues. The TOD system has progressed towards end-to-end modeling by leveraging pre-trained large language models. Fine-tuning the pre-trained language models using only supervised learning leads to the exposure bias and token l...	true	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	506,634
1903.03850	Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework	We develop a novel theoretical framework for understating OT schemes respecting a class structure. For this purpose, we propose a convex OT program with a sum-of-norms regularization term, which provably recovers the underlying class structure under geometric assumptions. Furthermore, we derive an accelerated proximal ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	123,836
1711.04259	On the Synthesis of Guaranteed-Quality Plans for Robot Fleets in Logistics Scenarios via Optimization Modulo Theories	In manufacturing, the increasing involvement of autonomous robots in production processes poses new challenges on the production management. In this paper we report on the usage of Optimization Modulo Theories (OMT) to solve certain multi-robot scheduling problems in this area. Whereas currently existing methods are he...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	84,366
1709.00228	Learning Multi-item Auctions with (or without) Samples	We provide algorithms that learn simple auctions whose revenue is approximately optimal in multi-item multi-bidder settings, for a wide range of valuations including unit-demand, additive, constrained additive, XOS, and subadditive. We obtain our learning results in two settings. The first is the commonly studied setti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	79,870
2005.02805	Network extraction by routing optimization	Routing optimization is a relevant problem in many contexts. Solving directly this type of optimization problem is often computationally unfeasible. Recent studies suggest that one can instead turn this problem into one of solving a dynamical system of equations, which can instead be solved efficiently using numerical ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	175,977
2201.12380	GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games	Explaining machine learning models is an important and increasingly popular area of research interest. The Shapley value from game theory has been proposed as a prime approach to compute feature importance towards model predictions on images, text, tabular data, and recently graph neural networks (GNNs) on graphs. In t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	277,619
1811.04324	Diversity-Driven Extensible Hierarchical Reinforcement Learning	Hierarchical reinforcement learning (HRL) has recently shown promising advances on speeding up learning, improving the exploration, and discovering intertask transferable skills. Most recent works focus on HRL with two levels, i.e., a master policy manipulates subpolicies, which in turn manipulate primitive actions. Ho...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,056
2401.08903	Rethinking Impersonation and Dodging Attacks on Face Recognition Systems	Face Recognition (FR) systems can be easily deceived by adversarial examples that manipulate benign face images through imperceptible perturbations. Adversarial attacks on FR encompass two types: impersonation (targeted) attacks and dodging (untargeted) attacks. Previous methods often achieve a successful impersonation...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	422,070
2307.02198	ChiENN: Embracing Molecular Chirality with Graph Neural Networks	Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics. However, typical GNNs cannot capture the concept of chirality, which means they do not distinguish between the 3D graph of a chemical compound and its mirror image (enantiomer). The ability to distingu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	377,617
2310.04585	Interventions Against Machine-Assisted Statistical Discrimination	I study statistical discrimination driven by verifiable beliefs, such as those generated by machine learning, rather than by humans. When beliefs are verifiable, interventions against statistical discrimination can move beyond simple, belief-free designs like affirmative action, to more sophisticated ones, that constra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	397,723
cs/0511012	Parameters Affecting the Resilience of Scale-Free Networks to Random Failures	It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. study scale-free networks including some which approximate the measured degree distribution of the Internet. Their results suggest that if each node in this network failed independently wit...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	539,057
2406.16224	From Text to Test: AI-Generated Control Software for Materials Science Instruments	Large language models (LLMs) are transforming the landscape of chemistry and materials science. Recent examples of LLM-accelerated experimental research include virtual assistants for parsing synthesis recipes from the literature, or using the extracted knowledge to guide synthesis and characterization. Despite these a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	467,046
2404.09320	MPC Based Linear Equivalence with Control Barrier Functions for VTOL-UAVs	In this work, we propose a cascaded scheme of linear Model prediction Control (MPC) based on Control Barrier Functions (CBF) with Dynamic Feedback Linearization (DFL) for Vertical Take-off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs). CBF is a tool that allows enforcement of forward invariance of a set using Lyap...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	446,624
2409.16925	Game4Loc: A UAV Geo-Localization Benchmark from Game Data	The vision-based geo-localization technology for UAV, serving as a secondary source of GPS information in addition to the global navigation satellite systems (GNSS), can still operate independently in the GPS-denied environment. Recent deep learning based methods attribute this as the task of image matching and retriev...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	491,578
2312.06406	Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing	In this paper, we address the issue of increasing the performance of reinforcement learning (RL) solutions for autonomous racing cars when navigating under conditions where practical vehicle modelling errors (commonly known as \emph{model mismatches}) are present. To address this challenge, we propose a partial end-to-...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	414,507
2307.07614	Towards Generalizable Detection of Urgency of Discussion Forum Posts	Students who take an online course, such as a MOOC, use the course's discussion forum to ask questions or reach out to instructors when encountering an issue. However, reading and responding to students' questions is difficult to scale because of the time needed to consider each message. As a result, critical issues ma...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	379,472
2311.09376	DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attention	Among the array of neural network architectures, the Vision Transformer (ViT) stands out as a prominent choice, acclaimed for its exceptional expressiveness and consistent high performance in various vision applications. Recently, the emerging Spiking ViT approach has endeavored to harness spiking neurons, paving the w...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	408,113
1611.02266	Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering	We propose the Gaussian attention model for content-based neural memory access. With the proposed attention model, a neural network has the additional degree of freedom to control the focus of its attention from a laser sharp attention to a broad attention. It is applicable whenever we can assume that the distance in t...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	63,535
1811.07441	CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition	Data-driven generative modeling has made remarkable progress by leveraging the power of deep neural networks. A reoccurring challenge is how to enable a model to generate a rich variety of samples from the entire target distribution, rather than only from a distribution confined to the training data. In other words, we...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	113,765
2109.10253	Short-term traffic prediction using physics-aware neural networks	In this work, we propose an algorithm performing short-term predictions of the flux of vehicles on a stretch of road, using past measurements of the flux. This algorithm is based on a physics-aware recurrent neural network. A discretization of a macroscopic traffic flow model (using the so-called Traffic Reaction Model...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,556
1811.08064	Model and Integrate Medical Resource Availability into Verifiably Correct Executable Medical Guidelines - Technical Report	Improving effectiveness and safety of patient care is an ultimate objective for medical cyber-physical systems. A recent study shows that the patients' death rate can be reduced by computerizing medical guidelines. Most existing medical guideline models are validated and/or verified based on the assumption that all nec...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	113,944
1801.03595	Efficient Local Map Search Algorithms for the Placement of Flying Relays	This paper studies the optimal unmanned aerial vehicle (UAV) placement problem for wireless networking. The UAV operates as a flying wireless relay to provide coverage extension for a base station (BS) and deliver capacity boost to a user shadowed by obstacles. While existing methods rely on statistical models for pote...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	88,121
2205.03436	EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers	Self-attention based models such as vision transformers (ViTs) have emerged as a very competitive architecture alternative to convolutional neural networks (CNNs) in computer vision. Despite increasingly stronger variants with ever-higher recognition accuracies, due to the quadratic complexity of self-attention, existi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	295,279
2408.03047	OpenOmni: A Collaborative Open Source Tool for Building Future-Ready Multimodal Conversational Agents	Multimodal conversational agents are highly desirable because they offer natural and human-like interaction. However, there is a lack of comprehensive end-to-end solutions to support collaborative development and benchmarking. While proprietary systems like GPT-4o and Gemini demonstrating impressive integration of audi...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	478,874
2105.05690	Machine learning moment closure models for the radiative transfer equation I: directly learning a gradient based closure	In this paper, we take a data-driven approach and apply machine learning to the moment closure problem for radiative transfer equation in slab geometry. Instead of learning the unclosed high order moment, we propose to directly learn the gradient of the high order moment using neural networks. This new approach is cons...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	234,891
2412.10857	Robust Persian Digit Recognition in Noisy Environments Using Hybrid CNN-BiGRU Model	Artificial intelligence (AI) has significantly advanced speech recognition applications. However, many existing neural network-based methods struggle with noise, reducing accuracy in real-world environments. This study addresses isolated spoken Persian digit recognition (zero to nine) under noisy conditions, particular...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	517,138
2009.02010	ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement Learning	DNN accelerators provide efficiency by leveraging reuse of activations/weights/outputs during the DNN computations to reduce data movement from DRAM to the chip. The reuse is captured by the accelerator's dataflow. While there has been significant prior work in exploring and comparing various dataflows, the strategy fo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	194,440
2201.01778	Quantum Capsule Networks	Capsule networks, which incorporate the paradigms of connectionism and symbolism, have brought fresh insights into artificial intelligence. The capsule, as the building block of capsule networks, is a group of neurons represented by a vector to encode different features of an entity. The information is extracted hierar...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	274,346
1607.04376	Intrinsically Motivated Multimodal Structure Learning	We present a long-term intrinsically motivated structure learning method for modeling transition dynamics during controlled interactions between a robot and semi-permanent structures in the world. In particular, we discuss how partially-observable state is represented using distributions over a Markovian state and buil...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	58,611
1805.07220	Memoryless Exact Solutions for Deterministic MDPs with Sparse Rewards	We propose an algorithm for deterministic continuous Markov Decision Processes with sparse rewards that computes the optimal policy exactly with no dependency on the size of the state space. The algorithm has time complexity of $O( \|R\|^3 \times \|A\|^2 )$ and memory complexity of $O( \|R\| \times \|A\| )$, where $\|R\|$ is the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	97,764
2501.05946	Coverage and Spectral Efficiency of NOMA-Enabled LEO Satellite Networks with Ordering Schemes	This paper investigates an analytical model for low-earth orbit (LEO) multi-satellite downlink non-orthogonal multiple access (NOMA) networks. The satellites transmit data to multiple NOMA user terminals (UTs), each employing successive interference cancellation (SIC) for decoding. Two ordering schemes are adopted for ...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	523,779
2403.12047	Alpha-wolves and Alpha-mammals: Exploring Dictionary Attacks on Iris Recognition Systems	A dictionary attack in a biometric system entails the use of a small number of strategically generated images or templates to successfully match with a large number of identities, thereby compromising security. We focus on dictionary attacks at the template level, specifically the IrisCodes used in iris recognition sys...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	439,004
2305.13088	Should We Attend More or Less? Modulating Attention for Fairness	The advances in natural language processing (NLP) pose both opportunities and challenges. While recent progress enables the development of high-performing models for a variety of tasks, it also poses the risk of models learning harmful biases from the data, such as gender stereotypes. In this work, we investigate the r...	false	false	false	false	true	false	true	false	true	false	false	false	false	true	false	false	false	false	366,343
2406.16282	Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation	Fine-tuning pretrained large models to downstream tasks is an important problem, which however suffers from huge memory overhead due to large-scale parameters. This work strives to reduce memory overhead in fine-tuning from perspectives of activation function and layer normalization. To this end, we propose the Approxi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	467,071
1808.08015	An Enhanced SCMA Detector Enabled by Deep Neural Network	In this paper, we propose a learning approach for sparse code multiple access (SCMA) signal detection by using a deep neural network via unfolding the procedure of message passing algorithm (MPA). The MPA can be converted to a sparsely connected neural network if we treat the weights as the parameters of a neural netwo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	105,858
2207.02504	Dual Decision Improves Open-Set Panoptic Segmentation	Open-set panoptic segmentation (OPS) problem is a new research direction aiming to perform segmentation for both \known classes and \unknown classes, i.e., the objects ("things") that are never annotated in the training set. The main challenges of OPS are twofold: (1) the infinite possibility of the \unknown object app...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	306,535
2106.10711	Transfer Bayesian Meta-learning via Weighted Free Energy Minimization	Meta-learning optimizes the hyperparameters of a training procedure, such as its initialization, kernel, or learning rate, based on data sampled from a number of auxiliary tasks. A key underlying assumption is that the auxiliary tasks, known as meta-training tasks, share the same generating distribution as the tasks to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	242,130
2001.05714	Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records	Unstructured information in electronic health records provide an invaluable resource for medical research. To protect the confidentiality of patients and to conform to privacy regulations, de-identification methods automatically remove personally identifying information from these medical records. However, due to the u...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	160,617
1208.4171	The Unified Logging Infrastructure for Data Analytics at Twitter	In recent years, there has been a substantial amount of work on large-scale data analytics using Hadoop-based platforms running on large clusters of commodity machines. A less-explored topic is how those data, dominated by application logs, are collected and structured to begin with. In this paper, we present Twitter's...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	18,186
2010.00993	MADRaS : Multi Agent Driving Simulator	In this work, we present MADRaS, an open-source multi-agent driving simulator for use in the design and evaluation of motion planning algorithms for autonomous driving. MADRaS provides a platform for constructing a wide variety of highway and track driving scenarios where multiple driving agents can train for motion pl...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	true	false	false	false	198,470
2307.11471	Robust Visual Question Answering: Datasets, Methods, and Future Challenges	Visual question answering requires a system to provide an accurate natural language answer given an image and a natural language question. However, it is widely recognized that previous generic VQA methods often exhibit a tendency to memorize biases present in the training data rather than learning proper behaviors, su...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	380,921
1405.1020	Study on performance improvement of oil paint image filter algorithm using parallel pattern library	This paper gives a detailed study on the performance of oil paint image filter algorithm with various parameters applied on an image of RGB model. Oil Paint image processing, being very performance hungry, current research tries to find improvement using parallel pattern library. With increasing kernel-size, the proces...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	32,835
2012.14204	Screening COVID-19 Based on CT/CXR Images & Building a Publicly Available CT-scan Dataset of COVID-19	The rapid outbreak of COVID-19 threatens humans life all around the world. Due to insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. As chest radiography, such as chest X-ray (CXR) and CT computed tomography (CT), is a possible way ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	213,440
2304.14332	On the Generalization Error of Meta Learning for the Gibbs Algorithm	We analyze the generalization ability of joint-training meta learning algorithms via the Gibbs algorithm. Our exact characterization of the expected meta generalization error for the meta Gibbs algorithm is based on symmetrized KL information, which measures the dependence between all meta-training datasets and the out...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	360,904
2005.00983	Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network	In many domestic and military applications, aerial vehicle detection and super-resolutionalgorithms are frequently developed and applied independently. However, aerial vehicle detection on super-resolved images remains a challenging task due to the lack of discriminative information in the super-resolved images. To add...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	175,460
1302.0215	Informational Divergence Approximations to Product Distributions	The minimum rate needed to accurately approximate a product distribution based on an unnormalized informational divergence is shown to be a mutual information. This result subsumes results of Wyner on common information and Han-Verd\'{u} on resolvability. The result also extends to cases where the source distribution i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	21,694
1805.06336	Characterizing Information Propagation in Plants	This paper considers an electro-chemical based communication model for intercellular communication in plants. Many plants, such as Mimosa pudica (the "sensitive plant"), employ electrochemical signals known as action potentials (APs) for communication purposes. In this paper we present a simple model for action potenti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	97,585
2301.06489	Simplex Autoencoders	Synthetic data generation is increasingly important due to privacy concerns. While Autoencoder-based approaches have been widely used for this purpose, sampling from their latent spaces can be challenging. Mixture models are currently the most efficient way to sample from these spaces. In this work, we propose a new ap...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	340,657
2405.15398	PriCE: Privacy-Preserving and Cost-Effective Scheduling for Parallelizing the Large Medical Image Processing Workflow over Hybrid Clouds	Running deep neural networks for large medical images is a resource-hungry and time-consuming task with centralized computing. Outsourcing such medical image processing tasks to hybrid clouds has benefits, such as a significant reduction of execution time and monetary cost. However, due to privacy concerns, it is still...	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	456,912
2203.14860	Time-inhomogeneous diffusion geometry and topology	Diffusion condensation is a dynamic process that yields a sequence of multiscale data representations that aim to encode meaningful abstractions. It has proven effective for manifold learning, denoising, clustering, and visualization of high-dimensional data. Diffusion condensation is constructed as a time-inhomogeneou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	288,156
2406.05850	Scaling Graph Convolutions for Mobile Vision	To compete with existing mobile architectures, MobileViG introduces Sparse Vision Graph Attention (SVGA), a fast token-mixing operator based on the principles of GNNs. However, MobileViG scales poorly with model size, falling at most 1% behind models with similar latency. This paper introduces Mobile Graph Convolution ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	462,321
2408.09702	Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering	The correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials, as well as the image formation process. While recent large-scale diffusion models have shown strong generative and inpainting capabilities, we find that current models d...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	481,549
1711.00489	Don't Decay the Learning Rate, Increase the Batch Size	It is common practice to decay the learning rate. Here we show one can usually obtain the same learning curve on both training and test sets by instead increasing the batch size during training. This procedure is successful for stochastic gradient descent (SGD), SGD with momentum, Nesterov momentum, and Adam. It reache...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	83,725
2104.01785	Annotating Columns with Pre-trained Language Models	Inferring meta information about tables, such as column headers or relationships between columns, is an active research topic in data management as we find many tables are missing some of this information. In this paper, we study the problem of annotating table columns (i.e., predicting column types and the relationshi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	228,479
2502.10120	Compress image to patches for Vision Transformer	The Vision Transformer (ViT) has made significant strides in the field of computer vision. However, as the depth of the model and the resolution of the input images increase, the computational cost associated with training and running ViT models has surged dramatically. This paper proposes a hybrid model based on CNN a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	533,743
2411.07621	Mix from Failure: Confusion-Pairing Mixup for Long-Tailed Recognition	Long-tailed image recognition is a computer vision problem considering a real-world class distribution rather than an artificial uniform. Existing methods typically detour the problem by i) adjusting a loss function, ii) decoupling classifier learning, or iii) proposing a new multi-head architecture called experts. In ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	507,619
2409.13319	Knowledge-Based Ultra-Low-Latency Semantic Communications for Robotic Edge Intelligence	The 6G mobile networks will feature the widespread deployment of AI algorithms at the network edge, which provides a platform for supporting robotic edge intelligence systems. In such a system, a large-scale knowledge graph (KG) is operated at an edge server as a "remote brain" to guide remote robots on environmental e...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	489,941
2409.16386	Camera Calibration and Stereo via a Single Image of a Spherical Mirror	This paper presents a novel technique for camera calibration using a single view that incorporates a spherical mirror. Leveraging the distinct characteristics of the sphere's contour visible in the image and its reflections, we showcase the effectiveness of our method in achieving precise calibration. Furthermore, the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	491,319
2306.13040	What to Learn: Features, Image Transformations, or Both?	Long-term visual localization is an essential problem in robotics and computer vision, but remains challenging due to the environmental appearance changes caused by lighting and seasons. While many existing works have attempted to solve it by directly learning invariant sparse keypoints and descriptors to match scenes,...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	375,141
2010.12089	The Pursuit of Algorithmic Fairness: On "Correcting" Algorithmic Unfairness in a Child Welfare Reunification Success Classifier	The algorithmic fairness of predictive analytic tools in the public sector has increasingly become a topic of rigorous exploration. While instruments pertaining to criminal recidivism and academic admissions, for example, have garnered much attention, the predictive instruments of Child Welfare jurisdictions have recei...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	202,556
2411.08562	Neural Corrective Machine Unranking	Machine unlearning in neural information retrieval (IR) systems requires removing specific data whilst maintaining model performance. Applying existing machine unlearning methods to IR may compromise retrieval effectiveness or inadvertently expose unlearning actions due to the removal of particular items from the retri...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	507,934
2008.13374	Active Local Learning	In this work we consider active local learning: given a query point $x$, and active access to an unlabeled training set $S$, output the prediction $h(x)$ of a near-optimal $h \in H$ using significantly fewer labels than would be needed to actually learn $h$ fully. In particular, the number of label queries should be in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	193,833
2404.10717	Mixed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation	Recently, prototype learning has emerged in semi-supervised medical image segmentation and achieved remarkable performance. However, the scarcity of labeled data limits the expressiveness of prototypes in previous methods, potentially hindering the complete representation of prototypes for class embedding. To address t...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	447,220
2402.06019	Checking the Sufficiently Scattered Condition using a Global Non-Convex Optimization Software	The sufficiently scattered condition (SSC) is a key condition in the study of identifiability of various matrix factorization problems, including nonnegative, minimum-volume, symmetric, simplex-structured, and polytopic matrix factorizations. The SSC allows one to guarantee that the computed matrix factorization is uni...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	428,121
2301.07057	Transformer Based Implementation for Automatic Book Summarization	Document Summarization is the procedure of generating a meaningful and concise summary of a given document with the inclusion of relevant and topic-important points. There are two approaches: one is picking up the most relevant statements from the document itself and adding it to the Summary known as Extractive and the...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	340,817
1312.3496	Memory effects induce structure in social networks with activity-driven agents	Activity-driven modeling has been recently proposed as an alternative growth mechanism for time varying networks, displaying power-law degree distribution in time-aggregated representation. This approach assumes memoryless agents developing random connections, thus leading to random networks that fail to reproduce two-...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	29,044
2008.06244	Cooperative Multi-Agent Bandits with Heavy Tails	We study the heavy-tailed stochastic bandit problem in the cooperative multi-agent setting, where a group of agents interact with a common bandit problem, while communicating on a network with delays. Existing algorithms for the stochastic bandit in this setting utilize confidence intervals arising from an averaging-ba...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	191,745
2104.01874	Deep Learning of Conjugate Mappings	Despite many of the most common chaotic dynamical systems being continuous in time, it is through discrete time mappings that much of the understanding of chaos is formed. Henri Poincar\'e first made this connection by tracking consecutive iterations of the continuous flow with a lower-dimensional, transverse subspace....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	228,510
2403.01181	Shaping Multi-Robot Patrol Performance with Heterogeneity in Individual Learning Behavior	Individual differences in learning behavior within social groups, whether in humans, other animals, or among robots, can have significant effects on collective task performance. This is because it can affect individuals' response to the environment and their interactions with each other. In recent years there has been ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	434,290
2205.11257	Manifold-aligned Neighbor Embedding	In this paper, we introduce a neighbor embedding framework for manifold alignment. We demonstrate the efficacy of the framework using a manifold-aligned version of the uniform manifold approximation and projection algorithm. We show that our algorithm can learn an aligned manifold that is visually competitive to embedd...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	298,074
2204.07406	SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard Example Focusing	Crowd counting based on density maps is generally regarded as a regression task.Deep learning is used to learn the mapping between image content and crowd density distribution. Although great success has been achieved, some pedestrians far away from the camera are difficult to be detected. And the number of hard exampl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	291,687
2308.14992	Satellite-MEC Integration for 6G Internet of Things: Minimal Structures, Advances, and Prospects	The sixth-generation (6G) network is envisioned to shift its focus from the service requirements of human beings' to those of Internet-of-Things (IoT) devices'. Satellite communications are indispensable in 6G to support IoT devices operating in rural or disastrous areas. However, satellite networks face the inherent c...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	388,522
2011.11007	SAMA-VTOL: A new unmanned aircraft system for remotely sensed data collection	In recent years, unmanned aircraft systems (UASs) are frequently used in many different applications of photogrammetry such as building damage monitoring, archaeological mapping and vegetation monitoring. In this paper, a new state-of-the-art vertical take-off and landing fixed-wing UAS is proposed to robust photogramm...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	207,692
2108.01794	Explicit RIP matrices: an update	Leveraging recent advances in additive combinatorics, we exhibit explicit matrices satisfying the Restricted Isometry Property with better parameters. Namely, for $\varepsilon=3.26\cdot 10^{-7}$, large $k$ and $k^{2-\varepsilon} \le N\le k^{2+\varepsilon}$, we construct $n \times N$ RIP matrices of order $k$ with $k = ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	249,126
2311.11704	Demonstrating Almost Linear Time Complexity of Bus Admittance Matrix-Based Distribution Network Power Flow: An Empirical Approach	The bus admittance matrix is central to many power system simulation algorithms, but the link between problem size and computation time (i.e., the time complexity) using modern sparse solvers is not fully understood. It has recently been suggested that some popular algorithms used in distribution system power flow anal...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	409,050
2201.05759	FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes	Most fair machine learning methods either highly rely on the sensitive information of the training samples or require a large modification on the target models, which hinders their practical application. To address this issue, we propose a two-stage training algorithm named FAIRIF. It minimizes the loss over the reweig...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	275,483
2007.13798	Linguistic Taboos and Euphemisms in Nepali	Languages across the world have words, phrases, and behaviors -- the taboos -- that are avoided in public communication considering them as obscene or disturbing to the social, religious, and ethical values of society. However, people deliberately use these linguistic taboos and other language constructs to make hurtfu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	189,219
2307.09323	Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis	This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based architecture for talking portrait synthesis that can concurrently achieve fast convergence, real-time rendering, and state-of-the-art performance with small model size. Our idea is to explicitly exploit the unequal contribution of spat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	380,134
2405.15786	Enhancement of Subjective Content Descriptions by using Human Feedback	An agent providing an information retrieval service may work with a corpus of text documents. The documents in the corpus may contain annotations such as Subjective Content Descriptions (SCD) -- additional data associated with different sentences of the documents. Each SCD is associated with multiple sentences of the c...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	457,099
2311.17447	Learning-driven Zero Trust in Distributed Computing Continuum Systems	Converging Zero Trust (ZT) with learning techniques can solve various operational and security challenges in Distributed Computing Continuum Systems (DCCS). Implementing centralized ZT architecture is seen as unsuitable for the computing continuum (e.g., computing entities with limited connectivity and visibility, etc....	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	411,301
2404.01328	WhatsApp Explorer: A Data Donation Tool To Facilitate Research on WhatsApp	In recent years, reports and anecdotal evidence pointing at the role of WhatsApp in a variety of events, ranging from elections to collective violence, have emerged. While academic research should examine the validity of these claims, obtaining WhatsApp data for research is notably challenging, contrasting with the rel...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	443,373
0811.4200	Two Models for Noisy Feedback in MIMO Channels	Two distinct models of feedback, suited for FDD (Frequency Division Duplex) and TDD (Frequency Division Duplex) systems respectively, have been widely studied in the literature. In this paper, we compare these two models of feedback in terms of the diversity multiplexing tradeoff for varying amount of channel state inf...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,700
1211.6687	Robustness Analysis of Hottopixx, a Linear Programming Model for Factoring Nonnegative Matrices	Although nonnegative matrix factorization (NMF) is NP-hard in general, it has been shown very recently that it is tractable under the assumption that the input nonnegative data matrix is close to being separable (separability requires that all columns of the input matrix belongs to the cone spanned by a small subset of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	20,001
1611.04499	Post Training in Deep Learning with Last Kernel	One of the main challenges of deep learning methods is the choice of an appropriate training strategy. In particular, additional steps, such as unsupervised pre-training, have been shown to greatly improve the performances of deep structures. In this article, we propose an extra training step, called post-training, whi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	63,853
1605.00029	Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies	Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring onl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	55,283
2212.10815	ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models	We explore the use of large language models (LLMs) for zero-shot semantic parsing. Semantic parsing involves mapping natural language utterances to task-specific meaning representations. Language models are generally trained on the publicly available text and code and cannot be expected to directly generalize to domain...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	337,621
1705.00986	Stochastic Geometric Coverage Analysis in mmWave Cellular Networks with a Realistic Channel Model	Millimeter-wave (mmWave) bands have been attracting growing attention as a possible candidate for next-generation cellular networks, since the available spectrum is orders of magnitude larger than in current cellular allocations. To precisely design mmWave systems, it is important to examine mmWave interference and SIR...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	72,778
2008.00199	Green Offloading in Fog-Assisted IoT Systems: An Online Perspective Integrating Learning and Control	In fog-assisted IoT systems, it is a common practice to offload tasks from IoT devices to their nearby fog nodes to reduce task processing latencies and energy consumptions. However, the design of online energy-efficient scheme is still an open problem because of various uncertainties in system dynamics such as process...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	189,939
2205.00301	ONCE-3DLanes: Building Monocular 3D Lane Detection	We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space. Conventional 2D lane detection from a monocular image yields poor performance of following planning and control tasks in autonomous driving due to the case of uneven road. Predicting the 3D lane layout is thus nece...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	294,203
1706.00587	Learning-based Surgical Workflow Detection from Intra-Operative Signals	A modern operating room (OR) provides a plethora of advanced medical devices. In order to better facilitate the information offered by them, they need to automatically react to the intra-operative context. To this end, the progress of the surgical workflow must be detected and interpreted, so that the current status ca...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	74,648
1703.00800	Creative Community Demystified: A Statistical Overview of Behance	Online communities are changing the ways that creative professionals such as artists and designers share ideas, receive feedback, and find inspiration. While they became increasingly popular, there have been few studies so far. In this paper, we investigate Behance, an online community site for creatives to maintain re...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	69,231
2306.08732	A Fluid-Solid-Growth Solver for Cardiovascular Modeling	We implement full, three-dimensional constrained mixture theory for vascular growth and remodeling into a finite element fluid-structure interaction (FSI) solver. The resulting "fluid-solid-growth" (FSG) solver allows long term, patient-specific predictions of changing hemodynamics, vessel wall morphology, tissue compo...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	373,515
2402.01201	Few-Shot Class-Incremental Learning with Prior Knowledge	To tackle the issues of catastrophic forgetting and overfitting in few-shot class-incremental learning (FSCIL), previous work has primarily concentrated on preserving the memory of old knowledge during the incremental phase. The role of pre-trained model in shaping the effectiveness of incremental learning is frequentl...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	425,909
1903.03699	Joint Inference of Kinematic and Force Trajectories with Visuo-Tactile Sensing	To perform complex tasks, robots must be able to interact with and manipulate their surroundings. One of the key challenges in accomplishing this is robust state estimation during physical interactions, where the state involves not only the robot and the object being manipulated, but also the state of the contact itsel...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	123,793
1212.2345	Enhanced Mobile Digital Video Broadcasting with Distributed Space-Time Coding	This paper investigates the distributed space-time (ST) coding proposals for the future Digital Video Broadcasting--Next Generation Handheld (DVB-NGH) standard. We first theoretically show that the distributed MIMO scheme is the best broadcasting scenario in terms of channel capacity. Consequently we evaluate the perfo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	20,243

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