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
2304.11739	Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement Learning	In deductive domains, three metacognitive knowledge types in ascending order are declarative, procedural, and conditional learning. This work leverages Deep Reinforcement Learning (DRL) in providing adaptive metacognitive interventions to bridge the gap between the three knowledge types and prepare students for future ...	true	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	true	359,946
2003.03501	Cross-modal Learning for Multi-modal Video Categorization	Multi-modal machine learning (ML) models can process data in multiple modalities (e.g., video, audio, text) and are useful for video content analysis in a variety of problems (e.g., object detection, scene understanding, activity recognition). In this paper, we focus on the problem of video categorization using a multi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	167,245
2312.14301	Autoencoder Based Face Verification System	The primary objective of this work is to present an alternative approach aimed at reducing the dependency on labeled data. Our proposed method involves utilizing autoencoder pre-training within a face image recognition task with two step processes. Initially, an autoencoder is trained in an unsupervised manner using a ...	false	false	false	false	true	false	false	false	false	false	false	true	false	true	false	false	false	false	417,580
2106.00055	More than just Frequency? Demasking Unsupervised Hypernymy Prediction Methods	This paper presents a comparison of unsupervised methods of hypernymy prediction (i.e., to predict which word in a pair of words such as fish-cod is the hypernym and which the hyponym). Most importantly, we demonstrate across datasets for English and for German that the predictions of three methods (WeedsPrec, invCL, S...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	237,965
2310.08756	Intelligent Scoliosis Screening and Diagnosis: A Survey	Scoliosis is a three-dimensional spinal deformity, which may lead to abnormal morphologies, such as thoracic deformity, and pelvic tilt. Severe patients may suffer from nerve damage and urinary abnormalities. At present, the number of scoliosis patients in primary and secondary schools has exceeded five million in Chin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	399,517
1508.00545	Connectivity in Secure Wireless Sensor Networks under Transmission Constraints	In wireless sensor networks (WSNs), the Eschenauer-Gligor (EG) key pre-distribution scheme is a widely recognized way to secure communications. Although connectivity properties of secure WSNs with the EG scheme have been extensively investigated, few results address physical transmission constraints. These constraints ...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	true	45,688
2109.04945	WikiCSSH: Extracting and Evaluating Computer Science Subject Headings from Wikipedia	Hierarchical domain-specific classification schemas (or subject heading vocabularies) are often used to identify, classify, and disambiguate concepts that occur in scholarly articles. In this work, we develop, apply, and evaluate a human-in-the-loop workflow that first extracts an initial category tree from crowd-sourc...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	254,602
2107.01729	Hebbian learning with gradients: Hebbian convolutional neural networks with modern deep learning frameworks	Deep learning networks generally use non-biological learning methods. By contrast, networks based on more biologically plausible learning, such as Hebbian learning, show comparatively poor performance and difficulties of implementation. Here we show that Hebbian learning in hierarchical, convolutional neural networks c...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	244,576
2410.01570	Truncated Kernel Stochastic Gradient Descent on Spheres	Inspired by the structure of spherical harmonics, we propose the truncated kernel stochastic gradient descent (T-kernel SGD) algorithm with a least-square loss function for spherical data fitting. T-kernel SGD employs a "truncation" operation, enabling the application of series-based kernels function in stochastic grad...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	493,828
2007.01228	MAESTRO-X: Distributed Orchestration of Rotary-Wing UAV-Relay Swarms	This work details a scalable framework to orchestrate a swarm of rotary-wing UAVs serving as cellular relays to facilitate beyond line-of-sight connectivity and traffic offloading for ground users. First, a Multiscale Adaptive Energy-conscious Scheduling and TRajectory Optimization (MAESTRO) framework is developed for ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	185,365
2404.19148	Enhancing Brazilian Sign Language Recognition through Skeleton Image Representation	Effective communication is paramount for the inclusion of deaf individuals in society. However, persistent communication barriers due to limited Sign Language (SL) knowledge hinder their full participation. In this context, Sign Language Recognition (SLR) systems have been developed to improve communication between sig...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	450,523
2104.02538	Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision	Visual re-localization means using a single image as input to estimate the camera's location and orientation relative to a pre-recorded environment. The highest-scoring methods are "structure based," and need the query camera's intrinsics as an input to the model, with careful geometric optimization. When intrinsics ar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	228,753
1604.08692	Optimal data recovery and forecasting with dummy long-horizon forecasts	The paper suggests a method of recovering missing values for sequences, including sequences with a multidimensional index, based on optimal approximation by processes featuring spectrum degeneracy. The problem is considered in the pathwise setting, without using probabilistic assumptions on the ensemble. The method req...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	55,241
1909.01767	Modular Modeling and Optimized Scheduling of Building Energy Systems Based on Mixed Integer Programming	Almost climate neutral buildings are one of the core goals in terms of sustainability. Beside the support of the necessary design decisions for an integrated, interoperable, ecological and economical operation of building energy systems, innovative management solutions for scheduling the operation of decentralized ener...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	143,997
1809.04497	Hyperprior Induced Unsupervised Disentanglement of Latent Representations	We address the problem of unsupervised disentanglement of latent representations learnt via deep generative models. In contrast to current approaches that operate on the evidence lower bound (ELBO), we argue that statistical independence in the latent space of VAEs can be enforced in a principled hierarchical Bayesian ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	107,583
2209.13426	From Ranked Lists to Carousels: A Carousel Click Model	Carousel-based recommendation interfaces allow users to explore recommended items in a structured, efficient, and visually-appealing way. This made them a de-facto standard approach to recommending items to end users in many real-life recommenders. In this work, we try to explain the efficiency of carousel recommenders...	true	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	319,888
2302.12716	Supervised Hierarchical Clustering using Graph Neural Networks for Speaker Diarization	Conventional methods for speaker diarization involve windowing an audio file into short segments to extract speaker embeddings, followed by an unsupervised clustering of the embeddings. This multi-step approach generates speaker assignments for each segment. In this paper, we propose a novel Supervised HierArchical gRa...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	347,673
1811.09038	Super Diffusion for Salient Object Detection	One major branch of saliency object detection methods is diffusion-based which construct a graph model on a given image and diffuse seed saliency values to the whole graph by a diffusion matrix. While their performance is sensitive to specific feature spaces and scales used for the diffusion matrix definition, little w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	114,181
2402.17245	Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation	In this work, we share three insights for achieving state-of-the-art aesthetic quality in text-to-image generative models. We focus on three critical aspects for model improvement: enhancing color and contrast, improving generation across multiple aspect ratios, and improving human-centric fine details. First, we delve...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	432,910
2306.09351	BN-DRISHTI: Bangla Document Recognition through Instance-level Segmentation of Handwritten Text Images	Handwriting recognition remains challenging for some of the most spoken languages, like Bangla, due to the complexity of line and word segmentation brought by the curvilinear nature of writing and lack of quality datasets. This paper solves the segmentation problem by introducing a state-of-the-art method (BN-DRISHTI) ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	373,795
2412.01928	MALT: Improving Reasoning with Multi-Agent LLM Training	Enabling effective collaboration among LLMs is a crucial step toward developing autonomous systems capable of solving complex problems. While LLMs are typically used as single-model generators, where humans critique and refine their outputs, the potential for jointly-trained collaborative models remains largely unexplo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	513,306
2107.11196	Multi-Modal Pedestrian Detection with Large Misalignment Based on Modal-Wise Regression and Multi-Modal IoU	The combined use of multiple modalities enables accurate pedestrian detection under poor lighting conditions by using the high visibility areas from these modalities together. The vital assumption for the combination use is that there is no or only a weak misalignment between the two modalities. In general, however, th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	247,533
1806.01340	Design of optimal illumination patterns in single-pixel imaging using image dictionaries	Single-pixel imaging (SPI) has a major drawback that many sequential illuminations are required for capturing one single image with long acquisition time. Basis illumination patterns such as Fourier patterns and Hadamard patterns can achieve much better imaging efficiency than random patterns. But the performance is st...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	99,532
1412.5207	Are We Ready for Driver-less Vehicles? Security vs. Privacy- A Social Perspective	At this moment Autonomous cars are probably the biggest and most talked about technology in the Robotics Research Community. In spite of great technological advances over past few years a full edged autonomous car is still far from reality. This article talks about the existing system and discusses the possibility of a...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	38,460
1703.03385	Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis	The definition of similarity is a key prerequisite when analyzing complex data types in data mining, information retrieval, or machine learning. However, the meaningful definition is often hampered by the complexity of data objects and particularly by different notions of subjective similarity latent in targeted user g...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	69,724
2409.16441	A novel open-source ultrasound dataset with deep learning benchmarks for spinal cord injury localization and anatomical segmentation	While deep learning has catalyzed breakthroughs across numerous domains, its broader adoption in clinical settings is inhibited by the costly and time-intensive nature of data acquisition and annotation. To further facilitate medical machine learning, we present an ultrasound dataset of 10,223 Brightness-mode (B-mode) ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	491,342
1412.1283	Convolutional Feature Masking for Joint Object and Stuff Segmentation	The topic of semantic segmentation has witnessed considerable progress due to the powerful features learned by convolutional neural networks (CNNs). The current leading approaches for semantic segmentation exploit shape information by extracting CNN features from masked image regions. This strategy introduces artificia...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	38,093
2405.15299	Transparent Object Depth Completion	The perception of transparent objects for grasp and manipulation remains a major challenge, because existing robotic grasp methods which heavily rely on depth maps are not suitable for transparent objects due to their unique visual properties. These properties lead to gaps and inaccuracies in the depth maps of the tran...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	456,856
1707.04820	Development of Direct Kinematics and Workspace Representation for Smokie Robot Manipulator & the Barret WAM	This paper discusses modelling two 6 DOF arm robots. The first step of modelling a robot is establishing its Denavit-Hartenberg parameters. It requires assigning proper coordinates for each link and finding their exact dimensions. In this project we will develop the direct kinematics and workspace representations for t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	77,105
2311.03122	CISRU: a robotics software suite to enable complex rover-rover and astronaut-rover interaction	The CISRU project has focused on the development of a software suite for planetary (and terrestrial) robotics, fully abstracted from the robotic platform and enabling interaction between rovers and astronauts in complex tasks and non-structured scenarios. To achieve this, a high level of autonomy is required, powered b...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	405,722
2010.03662	Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank	Detecting fine-grained differences in content conveyed in different languages matters for cross-lingual NLP and multilingual corpora analysis, but it is a challenging machine learning problem since annotation is expensive and hard to scale. This work improves the prediction and annotation of fine-grained semantic diver...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	199,478
2407.10233	Visual Prompt Selection for In-Context Learning Segmentation	As a fundamental and extensively studied task in computer vision, image segmentation aims to locate and identify different semantic concepts at the pixel level. Recently, inspired by In-Context Learning (ICL), several generalist segmentation frameworks have been proposed, providing a promising paradigm for segmenting s...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	472,893
1805.08736	Adversarially Robust Training through Structured Gradient Regularization	We propose a novel data-dependent structured gradient regularizer to increase the robustness of neural networks vis-a-vis adversarial perturbations. Our regularizer can be derived as a controlled approximation from first principles, leveraging the fundamental link between training with noise and regularization. It adds...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	98,241
2409.12467	SurgPLAN++: Universal Surgical Phase Localization Network for Online and Offline Inference	Surgical phase recognition is critical for assisting surgeons in understanding surgical videos. Existing studies focused more on online surgical phase recognition, by leveraging preceding frames to predict the current frame. Despite great progress, they formulated the task as a series of frame-wise classification, whic...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	489,605
1910.01493	From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition	There is an implicit assumption that traditional hybrid approaches for automatic speech recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to get competitive performance, especially on English which has poor grapheme-phoneme correspondence. In this work, we show for the first time t...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,958
1702.08415	An SDP-Based Algorithm for Linear-Sized Spectral Sparsification	For any undirected and weighted graph $G=(V,E,w)$ with $n$ vertices and $m$ edges, we call a sparse subgraph $H$ of $G$, with proper reweighting of the edges, a $(1+\varepsilon)$-spectral sparsifier if \[ (1-\varepsilon)x^{\intercal}L_Gx\leq x^{\intercal} L_{H} x\leq (1+\varepsilon) x^{\intercal} L_Gx \] holds for any ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	68,980
1808.02861	Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance	Individual neurons in convolutional neural networks supervised for image-level classification tasks have been shown to implicitly learn semantically meaningful concepts ranging from simple textures and shapes to whole or partial objects - forming a "dictionary" of concepts acquired through the learning process. In this...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,840
2207.09336	Uncertainty in Contrastive Learning: On the Predictability of Downstream Performance	The superior performance of some of today's state-of-the-art deep learning models is to some extent owed to extensive (self-)supervised contrastive pretraining on large-scale datasets. In contrastive learning, the network is presented with pairs of positive (similar) and negative (dissimilar) datapoints and is trained ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	308,873
1303.5492	Sample Distortion for Compressed Imaging	We propose the notion of a sample distortion (SD) function for independent and identically distributed (i.i.d) compressive distributions to fundamentally quantify the achievable reconstruction performance of compressed sensing for certain encoder-decoder pairs at a given sampling ratio. Two lower bounds on the achievab...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	23,134
2408.12086	Unlocking Attributes' Contribution to Successful Camouflage: A Combined Textual and VisualAnalysis Strategy	In the domain of Camouflaged Object Segmentation (COS), despite continuous improvements in segmentation performance, the underlying mechanisms of effective camouflage remain poorly understood, akin to a black box. To address this gap, we present the first comprehensive study to examine the impact of camouflage attribut...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	482,576
2207.08389	MLGOPerf: An ML Guided Inliner to Optimize Performance	For the past 25 years, we have witnessed an extensive application of Machine Learning to the Compiler space; the selection and the phase-ordering problem. However, limited works have been upstreamed into the state-of-the-art compilers, i.e., LLVM, to seamlessly integrate the former into the optimization pipeline of a c...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	true	308,575
2105.03905	Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam Prediction	6G -- sixth generation -- is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous car, and many more. Those algorithms have been also ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	234,315
2502.09473	Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements	Catheter ablation of Atrial Fibrillation (AF) consists of a one-size-fits-all treatment with limited success in persistent AF. This may be due to our inability to map the dynamics of AF with the limited resolution and coverage provided by sequential contact mapping catheters, preventing effective patient phenotyping fo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	533,457
2303.13056	Predicting the Initial Conditions of the Universe using a Deterministic Neural Network	Finding the initial conditions that led to the current state of the universe is challenging because it involves searching over an intractable input space of initial conditions, along with modeling their evolution via tools such as N-body simulations which are computationally expensive. Recently, deep learning has emerg...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	353,526
2408.09330	Fostering Natural Conversation in Large Language Models with NICO: a Natural Interactive COnversation dataset	Benefiting from diverse instruction datasets, contemporary Large Language Models (LLMs) perform effectively as AI assistants in collaborating with humans. However, LLMs still struggle to generate natural and colloquial responses in real-world applications such as chatbots and psychological counseling that require more ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	481,383
2310.02635	Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own	Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks. However, it is challenging to apply the RL algorithms directly in the real world. For one thing, RL is data-intensive and typically requires millions of interactions with environments, which are impractical in real scenarios. Fo...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	396,934
2111.12306	Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability	We study the $K$-armed contextual dueling bandit problem, a sequential decision making setting in which the learner uses contextual information to make two decisions, but only observes \emph{preference-based feedback} suggesting that one decision was better than the other. We focus on the regret minimization problem un...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	267,929
2501.17144	FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data	Prior research on training grounded factuality classification models to detect hallucinations in large language models (LLMs) has relied on public natural language inference (NLI) data and synthetic data. However, conventional NLI datasets are not well-suited for document-level reasoning, which is critical for detectin...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	528,223
2407.09083	BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge Distillation	Spiking neural networks (SNNs), which mimic biological neural system to convey information via discrete spikes, are well known as brain-inspired models with excellent computing efficiency. By utilizing the surrogate gradient estimation for discrete spikes, learning-based SNN training methods that can achieve ultra-low ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	472,446
1410.2698	Technical Report: Towards Efficient Indexing of Spatiotemporal Trajectories on the GPU for Distance Threshold Similarity Searches	Applications in many domains require processing moving object trajectories. In this work, we focus on a trajectory similarity search that finds all trajectories within a given distance of a query trajectory over a time interval, which we call the distance threshold similarity search. We develop three indexing strategie...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	36,640
2401.02416	ODIN: A Single Model for 2D and 3D Segmentation	State-of-the-art models on contemporary 3D segmentation benchmarks like ScanNet consume and label dataset-provided 3D point clouds, obtained through post processing of sensed multiview RGB-D images. They are typically trained in-domain, forego large-scale 2D pre-training and outperform alternatives that featurize the p...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	419,708
2408.15305	Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis	Semiconductors, crucial to modern electronics, are generally under-researched in foundational models. It highlights the need for research to enhance the semiconductor device technology portfolio and aid in high-end device fabrication. In this paper, we introduce sLAVA, a small-scale vision-language assistant tailored f...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	483,893
2502.04076	Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware Consistency	The advent of next-generation video generation models like \textit{Sora} poses challenges for AI-generated content (AIGC) video quality assessment (VQA). These models substantially mitigate flickering artifacts prevalent in prior models, enable longer and complex text prompts and generate longer videos with intricate, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	530,977
2404.04225	Twins in rotational spectroscopy: Does a rotational spectrum uniquely identify a molecule?	Rotational spectroscopy is the most accurate method for determining structures of molecules in the gas phase. It is often assumed that a rotational spectrum is a unique "fingerprint" of a molecule. The availability of large molecular databases and the development of artificial intelligence methods for spectroscopy make...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	444,552
2408.04671	Prompt and Prejudice	This paper investigates the impact of using first names in Large Language Models (LLMs) and Vision Language Models (VLMs), particularly when prompted with ethical decision-making tasks. We propose an approach that appends first names to ethically annotated text scenarios to reveal demographic biases in model outputs. O...	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	479,482
2008.09100	The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems	Domain-specific intelligent systems are meant to help system users in their decision-making process. Many systems aim to simultaneously support different users with varying levels of domain expertise, but prior domain knowledge can affect user trust and confidence in detecting system errors. While it is also known that...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	192,612
1902.00282	Understanding MCMC Dynamics as Flows on the Wasserstein Space	It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particle-based variational inference methods (ParVIs). But no more MCMC dynamics is understood in this way. In this work, by developing novel conce...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,370
2406.14340	Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses	It is known that the standard stochastic gradient descent (SGD) optimization method, as well as accelerated and adaptive SGD optimization methods such as the Adam optimizer fail to converge if the learning rates do not converge to zero (as, for example, in the situation of constant learning rates). Numerical simulation...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	466,270
2311.14225	An Agent-Based Discrete Event Simulation of Teleoperated Driving in Freight Transport Operations: The Fleet Sizing Problem	Teleoperated or remote-controlled driving complements automated driving and acts as transitional technology toward full automation. An economic advantage of teleoperated driving in logistics operations lies in managing fleets with fewer teleoperators compared to vehicles with in-vehicle drivers. This alleviates growing...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	410,038
2202.04147	The Rate-Distortion-Perception Tradeoff: The Role of Common Randomness	A rate-distortion-perception (RDP) tradeoff has recently been proposed by Blau and Michaeli and also Matsumoto. Focusing on the case of perfect realism, which coincides with the problem of distribution-preserving lossy compression studied by Li et al., a coding theorem for the RDP tradeoff that allows for a specified a...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	279,469
2501.02880	Conditional Mutual Information Based Diffusion Posterior Sampling for Solving Inverse Problems	Inverse problems are prevalent across various disciplines in science and engineering. In the field of computer vision, tasks such as inpainting, deblurring, and super-resolution are commonly formulated as inverse problems. Recently, diffusion models (DMs) have emerged as a promising approach for addressing noisy linear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	522,675
1805.09086	Instability in Stable Marriage Problem: Matching Unequally Numbered Men and Women	The Stable Marriage Problem is to find a one-to-one matching for two equally sized sets of agents. Due to its widespread applications in the real world, especially the unique importance to the centralized match maker, a very large number of questions have been extensively studied in this field. This article considers a...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	98,336
2310.03675	Hadamard Domain Training with Integers for Class Incremental Quantized Learning	Continual learning is a desirable feature in many modern machine learning applications, which allows in-field adaptation and updating, ranging from accommodating distribution shift, to fine-tuning, and to learning new tasks. For applications with privacy and low latency requirements, the compute and memory demands impo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	397,372
2312.11663	Eliciting Kemeny Rankings	We formulate the problem of eliciting agents' preferences with the goal of finding a Kemeny ranking as a Dueling Bandits problem. Here the bandits' arms correspond to alternatives that need to be ranked and the feedback corresponds to a pairwise comparison between alternatives by a randomly sampled agent. We consider b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,671
1012.2283	Artifacts of opinion dynamics at one dimension	The dynamics of a one dimensional Ising spin system is investigated using three families of local update rules, the Galam majority rules, Glauber inflow influences and Sznadj outflow drives. Given an initial density p of up spins the probability to reach a final state with all spins up is calculated exactly for each ch...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	8,496
2210.12553	Understanding Domain Learning in Language Models Through Subpopulation Analysis	We investigate how different domains are encoded in modern neural network architectures. We analyze the relationship between natural language domains, model size, and the amount of training data used. The primary analysis tool we develop is based on subpopulation analysis with Singular Vector Canonical Correlation Anal...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	325,792
1909.09264	Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing	Are two sets of observations drawn from the same distribution? This problem is a two-sample test. Kernel methods lead to many appealing properties. Indeed state-of-the-art approaches use the $L^2$ distance between kernel-based distribution representatives to derive their test statistics. Here, we show that $L^p$ distan...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,203
2307.03487	Learning Theory of Distribution Regression with Neural Networks	In this paper, we aim at establishing an approximation theory and a learning theory of distribution regression via a fully connected neural network (FNN). In contrast to the classical regression methods, the input variables of distribution regression are probability measures. Then we often need to perform a second-stag...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	378,058
2304.03531	From Retrieval to Generation: Efficient and Effective Entity Set Expansion	Entity Set Expansion (ESE) is a critical task aiming at expanding entities of the target semantic class described by seed entities. Most existing ESE methods are retrieval-based frameworks that need to extract contextual features of entities and calculate the similarity between seed entities and candidate entities. To ...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	356,848
1209.6217	An Evolving model of online bipartite networks	Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions.However, many emp...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	18,799
2205.14508	Core-set Selection Using Metrics-based Explanations (CSUME) for multiclass ECG	The adoption of deep learning-based healthcare decision support systems such as the detection of irregular cardiac rhythm is hindered by challenges such as lack of access to quality data and the high costs associated with the collection and annotation of data. The collection and processing of large volumes of healthcar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	299,389
2301.10810	On the inconsistency of separable losses for structured prediction	In this paper, we prove that separable negative log-likelihood losses for structured prediction are not necessarily Bayes consistent, or, in other words, minimizing these losses may not result in a model that predicts the most probable structure in the data distribution for a given input. This fact opens the question o...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	341,912
1407.2697	A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation	A key problem in statistics and machine learning is the determination of network structure from data. We consider the case where the structure of the graph to be reconstructed is known to be scale-free. We show that in such cases it is natural to formulate structured sparsity inducing priors using submodular functions,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	34,552
2007.04630	Maximum-and-Concatenation Networks	While successful in many fields, deep neural networks (DNNs) still suffer from some open problems such as bad local minima and unsatisfactory generalization performance. In this work, we propose a novel architecture called Maximum-and-Concatenation Networks (MCN) to try eliminating bad local minima and improving genera...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,421
2202.08021	Toward Development of Machine Learned Techniques for Production of Compact Kinetic Models	Chemical kinetic models are an essential component in the development and optimisation of combustion devices through their coupling to multi-dimensional simulations such as computational fluid dynamics (CFD). Low-dimensional kinetic models which retain good fidelity to the reality are needed, the production of which re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	280,738
2302.03530	Understanding the Loss in Community Resilience due to Hurricanes using Facebook Data	Significant negative impacts are observed in productivity, economy, and social wellbeing because of the reduced human activity due to extreme events. Community resilience is an important and widely used concept to understand the impacts of an extreme event to population activity. Resilience is generally defined as the ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	344,374
1801.03237	Symmetry reduction for dynamic programming	We present a method of exploiting symmetries of discrete-time optimal control problems to reduce the dimensionality of dynamic programming iterations. The results are derived for systems with continuous state variables, and can be applied to systems with continuous or discrete symmetry groups. We prove that symmetries ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	88,061
2309.05490	Learning Semantic Segmentation with Query Points Supervision on Aerial Images	Semantic segmentation is crucial in remote sensing, where high-resolution satellite images are segmented into meaningful regions. Recent advancements in deep learning have significantly improved satellite image segmentation. However, most of these methods are typically trained in fully supervised settings that require ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	391,103
0801.3024	Construction of Z4-linear Reed-Muller codes	New quaternary Plotkin constructions are given and are used to obtain new families of quaternary codes. The parameters of the obtained codes, such as the length, the dimension and the minimum distance are studied. Using these constructions new families of quaternary Reed-Muller codes are built with the peculiarity that...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,173
1910.00668	Wasserstein Neural Processes	Neural Processes (NPs) are a class of models that learn a mapping from a context set of input-output pairs to a distribution over functions. They are traditionally trained using maximum likelihood with a KL divergence regularization term. We show that there are desirable classes of problems where NPs, with this loss, f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,722
1110.0748	Compress-Forward without Wyner-Ziv Binning for the One-Way and Two-Way Relay Channels	We consider the role of Wyner-Ziv binning in compress-forward for relay channels. In the one-way relay channel, we analyze a compress-forward scheme without Wyner- Ziv binning but with joint decoding of both the message and compression index. It achieves the same rate as the original compress-forward scheme with binnin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	12,482
2003.01228	The Use of Implicit Human Motor Behaviour in the Online Personalisation of Prosthetic Interfaces	In previous work, the authors proposed a data-driven optimisation algorithm for the personalisation of human-prosthetic interfaces, demonstrating the possibility of adapting prosthesis behaviour to its user while the user performs tasks with it. This method requires that the human and the prosthesis personalisation alg...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	166,577
2405.17811	Mani-GS: Gaussian Splatting Manipulation with Triangular Mesh	Neural 3D representations such as Neural Radiance Fields (NeRF), excel at producing photo-realistic rendering results but lack the flexibility for manipulation and editing which is crucial for content creation. Previous works have attempted to address this issue by deforming a NeRF in canonical space or manipulating th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	458,127
2401.02794	Subjective and Objective Analysis of Indian Social Media Video Quality	We conducted a large-scale subjective study of the perceptual quality of User-Generated Mobile Video Content on a set of mobile-originated videos obtained from the Indian social media platform ShareChat. The content viewed by volunteer human subjects under controlled laboratory conditions has the benefit of culturally ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	419,848
2106.02900	Differentially Private Multi-Armed Bandits in the Shuffle Model	We give an $(\varepsilon,\delta)$-differentially private algorithm for the multi-armed bandit (MAB) problem in the shuffle model with a distribution-dependent regret of $O\left(\left(\sum_{a\in [k]:\Delta_a>0}\frac{\log T}{\Delta_a}\right)+\frac{k\sqrt{\log\frac{1}{\delta}}\log T}{\varepsilon}\right)$, and a distributi...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	239,085
2308.08074	Real-Time Numerical Differentiation of Sampled Data Using Adaptive Input and State Estimation	Real-time numerical differentiation plays a crucial role in many digital control algorithms, such as PID control, which requires numerical differentiation to implement derivative action. This paper addresses the problem of numerical differentiation for real-time implementation with minimal prior information about the s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	385,754
2203.00112	GraphWorld: Fake Graphs Bring Real Insights for GNNs	Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets are currently used to evaluate new models. This continued reliance on a handful of datasets provides minimal insight into the performance differences between models, and is especially challenging for industrial practitio...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,872
2308.10253	StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data	The remarkable multimodal capabilities demonstrated by OpenAI's GPT-4 have sparked significant interest in the development of multimodal Large Language Models (LLMs). A primary research objective of such models is to align visual and textual modalities effectively while comprehending human instructions. Current methodo...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	386,639
2303.01234	Frauds Bargain Attack: Generating Adversarial Text Samples via Word Manipulation Process	Recent research has revealed that natural language processing (NLP) models are vulnerable to adversarial examples. However, the current techniques for generating such examples rely on deterministic heuristic rules, which fail to produce optimal adversarial examples. In response, this study proposes a new method called ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	348,883
1909.03826	SRIM and SCRIM Factors of $x^n+1$ over Finite Fields and Their Applications	Self-Reciprocal Irreducible Monic (SRIM) and Self-Conjugate-Reciprocal Irreducible Monic (SRCIM) factors of $x^n-1$ over finite fields have become of interest due to their rich algebraic structures and wide applications. In this paper, these notions are extended to factors of $x^n+ 1$ over finite fields. Characterizati...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	144,614
2301.12206	Semantic Tagging with LSTM-CRF	In the present paper, two models are presented namely LSTM-CRF and BERT-LSTM-CRF for semantic tagging of universal semantic tag dataset. The experiments show that the first model is much easier to converge while the second model that leverages BERT embedding, takes a long time to converge and needs a big dataset for se...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	342,441
2010.08008	Risk-Informed Participation in T&D Markets	Power producers can exhibit strategic behavior in electricity markets to maximize their profits. This behavior is more pronounced with the deregulation of distribution markets, which offers an opportunity for profit arbitrage between transmission and distribution (T&D) markets. However, the temporally distinct nature o...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	201,021
2210.06066	On the Optimality of Coded Caching With Heterogeneous User Profiles	In this paper, we consider a coded caching scenario where users have heterogeneous interests. Taking into consideration the system model originally proposed by Wang and Peleato, for which the end-receiving users are divided into groups according to their file preferences, we develop a novel information-theoretic conver...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	323,127
2012.02234	COVID-CLNet: COVID-19 Detection with Compressive Deep Learning Approaches	One of the most serious global health threat is COVID-19 pandemic. The emphasis on improving diagnosis and increasing the diagnostic capability helps stopping its spread significantly. Therefore, to assist the radiologist or other medical professional to detect and identify the COVID-19 cases in the shortest possible t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	209,694
1509.06947	Recipes for stable linear embeddings from Hilbert spaces to R^m	We consider the problem of constructing a linear map from a Hilbert space $\mathcal{H}$ (possibly infinite dimensional) to $\mathbb{R}^m$ that satisfies a restricted isometry property (RIP) on an arbitrary signal model $\mathcal{S} \subset \mathcal{H}$. We present a generic framework that handles a large class of low-d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	47,213
1710.04373	Deep Learning in Multiple Multistep Time Series Prediction	The project aims to research on combining deep learning specifically Long-Short Memory (LSTM) and basic statistics in multiple multistep time series prediction. LSTM can dive into all the pages and learn the general trends of variation in a large scope, while the well selected medians for each page can keep the special...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	82,471
2411.04713	Multi-Reward as Condition for Instruction-based Image Editing	High-quality training triplets (instruction, original image, edited image) are essential for instruction-based image editing. Predominant training datasets (e.g., InsPix2Pix) are created using text-to-image generative models (e.g., Stable Diffusion, DALL-E) which are not trained for image editing. Accordingly, these da...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	506,388
2305.06156	The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation	We present The Vault, a dataset of high-quality code-text pairs in multiple programming languages for training large language models to understand and generate code. We present methods for thoroughly extracting samples that use both rule-based and deep learning-based methods to ensure that they contain high-quality pai...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	363,430
1806.03962	Rotation Equivariant CNNs for Digital Pathology	We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection. Utilizing recent findings on rotation equivariant CNNs, the proposed model leverages these symmetries in a principled manner. We present a visual analysi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	100,126
2410.21969	BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays	Medical Vision-Language Pretraining (MedVLP) shows promise in learning generalizable and transferable visual representations from paired and unpaired medical images and reports. MedVLP can provide useful features to downstream tasks and facilitate adapting task-specific models to new setups using fewer examples. Howeve...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	503,455

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