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
2303.07647	Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review	For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantificatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	351,331
1909.08864	Adversarial Vulnerability Bounds for Gaussian Process Classification	Machine learning (ML) classification is increasingly used in safety-critical systems. Protecting ML classifiers from adversarial examples is crucial. We propose that the main threat is that of an attacker perturbing a confidently classified input to produce a confident misclassification. To protect against this we devi...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	146,081
2202.05481	Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion	In this paper, we propose a locomotion training framework where a control policy and a state estimator are trained concurrently. The framework consists of a policy network which outputs the desired joint positions and a state estimation network which outputs estimates of the robot's states such as the base linear veloc...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	279,891
2103.12553	Safe Multi-Agent Reinforcement Learning through Decentralized Multiple Control Barrier Functions	Multi-Agent Reinforcement Learning (MARL) algorithms show amazing performance in simulation in recent years, but placing MARL in real-world applications may suffer safety problems. MARL with centralized shields was proposed and verified in safety games recently. However, centralized shielding approaches can be infeasib...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	226,218
1902.02523	Distributed Joint Sensor Registration and Multitarget Tracking Via Sensor Network	This paper addresses distributed registration of a sensor network for multitarget tracking. Each sensor gets measurements of the target position in a local coordinate frame, having no knowledge about the relative positions (referred to as drift parameters) and azimuths (referred to as orientation parameters) of its nei...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	120,902
2106.08767	To Raise or Not To Raise: The Autonomous Learning Rate Question	There is a parameter ubiquitous throughout the deep learning world: learning rate. There is likewise a ubiquitous question: what should that learning rate be? The true answer to this question is often tedious and time consuming to obtain, and a great deal of arcane knowledge has accumulated in recent years over how to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	241,411
2402.01147	Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems	We consider the problem of efficiently routing jobs that arrive into a central queue to a system of heterogeneous servers. Unlike homogeneous systems, a threshold policy, that routes jobs to the slow server(s) when the queue length exceeds a certain threshold, is known to be optimal for the one-fast-one-slow two-server...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	425,884
1806.10078	A General Framework for Anytime Approximation in Probabilistic Databases	Anytime approximation algorithms that compute the probabilities of queries over probabilistic databases can be of great use to statistical learning tasks. Those approaches have been based so far on either (i) sampling or (ii) branch-and-bound with model-based bounds. We present here a more general branch-and-bound fram...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	101,483
2403.01999	LLM-Oriented Retrieval Tuner	Dense Retrieval (DR) is now considered as a promising tool to enhance the memorization capacity of Large Language Models (LLM) such as GPT3 and GPT-4 by incorporating external memories. However, due to the paradigm discrepancy between text generation of LLM and DR, it is still an open challenge to integrate the retriev...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	434,669
1510.06197	Reciprocal Altruism-based Cooperation in a Social Network Game	Cooperative behaviors are common in humans and are fundamental to our society. Theoretical and experimental studies have modeled environments in which the behaviors of humans, or agents, have been restricted to analyze their social behavior. However, it is important that such studies are generalized to less restrictive...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	48,095
2305.16192	Explainability Techniques for Chemical Language Models	Explainability techniques are crucial in gaining insights into the reasons behind the predictions of deep learning models, which have not yet been applied to chemical language models. We propose an explainable AI technique that attributes the importance of individual atoms towards the predictions made by these models. ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	367,955
2111.07004	Fault Diagnosis of Nonlinear Systems Using a Hybrid-Degree Dual Cubature-based Estimation Scheme	In this paper, a novel hybrid-degree dual estimation approach based on cubature rules and cubature-based nonlinear filters is proposed for fault diagnosis of nonlinear systems through simultaneous state and time-varying parameter estimation. Our proposed dual nonlinear filtering scheme is developed based on case-depend...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	266,245
1810.13409	You May Not Need Attention	In NMT, how far can we get without attention and without separate encoding and decoding? To answer that question, we introduce a recurrent neural translation model that does not use attention and does not have a separate encoder and decoder. Our eager translation model is low-latency, writing target tokens as soon as i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	111,972
2310.16111	Locally Differentially Private Document Generation Using Zero Shot Prompting	Numerous studies have highlighted the privacy risks associated with pretrained large language models. In contrast, our research offers a unique perspective by demonstrating that pretrained large language models can effectively contribute to privacy preservation. We propose a locally differentially private mechanism cal...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	402,577
2111.04469	Mixed-Integer Optimization with Constraint Learning	We establish a broad methodological foundation for mixed-integer optimization with learned constraints. We propose an end-to-end pipeline for data-driven decision making in which constraints and objectives are directly learned from data using machine learning, and the trained models are embedded in an optimization form...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	265,491
1110.5091	3D Protein Structure Predicted from Sequence	The evolutionary trajectory of a protein through sequence space is constrained by function and three-dimensional (3D) structure. Residues in spatial proximity tend to co-evolve, yet attempts to invert the evolutionary record to identify these constraints and use them to computationally fold proteins have so far been un...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,746
2311.07519	Machine Learning For Beamline Steering	Beam steering is the process involving the calibration of the angle and position at which a particle accelerator's electron beam is incident upon the x-ray target with respect to the rotation axis of the collimator. Beam Steering is an essential task for light sources. In the case under study, the LINAC To Undulator (L...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	407,357
2305.13823	XRoute Environment: A Novel Reinforcement Learning Environment for Routing	Routing is a crucial and time-consuming stage in modern design automation flow for advanced technology nodes. Great progress in the field of reinforcement learning makes it possible to use those approaches to improve the routing quality and efficiency. However, the scale of the routing problems solved by reinforcement ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	366,704
2401.17514	How Useful is Continued Pre-Training for Generative Unsupervised Domain Adaptation?	Recent breakthroughs in scale have enabled the emergence of powerful generative language models, and the ability to fine-tune these models on various tasks by casting them into prompts or instructions. In this landscape, the problem of Unsupervised Domain Adaptation (UDA), or the problem of leveraging knowledge from a ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	425,223
2002.08901	Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR	Multimorbidity research in mental health services requires data from physical health conditions which is traditionally limited in mental health care electronic health records. In this study, we aimed to extract data from physical health conditions from clinical notes using SemEHR. Data was extracted from Clinical Recor...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	164,895
1805.03718	Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks	This paper presents the Neural Cache architecture, which re-purposes cache structures to transform them into massively parallel compute units capable of running inferences for Deep Neural Networks. Techniques to do in-situ arithmetic in SRAM arrays, create efficient data mapping and reducing data movement are proposed....	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	97,103
2405.05904	Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?	When large language models are aligned via supervised fine-tuning, they may encounter new factual information that was not acquired through pre-training. It is often conjectured that this can teach the model the behavior of hallucinating factually incorrect responses, as the model is trained to generate facts that are ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	453,104
2007.15296	Leverage Unlabeled Data for Abstractive Speech Summarization with Self-Supervised Learning and Back-Summarization	Supervised approaches for Neural Abstractive Summarization require large annotated corpora that are costly to build. We present a French meeting summarization task where reports are predicted based on the automatic transcription of the meeting audio recordings. In order to build a corpus for this task, it is necessary ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	189,638
2109.03839	Sqrt(d) Dimension Dependence of Langevin Monte Carlo	This article considers the popular MCMC method of unadjusted Langevin Monte Carlo (LMC) and provides a non-asymptotic analysis of its sampling error in 2-Wasserstein distance. The proof is based on a refinement of mean-square analysis in Li et al. (2019), and this refined framework automates the analysis of a large cla...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	254,185
2407.04185	HAF-RM: A Hybrid Alignment Framework for Reward Model Training	The reward model has become increasingly important in alignment, assessment, and data construction for large language models (LLMs). Most existing researchers focus on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	470,459
1604.07371	Do the Hard Stuff First: Scheduling Dependent Computations in Data-Analytics Clusters	We present a scheduler that improves cluster utilization and job completion times by packing tasks having multi-resource requirements and inter-dependencies. While the problem is algorithmically very hard, we achieve near-optimality on the job DAGs that appear in production clusters at a large enterprise and in benchma...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	true	55,084
2203.04768	Explainable Machine Learning for Predicting Homicide Clearance in the United States	Purpose: To explore the potential of Explainable Machine Learning in the prediction and detection of drivers of cleared homicides at the national- and state-levels in the United States. Methods: First, nine algorithmic approaches are compared to assess the best performance in predicting cleared homicides country-wise...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	284,598
2401.03343	Rediscovering Ranganathan: A Prismatic View of His Life through the Knowledge Graph Spectrum	The present study puts forward a novel biographical knowledge graph (KG) on Prof. S. R. Ranganathan, one of the pioneering figures in the Library and Information Science (LIS) domain. It has been found that most of the relevant facts about Ranganathan exist in a variety of resources (e.g., books, essays, journal articl...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	420,068
2111.00490	DSC-IITISM at FinCausal 2021: Combining POS tagging with Attention-based Contextual Representations for Identifying Causal Relationships in Financial Documents	Causality detection draws plenty of attention in the field of Natural Language Processing and linguistics research. It has essential applications in information retrieval, event prediction, question answering, financial analysis, and market research. In this study, we explore several methods to identify and extract cau...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	264,234
2407.12773	OMG-Net: A Deep Learning Framework Deploying Segment Anything to Detect Pan-Cancer Mitotic Figures from Haematoxylin and Eosin-Stained Slides	Mitotic activity is an important feature for grading several cancer types. Counting mitotic figures (MFs) is a time-consuming, laborious task prone to inter-observer variation. Inaccurate recognition of MFs can lead to incorrect grading and hence potential suboptimal treatment. In this study, we propose an artificial i...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	474,066
1801.00602	Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture	In neuroscience, all kinds of computation models were designed to answer the open question of how sensory stimuli are encoded by neurons and conversely, how sensory stimuli can be decoded from neuronal activities. Especially, functional Magnetic Resonance Imaging (fMRI) studies have made many great achievements with th...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	87,597
2409.04341	Towards Fine-Grained Webpage Fingerprinting at Scale	Website Fingerprinting (WF) attacks can effectively identify the websites visited by Tor clients via analyzing encrypted traffic patterns. Existing attacks focus on identifying different websites, but their accuracy dramatically decreases when applied to identify fine-grained webpages, especially when distinguishing am...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	486,362
2501.14767	Leveraging Social Media Data and Artificial Intelligence for Improving Earthquake Response Efforts	The integration of social media and artificial intelligence (AI) into disaster management, particularly for earthquake response, represents a profound evolution in emergency management practices. In the digital age, real-time information sharing has reached unprecedented levels, with social media platforms emerging as ...	false	false	false	true	true	true	false	false	true	false	false	false	false	true	false	false	false	false	527,262
2209.09018	A Causal Intervention Scheme for Semantic Segmentation of Quasi-periodic Cardiovascular Signals	Precise segmentation is a vital first step to analyze semantic information of cardiac cycle and capture anomaly with cardiovascular signals. However, in the field of deep semantic segmentation, inference is often unilaterally confounded by the individual attribute of data. Towards cardiovascular signals, quasi-periodic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	318,347
1811.04455	Learning with tree-based tensor formats	This paper is concerned with the approximation of high-dimensional functions in a statistical learning setting, by empirical risk minimization over model classes of functions in tree-based tensor format. These are particular classes of rank-structured functions that can be seen as deep neural networks with a sparse arc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,094
2107.11250	Multi-Channel Automatic Music Transcription Using Tensor Algebra	Music is an art, perceived in unique ways by every listener, coming from acoustic signals. In the meantime, standards as musical scores exist to describe it. Even if humans can make this transcription, it is costly in terms of time and efforts, even more with the explosion of information consecutively to the rise of th...	false	false	true	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	247,543
2411.10581	On the Shortcut Learning in Multilingual Neural Machine Translation	In this study, we revisit the commonly-cited off-target issue in multilingual neural machine translation (MNMT). By carefully designing experiments on different MNMT scenarios and models, we attribute the off-target issue to the overfitting of the shortcuts of (non-centric, centric) language mappings. Specifically, the...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	508,701
2310.03051	How FaR Are Large Language Models From Agents with Theory-of-Mind?	"Thinking is for Doing." Humans can infer other people's mental states from observations--an ability called Theory-of-Mind (ToM)--and subsequently act pragmatically on those inferences. Existing question answering benchmarks such as ToMi ask models questions to make inferences about beliefs of characters in a story, bu...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	397,117
2310.18988	A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning	Conventional statistical wisdom established a well-understood relationship between model complexity and prediction error, typically presented as a U-shaped curve reflecting a transition between under- and overfitting regimes. However, motivated by the success of overparametrized neural networks, recent influential work...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	403,800
1605.09114	ParMAC: distributed optimisation of nested functions, with application to learning binary autoencoders	Many powerful machine learning models are based on the composition of multiple processing layers, such as deep nets, which gives rise to nonconvex objective functions. A general, recent approach to optimise such "nested" functions is the method of auxiliary coordinates (MAC). MAC introduces an auxiliary coordinate for ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	56,525
1709.02339	When Labels Fall Short: Property Graph Simulation via Blending of Network Structure and Vertex Attributes	Property graphs can be used to represent heterogeneous networks with labeled (attributed) vertices and edges. Given a property graph, simulating another graph with same or greater size with the same statistical properties with respect to the labels and connectivity is critical for privacy preservation and benchmarking ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	80,249
2304.14058	A Parameterized Theory of PAC Learning	Probably Approximately Correct (i.e., PAC) learning is a core concept of sample complexity theory, and efficient PAC learnability is often seen as a natural counterpart to the class P in classical computational complexity. But while the nascent theory of parameterized complexity has allowed us to push beyond the P-NP `...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	360,808
2210.05327	A Causal Analysis of Harm	As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework to address when and how such a system harms someone. There have been several attempts within the philosophy literature to define harm, but none of them has proven capable of dealing with with the many examples ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	322,806
2109.00700	Machine learning moment closure models for the radiative transfer equation III: enforcing hyperbolicity and physical characteristic speeds	This is the third paper in a series in which we develop machine learning (ML) moment closure models for the radiative transfer equation (RTE). In our previous work \cite{huang2021gradient}, we proposed an approach to learn the gradient of the unclosed high order moment, which performs much better than learning the mome...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	253,199
2406.01591	DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation	This paper presents Deformable Neural Vessel Representations (DeNVeR), an unsupervised approach for vessel segmentation in X-ray angiography videos without annotated ground truth. DeNVeR utilizes optical flow and layer separation techniques, enhancing segmentation accuracy and adaptability through test-time training. K...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	460,382
2110.08003	A Broad-persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments	Deep Reinforcement Learning (DeepRL) methods have been widely used in robotics to learn about the environment and acquire behaviors autonomously. Deep Interactive Reinforcement Learning (DeepIRL) includes interactive feedback from an external trainer or expert giving advice to help learners choosing actions to speed up...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	261,208
1905.01684	Unsupervised Detection of Distinctive Regions on 3D Shapes	This paper presents a novel approach to learn and detect distinctive regions on 3D shapes. Unlike previous works, which require labeled data, our method is unsupervised. We conduct the analysis on point sets sampled from 3D shapes, then formulate and train a deep neural network for an unsupervised shape clustering task...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	129,788
1805.11686	Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition	The design of a reward function often poses a major practical challenge to real-world applications of reinforcement learning. Approaches such as inverse reinforcement learning attempt to overcome this challenge, but require expert demonstrations, which can be difficult or expensive to obtain in practice. We propose var...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	98,975
2401.10501	Enhancing medical vision-language contrastive learning via inter-matching relation modelling	Medical image representations can be learned through medical vision-language contrastive learning (mVLCL) where medical imaging reports are used as weak supervision through image-text alignment. These learned image representations can be transferred to and benefit various downstream medical vision tasks such as disease...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	422,662
1907.13548	Optimal Attacks on Reinforcement Learning Policies	Control policies, trained using the Deep Reinforcement Learning, have been recently shown to be vulnerable to adversarial attacks introducing even very small perturbations to the policy input. The attacks proposed so far have been designed using heuristics, and build on existing adversarial example crafting techniques ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	140,397
2106.05554	Progressive Stage-wise Learning for Unsupervised Feature Representation Enhancement	Unsupervised learning methods have recently shown their competitiveness against supervised training. Typically, these methods use a single objective to train the entire network. But one distinct advantage of unsupervised over supervised learning is that the former possesses more variety and freedom in designing the obj...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	240,143
1701.01495	Membrane-Dependent Neuromorphic Learning Rule for Unsupervised Spike Pattern Detection	Several learning rules for synaptic plasticity, that depend on either spike timing or internal state variables, have been proposed in the past imparting varying computational capabilities to Spiking Neural Networks. Due to design complications these learning rules are typically not implemented on neuromorphic devices l...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	66,412
1604.05213	Non-contact hemodynamic imaging reveals the jugular venous pulse waveform	Cardiovascular monitoring is important to prevent diseases from progressing. The jugular venous pulse (JVP) waveform offers important clinical information about cardiac health, but is not routinely examined due to its invasive catheterisation procedure. Here, we demonstrate for the first time that the JVP can be consis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	54,780
2408.09300	Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein model	We present Malacopula, a neural-based generalised Hammerstein model designed to introduce adversarial perturbations to spoofed speech utterances so that they better deceive automatic speaker verification (ASV) systems. Using non-linear processes to modify speech utterances, Malacopula enhances the effectiveness of spoo...	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	481,370
2109.07105	Local NMPC on Global Optimised Path for Autonomous Racing	The paper presents a strategy for the control of anautonomous racing car on a pre-mapped track. Using a dynamic model of the vehicle, the optimal racing line is computed, taking track boundaries into account. With the optimal racing line as areference, a local nonlinear model predictive controller (NMPC) is proposed, w...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	255,385
2002.07376	Picking Winning Tickets Before Training by Preserving Gradient Flow	Overparameterization has been shown to benefit both the optimization and generalization of neural networks, but large networks are resource hungry at both training and test time. Network pruning can reduce test-time resource requirements, but is typically applied to trained networks and therefore cannot avoid the expen...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	164,453
2211.13226	ClimateNeRF: Extreme Weather Synthesis in Neural Radiance Field	Physical simulations produce excellent predictions of weather effects. Neural radiance fields produce SOTA scene models. We describe a novel NeRF-editing procedure that can fuse physical simulations with NeRF models of scenes, producing realistic movies of physical phenomena in those scenes. Our application -- Climate ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	332,395
2501.06308	Uncertainty Estimation for Path Loss and Radio Metric Models	This research leverages Conformal Prediction (CP) in the form of Conformal Predictive Systems (CPS) to accurately estimate uncertainty in a suite of machine learning (ML)-based radio metric models [1] as well as in a 2-D map-based ML path loss model [2]. Utilizing diverse difficulty estimators, we construct 95% confide...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	523,935
2310.11709	Live Graph Lab: Towards Open, Dynamic and Real Transaction Graphs with NFT	Numerous studies have been conducted to investigate the properties of large-scale temporal graphs. Despite the ubiquity of these graphs in real-world scenarios, it's usually impractical for us to obtain the whole real-time graphs due to privacy concerns and technical limitations. In this paper, we introduce the concept...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	400,750
1910.01300	Resilience in multi-robot target tracking through reconfiguration	We address the problem of maintaining resource availability in a networked multi-robot system performing distributed target tracking. In our model, robots are equipped with sensing and computational resources enabling them to track a target's position using a Distributed Kalman Filter (DKF). We use the trace of each ro...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	147,904
1909.08540	No-Regret Learning in Unknown Games with Correlated Payoffs	We consider the problem of learning to play a repeated multi-agent game with an unknown reward function. Single player online learning algorithms attain strong regret bounds when provided with full information feedback, which unfortunately is unavailable in many real-world scenarios. Bandit feedback alone, i.e., observ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	146,000
1709.00653	From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach	One key challenge in talent search is to translate complex criteria of a hiring position into a search query, while it is relatively easy for a searcher to list examples of suitable candidates for a given position. To improve search efficiency, we propose the next generation of talent search at LinkedIn, also referred ...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	79,942
2212.14173	Near-Tight Algorithms for the Chamberlin-Courant and Thiele Voting Rules	We present an almost optimal algorithm for the classic Chamberlin-Courant multiwinner voting rule (CC) on single-peaked preference profiles. Given $n$ voters and $m$ candidates, it runs in almost linear time in the input size, improving the previous best $O(nm^2)$ time algorithm of Betzler et al. (2013). We also study ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	338,531
2308.14663	Formal Modelling and Analysis of a Self-Adaptive Robotic System	Self-adaptation is a crucial feature of autonomous systems that must cope with uncertainties in, e.g., their environment and their internal state. Self-adaptive systems are often modelled as two-layered systems with a managed subsystem handling the domain concerns and a managing subsystem implementing the adaptation lo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	388,411
2002.07806	Data-Driven Symbol Detection via Model-Based Machine Learning	The design of symbol detectors in digital communication systems has traditionally relied on statistical channel models that describe the relation between the transmitted symbols and the observed signal at the receiver. Here we review a data-driven framework to symbol detection design which combines machine learning (ML...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	164,575
2307.06120	Recognizing student identification numbers from the matrix templates using a modified U-net architecture	This paper presents an innovative approach to student identification during exams and knowledge tests, which overcomes the limitations of the traditional personal information entry method. The proposed method employs a matrix template on the designated section of the exam, where squares containing numbers are selective...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	378,973
1910.05309	Communications and Networking Technologies for Intelligent Drone Cruisers	Future mobile communication networks require an Aerial Base Station (ABS) with fast mobility and long-term hovering capabilities. At present, unmanned aerial vehicles (UAV) or drones do not have long flight times and are mainly used for monitoring, surveillance, and image post-processing. On the other hand, the traditi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	149,021
2306.10878	Handwritten Text Recognition from Crowdsourced Annotations	In this paper, we explore different ways of training a model for handwritten text recognition when multiple imperfect or noisy transcriptions are available. We consider various training configurations, such as selecting a single transcription, retaining all transcriptions, or computing an aggregated transcription from ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	374,399
2410.05733	Private and Communication-Efficient Federated Learning based on Differentially Private Sketches	Federated learning (FL) faces two primary challenges: the risk of privacy leakage due to parameter sharing and communication inefficiencies. To address these challenges, we propose DPSFL, a federated learning method that utilizes differentially private sketches. DPSFL compresses the local gradients of each client using...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	495,883
2202.04267	Efficiently Computable Converses for Finite-Blocklength Communication	This paper presents a method for computing a finite-blocklength converse for the rate of fixed-length codes with feedback used on discrete memoryless channels (DMCs). The new converse is expressed in terms of a stochastic control problem whose solution can be efficiently computed using dynamic programming and Fourier m...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	279,509
2405.15110	CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems	In this work, we dive deep into one of the popular knowledge-grounded dialogue benchmarks that focus on faithfulness, FaithDial. We show that a significant portion of the FaithDial data contains annotation artifacts, which may bias models towards completely ignoring the conversation history. We therefore introduce CHAR...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	456,749
2212.01136	Robustness in Fatigue Strength Estimation	Fatigue strength estimation is a costly manual material characterization process in which state-of-the-art approaches follow a standardized experiment and analysis procedure. In this paper, we examine a modular, Machine Learning-based approach for fatigue strength estimation that is likely to reduce the number of exper...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	334,322
2304.07927	A Randomized Approach for Tight Privacy Accounting	Bounding privacy leakage over compositions, i.e., privacy accounting, is a key challenge in differential privacy (DP). The privacy parameter ($\eps$ or $\delta$) is often easy to estimate but hard to bound. In this paper, we propose a new differential privacy paradigm called estimate-verify-release (EVR), which address...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	358,526
1612.06140	Domain Control for Neural Machine Translation	Machine translation systems are very sensitive to the domains they were trained on. Several domain adaptation techniques have been deeply studied. We propose a new technique for neural machine translation (NMT) that we call domain control which is performed at runtime using a unique neural network covering multiple dom...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	65,783
1709.03413	Gigamachine: incremental machine learning on desktop computers	We present a concrete design for Solomonoff's incremental machine learning system suitable for desktop computers. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a Levin Search variant based on a stochastic Context Free Grammar together with new update algorithms ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	80,456
2110.14143	SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation	Natural language instructions for visual navigation often use scene descriptions (e.g., "bedroom") and object references (e.g., "green chairs") to provide a breadcrumb trail to a goal location. This work presents a transformer-based vision-and-language navigation (VLN) agent that uses two different visual encoders -- a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	263,427
2210.10998	Semi-supervised object detection based on single-stage detector for thighbone fracture localization	The thighbone is the largest bone supporting the lower body. If the thighbone fracture is not treated in time, it will lead to lifelong inability to walk. Correct diagnosis of thighbone disease is very important in orthopedic medicine. Deep learning is promoting the development of fracture detection technology. However...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	325,140
2109.03391	Visual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and Prospects	Visual sensation and perception refers to the process of sensing, organizing, identifying, and interpreting visual information in environmental awareness and understanding. Computational models inspired by visual perception have the characteristics of complexity and diversity, as they come from many subjects such as co...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	254,047
2301.06059	Learning Audio-Driven Viseme Dynamics for 3D Face Animation	We present a novel audio-driven facial animation approach that can generate realistic lip-synchronized 3D facial animations from the input audio. Our approach learns viseme dynamics from speech videos, produces animator-friendly viseme curves, and supports multilingual speech inputs. The core of our approach is a novel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	340,537
2207.11717	A Priority Map for Vision-and-Language Navigation with Trajectory Plans and Feature-Location Cues	In a busy city street, a pedestrian surrounded by distractions can pick out a single sign if it is relevant to their route. Artificial agents in outdoor Vision-and-Language Navigation (VLN) are also confronted with detecting supervisory signal on environment features and location in inputs. To boost the prominence of r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	309,750
1911.04273	A Contextual Hierarchical Graph Model for Generating Random Sequences of Objects with Application to Music Playlists	Recommending the right content in large scale multimedia streaming services is an important and challenging problem that has received much attention in the past decade. A key ingredient for successful recommendations is an effective similarity metric between two objects, and models that leverage the current context to ...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	152,947
1112.1989	Coded Single-Tone Signaling and Its Application to Resource Coordination and Interference Management in Femtocell Networks	Resource coordination and interference management is the key to achieving the benefits of femtocell networks. Over-the-air signaling is one of the most effective means for distributed dynamic resource coordination and interference management. However, the design of this type of signal is challenging. In this paper, we ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	13,376
2008.11433	Surrogate Model For Field Optimization Using Beta-VAE Based Regression	Oilfield development related decisions are made using reservoir simulation-based optimization study in which different production scenarios and well controls are compared. Such simulations are computationally expensive and so surrogate models are used to accelerate studies. Deep learning has been used in past to genera...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	193,277
2303.00461	Uzbek text summarization based on TF-IDF	The volume of information is increasing at an incredible rate with the rapid development of the Internet and electronic information services. Due to time constraints, we don't have the opportunity to read all this information. Even the task of analyzing textual data related to one field requires a lot of work. The text...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	348,606
1905.03003	Multi-task human analysis in still images: 2D/3D pose, depth map, and multi-part segmentation	While many individual tasks in the domain of human analysis have recently received an accuracy boost from deep learning approaches, multi-task learning has mostly been ignored due to a lack of data. New synthetic datasets are being released, filling this gap with synthetic generated data. In this work, we analyze four ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	130,101
2312.11926	Big Learning Expectation Maximization	Mixture models serve as one fundamental tool with versatile applications. However, their training techniques, like the popular Expectation Maximization (EM) algorithm, are notoriously sensitive to parameter initialization and often suffer from bad local optima that could be arbitrarily worse than the optimal. To addres...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,781
2408.04245	Scalable Transformer for High Dimensional Multivariate Time Series Forecasting	Deep models for Multivariate Time Series (MTS) forecasting have recently demonstrated significant success. Channel-dependent models capture complex dependencies that channel-independent models cannot capture. However, the number of channels in real-world applications outpaces the capabilities of existing channel-depend...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	479,311
2307.09575	Causal Influences over Social Learning Networks	This paper investigates causal influences between agents linked by a social graph and interacting over time. In particular, the work examines the dynamics of social learning models and distributed decision-making protocols, and derives expressions that reveal the causal relations between pairs of agents and explain the...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	false	380,215
2406.20052	Understanding and Mitigating Language Confusion in LLMs	We investigate a surprising limitation of LLMs: their inability to consistently generate text in a user's desired language. We create the Language Confusion Benchmark (LCB) to evaluate such failures, covering 15 typologically diverse languages with existing and newly-created English and multilingual prompts. We evaluat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	468,648
1811.09067	Online Collective Animal Movement Activity Recognition	Learning the activities of animals is important for the purpose of monitoring their welfare vis a vis their behaviour with respect to their environment and conspecifics. While previous works have largely focused on activity recognition in a single animal, little or no work has been done in learning the collective behav...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	114,187
1601.04745	A Theoretical Analysis of Two-Stage Recommendation for Cold-Start Collaborative Filtering	In this paper, we present a theoretical framework for tackling the cold-start collaborative filtering problem, where unknown targets (items or users) keep coming to the system, and there is a limited number of resources (users or items) that can be allocated and related to them. The solution requires a trade-off betwee...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	51,055
1205.0792	Exact Wavelets on the Ball	We develop an exact wavelet transform on the three-dimensional ball (i.e. on the solid sphere), which we name the flaglet transform. For this purpose we first construct an exact transform on the radial half-line using damped Laguerre polynomials and develop a corresponding quadrature rule. Combined with the spherical h...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	15,788
1403.1412	Rate Prediction and Selection in LTE systems using Modified Source Encoding Techniques	In current wireless systems, the base-Station (eNodeB) tries to serve its user-equipment (UE) at the highest possible rate that the UE can reliably decode. The eNodeB obtains this rate information as a quantized feedback from the UE at time n and uses this, for rate selection till the next feedback is received at time ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	31,391
2404.19696	Naturally Supervised 3D Visual Grounding with Language-Regularized Concept Learners	3D visual grounding is a challenging task that often requires direct and dense supervision, notably the semantic label for each object in the scene. In this paper, we instead study the naturally supervised setting that learns from only 3D scene and QA pairs, where prior works underperform. We propose the Language-Regul...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	450,738
2308.11990	RankMixup: Ranking-Based Mixup Training for Network Calibration	Network calibration aims to accurately estimate the level of confidences, which is particularly important for employing deep neural networks in real-world systems. Recent approaches leverage mixup to calibrate the network's predictions during training. However, they do not consider the problem that mixtures of labels i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	387,364
2312.00387	Partition-based K-space Synthesis for Multi-contrast Parallel Imaging	Multi-contrast magnetic resonance imaging is a significant and essential medical imaging technique.However, multi-contrast imaging has longer acquisition time and is easy to cause motion artifacts. In particular, the acquisition time for a T2-weighted image is prolonged due to its longer repetition time (TR). On the co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,035
2005.12240	A review of sentiment analysis research in Arabic language	Sentiment analysis is a task of natural language processing which has recently attracted increasing attention. However, sentiment analysis research has mainly been carried out for the English language. Although Arabic is ramping up as one of the most used languages on the Internet, only a few studies have focused on Ar...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	178,681
2402.07140	Can Graph Descriptive Order Affect Solving Graph Problems with LLMs?	Large language models (LLMs) have achieved significant success in reasoning tasks, including mathematical reasoning and logical deduction. Among these reasoning tasks, graph problems stand out due to their complexity and unique structural characteristics, attracting considerable attention from researchers. Previous stu...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	428,580
1911.13288	Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019	Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	155,642
2005.06601	Unlocking the Power of Deep PICO Extraction: Step-wise Medical NER Identification	The PICO framework (Population, Intervention, Comparison, and Outcome) is usually used to formulate evidence in the medical domain. The major task of PICO extraction is to extract sentences from medical literature and classify them into each class. However, in most circumstances, there will be more than one evidences i...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	177,042

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