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
2405.05146	Hybrid Convolutional Neural Networks with Reliability Guarantee	Making AI safe and dependable requires the generation of dependable models and dependable execution of those models. We propose redundant execution as a well-known technique that can be used to ensure reliable execution of the AI model. This generic technique will extend the application scope of AI-accelerators that do...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	452,812
2302.11197	Quantized Low-Rank Multivariate Regression with Random Dithering	Low-rank multivariate regression (LRMR) is an important statistical learning model that combines highly correlated tasks as a multiresponse regression problem with low-rank priori on the coefficient matrix. In this paper, we study quantized LRMR, a practical setting where the responses and/or the covariates are discret...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	347,124
2311.13612	Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning	Over the past year, a large body of multimodal research has emerged around zero-shot evaluation using GPT descriptors. These studies boost the zero-shot accuracy of pretrained VL models with an ensemble of label-specific text generated by GPT. A recent study, WaffleCLIP, demonstrated that similar zero-shot accuracy can...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	409,800
2204.10921	Subscriptions and external links help drive resentful users to alternative and extremist YouTube videos	Do online platforms facilitate the consumption of potentially harmful content? Using paired behavioral and survey data provided by participants recruited from a representative sample in 2020 (n=1,181), we show that exposure to alternative and extremist channel videos on YouTube is heavily concentrated among a small gro...	true	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	false	292,960
2202.05775	Inference of Multiscale Gaussian Graphical Model	Gaussian Graphical Models (GGMs) are widely used for exploratory data analysis in various fields such as genomics, ecology, psychometry. In a high-dimensional setting, when the number of variables exceeds the number of observations by several orders of magnitude, the estimation of GGM is a difficult and unstable optimi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,982
1807.02996	Dynamic Objects Segmentation for Visual Localization in Urban Environments	Visual localization and mapping is a crucial capability to address many challenges in mobile robotics. It constitutes a robust, accurate and cost-effective approach for local and global pose estimation within prior maps. Yet, in highly dynamic environments, like crowded city streets, problems arise as major parts of th...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	102,398
2412.04910	Learning High-Degree Parities: The Crucial Role of the Initialization	Parities have become a standard benchmark for evaluating learning algorithms. Recent works show that regular neural networks trained by gradient descent can efficiently learn degree $k$ parities on uniform inputs for constant $k$, but fail to do so when $k$ and $d-k$ grow with $d$ (here $d$ is the ambient dimension). H...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	514,623
1901.00188	Complementary reinforcement learning towards explainable agents	Reinforcement learning (RL) algorithms allow agents to learn skills and strategies to perform complex tasks without detailed instructions or expensive labelled training examples. That is, RL agents can learn, as we learn. Given the importance of learning in our intelligence, RL has been thought to be one of key compone...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	117,712
2111.05701	Single image dehazing via combining the prior knowledge and CNNs	Aiming at the existing single image haze removal algorithms, which are based on prior knowledge and assumptions, subject to many limitations in practical applications, and could suffer from noise and halo amplification. An end-to-end system is proposed in this paper to reduce defects by combining the prior knowledge an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	265,862
2501.15328	Physiologically-Informed Predictability of a Teammate's Future Actions Forecasts Team Performance	In collaborative environments, a deep understanding of multi-human teaming dynamics is essential for optimizing performance. However, the relationship between individuals' behavioral and physiological markers and their combined influence on overall team performance remains poorly understood. To explore this, we designe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	527,504
1411.6307	Diversifying Sparsity Using Variational Determinantal Point Processes	We propose a novel diverse feature selection method based on determinantal point processes (DPPs). Our model enables one to flexibly define diversity based on the covariance of features (similar to orthogonal matching pursuit) or alternatively based on side information. We introduce our approach in the context of Bayes...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	37,827
2401.01066	DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic Segmentation	Due to the poor illumination and the difficulty in annotating, nighttime conditions pose a significant challenge for autonomous vehicle perception systems. Unsupervised domain adaptation (UDA) has been widely applied to semantic segmentation on such images to adapt models from normal conditions to target nighttime-cond...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	419,209
2011.04755	Learning to Infer Semantic Parameters for 3D Shape Editing	Many applications in 3D shape design and augmentation require the ability to make specific edits to an object's semantic parameters (e.g., the pose of a person's arm or the length of an airplane's wing) while preserving as much existing details as possible. We propose to learn a deep network that infers the semantic pa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	205,675
1703.03842	Effects of Limiting Memory Capacity on the Behaviour of Exemplar Dynamics	Exemplar models are a popular class of models used to describe language change. Here we study how limiting the memory capacity of an individual in these models affects the system's behaviour. In particular we demonstrate the effect this change has on the extinction of categories. Previous work in exemplar dynamics has ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	69,779
1807.03661	Threshold $q$-voter model	We introduce the threshold $q$-voter opinion dynamics where an agent, facing a binary choice, can change its mind when at least $q_0$ amongst $q$ neighbors share the opposite opinion. Otherwise, the agent can still change its mind with a certain probability $\varepsilon$. This threshold dynamics contemplates the possib...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	102,584
2304.00450	Sketch-based Video Object Localization	We introduce Sketch-based Video Object Localization (SVOL), a new task aimed at localizing spatio-temporal object boxes in video queried by the input sketch. We first outline the challenges in the SVOL task and build the Sketch-Video Attention Network (SVANet) with the following design principles: (i) to consider tempo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	355,693
2110.08202	Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System	Federated Learning (FL) decouples model training from the need for direct access to the data and allows organizations to collaborate with industry partners to reach a satisfying level of performance without sharing vulnerable business information. The performance of a machine learning algorithm is highly sensitive to t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	261,289
2405.14318	Adaptive Retention & Correction: Test-Time Training for Continual Learning	Continual learning, also known as lifelong learning or incremental learning, refers to the process by which a model learns from a stream of incoming data over time. A common problem in continual learning is the classification layer's bias towards the most recent task. Traditionally, methods have relied on incorporating...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	456,368
2012.01724	Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection	This paper proposes the Parallel Residual Bi-Fusion Feature Pyramid Network (PRB-FPN) for fast and accurate single-shot object detection. Feature Pyramid (FP) is widely used in recent visual detection, however the top-down pathway of FP cannot preserve accurate localization due to pooling shifting. The advantage of FP ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	209,508
2212.07918	Construction of a Surrogate Model: Multivariate Time Series Prediction with a Hybrid Model	Recent developments of advanced driver-assistance systems necessitate an increasing number of tests to validate new technologies. These tests cannot be carried out on track in a reasonable amount of time and automotive groups rely on simulators to perform most tests. The reliability of these simulators for constantly r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	336,567
1505.02997	Optimal Data and Training Symbol Ratio for Communication over Uncertain Channels	We consider the problem of determining the power ratio between the training symbols and data symbols in order to maximize the channel capacity for transmission over uncertain channels with a channel estimate available at both the transmitter and receiver. The receiver makes an estimate of the channel by using a known s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	43,025
2308.02219	Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies	Restrictive rules for data sharing in many industries have led to the development of federated learning. Federated learning is a machine-learning technique that allows distributed clients to train models collaboratively without the need to share their respective training data with others. In this paper, we first explor...	false	false	false	true	true	false	false	false	false	false	false	false	false	true	false	false	false	false	383,541
1911.06833	Improved Exploration through Latent Trajectory Optimization in Deep Deterministic Policy Gradient	Model-free reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG) often require additional exploration strategies, especially if the actor is of deterministic nature. This work evaluates the use of model-based trajectory optimization methods used for exploration in Deep Deterministic Policy...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	153,630
1812.10394	Extraction of Behavioral Features from Smartphone and Wearable Data	The rich set of sensors in smartphones and wearable devices provides the possibility to passively collect streams of data in the wild. The raw data streams, however, can rarely be directly used in the modeling pipeline. We provide a generic framework that can process raw data streams and extract useful features related...	true	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	117,354
2402.00769	AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data	This paper introduces an effective method for computation-efficient personalized style video generation without requiring access to any personalized video data. It reduces the necessary generation time of similarly sized video diffusion models from 25 seconds to around 1 second while maintaining the same level of perfo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	425,714
1507.04019	Feature Normalisation for Robust Speech Recognition	Speech recognition system performance degrades in noisy environments. If the acoustic models are built using features of clean utterances, the features of a noisy test utterance would be acoustically mismatched with the trained model. This gives poor likelihoods and poor recognition accuracy. Model adaptation and featu...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	45,125
1703.07830	Randomized Kernel Methods for Least-Squares Support Vector Machines	The least-squares support vector machine is a frequently used kernel method for non-linear regression and classification tasks. Here we discuss several approximation algorithms for the least-squares support vector machine classifier. The proposed methods are based on randomized block kernel matrices, and we show that t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	70,461
1912.12064	Efficient Data Analytics on Augmented Similarity Triplets	Data analysis require a pairwise proximity measure over objects. Recent work has extended this to situations where the distance information between objects is given as comparison results of distances between three objects (triplets). Humans find the comparison tasks much easier than the exact distance computation and s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	158,744
1108.0535	Universal Rateless Codes From Coupled LT Codes	It was recently shown that spatial coupling of individual low-density parity-check codes improves the belief-propagation threshold of the coupled ensemble essentially to the maximum a posteriori threshold of the underlying ensemble. We study the performance of spatially coupled low-density generator-matrix ensembles wh...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	11,546
2007.04567	Ultra-sensitive Parity-Time Symmetry based Graphene FET (PTS-GFET) Sensors	A novel ultra-sensitive Parity-Time symmetry based Graphene FET (PTS-GFET) sensor is studied for gas concentration detection. The PTS-GFET sensor effectively integrates the sensitivity of the PT symmetry around its Exceptional Point (EP) and the tunability of the GFET conductance. The change of GFET conductance with th...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	186,397
2501.10221	Modelling Activity Scheduling Behaviour with Deep Generative Machine Learning	We model human activity scheduling behaviour using a deep generative machine learning approach. Activity schedules, which represent the activities and associated travel behaviours of individuals, are a core component of many applied models in the transport, energy and epidemiology domains. Our data driven approach lear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	525,444
2202.01782	Retinal Vessel Segmentation with Pixel-wise Adaptive Filters	Accurate retinal vessel segmentation is challenging because of the complex texture of retinal vessels and low imaging contrast. Previous methods generally refine segmentation results by cascading multiple deep networks, which are time-consuming and inefficient. In this paper, we propose two novel methods to address the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	278,584
2007.11115	Byzantine-Resilient Secure Federated Learning	Secure federated learning is a privacy-preserving framework to improve machine learning models by training over large volumes of data collected by mobile users. This is achieved through an iterative process where, at each iteration, users update a global model using their local datasets. Each user then masks its local ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	188,467
2010.11655	Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games	We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to represent the environment information and language actions, existing RL agents are not empowered with any reasoning capabilities to deal with te...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	202,352
2306.09979	Evaluation of Speech Representations for MOS prediction	In this paper, we evaluate feature extraction models for predicting speech quality. We also propose a model architecture to compare embeddings of supervised learning and self-supervised learning models with embeddings of speaker verification models to predict the metric MOS. Our experiments were performed on the VCC201...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	374,040
1301.1429	Adaptation of fictional and online conversations to communication media	Conversations allow the quick transfer of short bits of information and it is reasonable to expect that changes in communication medium affect how we converse. Using conversations in works of fiction and in an online social networking platform, we show that the utterance length of conversations is slowly shortening wit...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	20,859
2304.01489	Improved Visual Fine-tuning with Natural Language Supervision	Fine-tuning a visual pre-trained model can leverage the semantic information from large-scale pre-training data and mitigate the over-fitting problem on downstream vision tasks with limited training examples. While the problem of catastrophic forgetting in pre-trained backbone has been extensively studied for fine-tuni...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	356,095
2407.08795	Feasibility of Neural Radiance Fields for Crime Scene Video Reconstruction	This paper aims to review and determine the feasibility of using variations of NeRF models in order to reconstruct crime scenes given input videos of the scene. We focus on three main innovations of NeRF when it comes to reconstructing crime scenes: Multi-object Synthesis, Deformable Synthesis, and Lighting. From there...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,310
1610.04062	Video Fill in the Blank with Merging LSTMs	Given a video and its incomplete textural description with missing words, the Video-Fill-in-the-Blank (ViFitB) task is to automatically find the missing word. The contextual information of the sentences are important to infer the missing words; the visual cues are even more crucial to get a more accurate inference. In ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	62,337
2210.03971	An Ordinal Latent Variable Model of Conflict Intensity	Measuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of "who did what to whom" micro-records that enable data-driven approaches to monitoring conflict. The Goldstein scale is a widely-used expert-based measure that...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	322,254
1711.05941	Skepxels: Spatio-temporal Image Representation of Human Skeleton Joints for Action Recognition	Human skeleton joints are popular for action analysis since they can be easily extracted from videos to discard background noises. However, current skeleton representations do not fully benefit from machine learning with CNNs. We propose "Skepxels" a spatio-temporal representation for skeleton sequences to fully exploi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	84,681
2010.11518	Geometry-Aware Hamiltonian Variational Auto-Encoder	Variational auto-encoders (VAEs) have proven to be a well suited tool for performing dimensionality reduction by extracting latent variables lying in a potentially much smaller dimensional space than the data. Their ability to capture meaningful information from the data can be easily apprehended when considering their...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	202,286
2402.12922	Gimbal Actuator Modeling for a Spin-Stabilized Spacecraft Equipped with a 1DoF Gimbaled-Thruster and two Reaction Wheels	Attitude control of spacecraft during an impulsive orbital maneuver is a vital task. Many spacecraft and launchers use the gimbaled thrust vector control (TVC) in their attitude control system during an orbital maneuver. Mathematical modeling of the gimbal actuator is an important task because we should show the applic...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	431,046
2107.06534	Zeroth and First Order Stochastic Frank-Wolfe Algorithms for Constrained Optimization	This paper considers stochastic convex optimization problems with two sets of constraints: (a) deterministic constraints on the domain of the optimization variable, which are difficult to project onto; and (b) deterministic or stochastic constraints that admit efficient projection. Problems of this form arise frequentl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	246,121
1601.03766	Characterization of the Nonequilibrium Steady State of a Heterogeneous Nonlinear $q$-Voter Model with Zealotry	We introduce an heterogeneous nonlinear $q$-voter model with zealots and two types of susceptible voters, and study its non-equilibrium properties when the population is finite and well mixed. In this two-opinion model, each individual supports one of two parties and is either a zealot or a susceptible voter of type $q...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	50,940
cs/0607075	On entropy for mixtures of discrete and continuous variables	Let $X$ be a discrete random variable with support $S$ and $f : S \to S^\prime$ be a bijection. Then it is well-known that the entropy of $X$ is the same as the entropy of $f(X)$. This entropy preservation property has been well-utilized to establish non-trivial properties of discrete stochastic processes, e.g. queuing...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,589
1906.09086	QoE-Aware Resource Allocation for Crowdsourced Live Streaming: A Machine Learning Approach	Driven by the tremendous technological advancement of personal devices and the prevalence of wireless mobile network accesses, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a better viewers quality of experience (QoE) is the key to maximize the audiences number and increase streaming pro...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	136,060
2404.12294	Bayesian evidence estimation from posterior samples with normalizing flows	We propose a novel method ($floZ$), based on normalizing flows, to estimate the Bayesian evidence (and its numerical uncertainty) from a pre-existing set of samples drawn from the unnormalized posterior distribution. We validate it on distributions whose evidence is known analytically, up to 15 parameter space dimensio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	447,824
2305.03923	Active Continual Learning: On Balancing Knowledge Retention and Learnability	Acquiring new knowledge without forgetting what has been learned in a sequence of tasks is the central focus of continual learning (CL). While tasks arrive sequentially, the training data are often prepared and annotated independently, leading to the CL of incoming supervised learning tasks. This paper considers the un...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	362,565
cs/0204028	Decision Lists for English and Basque	In this paper we describe the systems we developed for the English (lexical and all-words) and Basque tasks. They were all supervised systems based on Yarowsky's Decision Lists. We used Semcor for training in the English all-words task. We defined different feature sets for each language. For Basque, in order to extrac...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	537,548
2002.09786	HarDNN: Feature Map Vulnerability Evaluation in CNNs	As Convolutional Neural Networks (CNNs) are increasingly being employed in safety-critical applications, it is important that they behave reliably in the face of hardware errors. Transient hardware errors may percolate undesirable state during execution, resulting in software-manifested errors which can adversely affec...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	165,178
1805.03789	Reliable and Secure Multishot Network Coding using Linearized Reed-Solomon Codes	Multishot network coding is considered in a worst-case adversarial setting in which an omniscient adversary with unbounded computational resources may inject erroneous packets in up to $t$ links, erase up to $\rho$ packets, and wire-tap up to $\mu$ links, all throughout $\ell$ shots of a linearly-coded network. Assumin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	97,117
1408.3359	Linear Contour Learning: A Method for Supervised Dimension Reduction	We propose a novel approach to sufficient dimension reduction in regression, based on estimating contour directions of negligible variation for the response surface. These directions span the orthogonal complement of the minimal space relevant for the regression, and can be extracted according to a measure of the varia...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	35,372
1805.06051	Graph-based Ontology Summarization: A Survey	Ontologies have been widely used in numerous and varied applications, e.g., to support data modeling, information integration, and knowledge management. With the increasing size of ontologies, ontology understanding, which is playing an important role in different tasks, is becoming more difficult. Consequently, ontolo...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	97,521
2205.03672	Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions	Nonlinear partial differential equations (PDEs) are used to model dynamical processes in a large number of scientific fields, ranging from finance to biology. In many applications standard local models are not sufficient to accurately account for certain non-local phenomena such as, e.g., interactions at a distance. In...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	295,371
2003.08607	Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering	Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data on a target domain, given labeled data on a source domain whose distribution shifts from the target one. Mainstream UDA methods learn aligned features between the two domains, such that a classifier trained on the source features can be read...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,791
1108.1122	Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognition	We employ the face recognition technology developed in house at face.com to a well accepted benchmark and show that without any tuning we are able to considerably surpass state of the art results. Much of the improvement is concentrated in the high-valued performance point of zero false positive matches, where the obta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	11,565
1608.09005	Measuring the Quality of Exercises	This work explores the problem of exercise quality measurement since it is essential for effective management of diseases like cerebral palsy (CP). This work examines the assessment of quality of large amplitude movement (LAM) exercises designed to treat CP in an automated fashion. Exercise data was collected by traine...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	60,420
2108.10517	Uncertainty Quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ball	There are essentially three kinds of approaches to Uncertainty Quantification (UQ): (A) robust optimization, (B) Bayesian, (C) decision theory. Although (A) is robust, it is unfavorable with respect to accuracy and data assimilation. (B) requires a prior, it is generally brittle and posterior estimations can be slow. A...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,918
2405.06712	Digital Diagnostics: The Potential Of Large Language Models In Recognizing Symptoms Of Common Illnesses	The recent swift development of LLMs like GPT-4, Gemini, and GPT-3.5 offers a transformative opportunity in medicine and healthcare, especially in digital diagnostics. This study evaluates each model diagnostic abilities by interpreting a user symptoms and determining diagnoses that fit well with common illnesses, and ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	453,422
1809.00491	Prediction of Electric Multiple Unit Fleet Size Based on Convolutional Neural Network	With the expansion of high-speed railway network and growth of passenger transportation demands, the fleet size of electric multiple unit (EMU) in China needs to be adjusted accordingly. Generally, an EMU train costs tens of millions of dollars which constitutes a significant portion of capital investment. Thus, the pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	106,591
2108.06960	Channel Knowledge Map for Environment-Aware Communications: EM Algorithm for Map Construction	Channel knowledge map (CKM) is an emerging technique to enable environment-aware wireless communications, in which databases with location-specific channel knowledge are used to facilitate or even obviate real-time channel state information acquisition. One fundamental problem for CKM-enabled communication is how to ef...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	250,794
2501.12612	T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image Generation	Text-to-image (T2I) models have rapidly advanced, enabling the generation of high-quality images from text prompts across various domains. However, these models present notable safety concerns, including the risk of generating harmful, biased, or private content. Current research on assessing T2I safety remains in its ...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	526,379
2308.13658	Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs	Validating the safety of Autonomous Vehicles (AVs) operating in open-ended, dynamic environments is challenging as vehicles will eventually encounter safety-critical situations for which there is not representative training data. By increasing the coverage of different road and traffic conditions and by including corne...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	387,999
2210.14488	History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96	Physical parameterizations are used as representations of unresolved subgrid processes within weather and global climate models or coarse-scale turbulent models, whose resolutions are too coarse to resolve small-scale processes. These parameterizations are typically grounded on physically-based, yet empirical, represen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	326,569
2008.02144	FRMDN: Flow-based Recurrent Mixture Density Network	The class of recurrent mixture density networks is an important class of probabilistic models used extensively in sequence modeling and sequence-to-sequence mapping applications. In this class of models, the density of a target sequence in each time-step is modeled by a Gaussian mixture model with the parameters given ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	190,536
2204.07741	Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion	Persuading people to change their opinions is a common practice in online discussion forums on topics ranging from political campaigns to relationship consultation. Enhancing people's ability to write persuasive arguments could not only practice their critical thinking and reasoning but also contribute to the effective...	true	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	291,827
2111.09800	Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates	In 2021 the Johns Hopkins University Applied Physics Laboratory held an internal challenge to develop artificially intelligent (AI) agents that could excel at the collaborative card game Hanabi. Agents were evaluated on their ability to play with human players whom the agents had never previously encountered. This stud...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	267,114
2005.12732	Mathematics of Nested Districts: The Case of Alaska	In eight states, a "nesting rule" requires that each state Senate district be exactly composed of two adjacent state House districts. In this paper we investigate the potential impacts of these nesting rules with a focus on Alaska, where Republicans have a 2/3 majority in the Senate while a Democratic-led coalition con...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	178,812
2305.01735	DiffuSum: Generation Enhanced Extractive Summarization with Diffusion	Extractive summarization aims to form a summary by directly extracting sentences from the source document. Existing works mostly formulate it as a sequence labeling problem by making individual sentence label predictions. This paper proposes DiffuSum, a novel paradigm for extractive summarization, by directly generatin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	361,782
2310.17159	MaxEnt Loss: Constrained Maximum Entropy for Calibration under Out-of-Distribution Shift	We present a new loss function that addresses the out-of-distribution (OOD) calibration problem. While many objective functions have been proposed to effectively calibrate models in-distribution, our findings show that they do not always fare well OOD. Based on the Principle of Maximum Entropy, we incorporate helpful s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	403,020
2112.11347	Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objects	Rendering articulated objects while controlling their poses is critical to applications such as virtual reality or animation for movies. Manipulating the pose of an object, however, requires the understanding of its underlying structure, that is, its joints and how they interact with each other. Unfortunately, assuming...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	272,680
2004.13480	L-Vector: Neural Label Embedding for Domain Adaptation	We propose a novel neural label embedding (NLE) scheme for the domain adaptation of a deep neural network (DNN) acoustic model with unpaired data samples from source and target domains. With NLE method, we distill the knowledge from a powerful source-domain DNN into a dictionary of label embeddings, or l-vectors, one f...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	174,564
2003.01335	ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search	How to discover and evaluate the true strength of models quickly and accurately is one of the key challenges in Neural Architecture Search (NAS). To cope with this problem, we propose an Architecture-Driven Weight Prediction (ADWP) approach for neural architecture search (NAS). In our approach, we first design an archi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	166,620
2210.16739	DuDe: Dual-Decoder Multilingual ASR for Indian Languages using Common Label Set	In a multilingual country like India, multilingual Automatic Speech Recognition (ASR) systems have much scope. Multilingual ASR systems exhibit many advantages like scalability, maintainability, and improved performance over the monolingual ASR systems. However, building multilingual systems for Indian languages is cha...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	327,439
2206.10770	On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL	We study reward-free reinforcement learning (RL) under general non-linear function approximation, and establish sample efficiency and hardness results under various standard structural assumptions. On the positive side, we propose the RFOLIVE (Reward-Free OLIVE) algorithm for sample-efficient reward-free exploration un...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	304,025
2404.08997	Labeled Morphological Segmentation with Semi-Markov Models	We present labeled morphological segmentation, an alternative view of morphological processing that unifies several tasks. From an annotation standpoint, we additionally introduce a new hierarchy of morphotactic tagsets. Finally, we develop \modelname, a discriminative morphological segmentation system that, contrary t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	446,501
2009.13301	A Column Generation based Heuristic for the Tail Assignment Problem	This article proposes an efficient heuristic in accelerating the column generation by parallel resolution of pricing problems for aircrafts in the tail assignment problem (TAP). The approach is able to achieve considerable improvement in resolution time for real life test instances from two major Indian air carriers. T...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	197,705
2302.11412	Data Augmentation for Neural NLP	Data scarcity is a problem that occurs in languages and tasks where we do not have large amounts of labeled data but want to use state-of-the-art models. Such models are often deep learning models that require a significant amount of data to train. Acquiring data for various machine learning problems is accompanied by ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	347,200
2103.00608	Active learning based generative design for the discovery of wide bandgap materials	Active learning has been increasingly applied to screening functional materials from existing materials databases with desired properties. However, the number of known materials deposited in the popular materials databases such as ICSD and Materials Project is extremely limited and consists of just a tiny portion of th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	222,340
2410.10867	Mitigating the Impact of Reference Quality on Evaluation of Summarization Systems with Reference-Free Metrics	Automatic metrics are used as proxies to evaluate abstractive summarization systems when human annotations are too expensive. To be useful, these metrics should be fine-grained, show a high correlation with human annotations, and ideally be independent of reference quality; however, most standard evaluation metrics for...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	true	498,285
2111.04986	Unified Group Fairness on Federated Learning	Federated learning (FL) has emerged as an important machine learning paradigm where a global model is trained based on the private data from distributed clients. However, most of existing FL algorithms cannot guarantee the performance fairness towards different groups because of data distribution shift over groups. In ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	265,665
2006.06380	Pointer Graph Networks	Graph neural networks (GNNs) are typically applied to static graphs that are assumed to be known upfront. This static input structure is often informed purely by insight of the machine learning practitioner, and might not be optimal for the actual task the GNN is solving. In absence of reliable domain expertise, one mi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	181,411
1312.6594	Sequentially Generated Instance-Dependent Image Representations for Classification	In this paper, we investigate a new framework for image classification that adaptively generates spatial representations. Our strategy is based on a sequential process that learns to explore the different regions of any image in order to infer its category. In particular, the choice of regions is specific to each image...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	29,378
2009.07453	Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation	The deployment of widely used Transformer architecture is challenging because of heavy computation load and memory overhead during inference, especially when the target device is limited in computational resources such as mobile or edge devices. Quantization is an effective technique to address such challenges. Our ana...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	195,930
2210.07651	Decentralized Policy Gradient for Nash Equilibria Learning of General-sum Stochastic Games	We study Nash equilibria learning of a general-sum stochastic game with an unknown transition probability density function. Agents take actions at the current environment state and their joint action influences the transition of the environment state and their immediate rewards. Each agent only observes the environment...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	true	false	false	true	323,809
2202.01277	Global Optimization Networks	We consider the problem of estimating a good maximizer of a black-box function given noisy examples. To solve such problems, we propose to fit a new type of function which we call a global optimization network (GON), defined as any composition of an invertible function and a unimodal function, whose unique global maxim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,422
2302.07579	Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks	Deep regression is an important problem with numerous applications. These range from computer vision tasks such as age estimation from photographs, to medical tasks such as ejection fraction estimation from echocardiograms for disease tracking. Semi-supervised approaches for deep regression are notably under-explored c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	345,772
1904.10142	Android Malicious Application Classification Using Clustering	Android malware have been growing at an exponential pace and becomes a serious threat to mobile users. It appears that most of the anti-malware still relies on the signature-based detection system which is generally slow and often not able to detect advanced obfuscated malware. Hence time-to-time various authors have p...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	128,561
2006.07210	Data-driven Koopman Operators for Model-based Shared Control of Human-Machine Systems	We present a data-driven shared control algorithm that can be used to improve a human operator's control of complex dynamic machines and achieve tasks that would otherwise be challenging, or impossible, for the user on their own. Our method assumes no a priori knowledge of the system dynamics. Instead, both the dynamic...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	181,720
1811.06672	Detecting Irregular Patterns in IoT Streaming Data for Fall Detection	Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	true	113,582
2002.11034	Lying on networks: The role of structure and topology in promoting honesty	Lies can have a negating impact on governments, companies, and the society as a whole. Understanding the dynamics of lying is therefore of crucial importance across different fields of research. While lying has been studied before in well-mixed populations, it is a fact that real interactions are rarely well-mixed. Ind...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	165,590
2102.01295	Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation	A high-precision manipulation task, such as needle threading, is challenging. Physiological studies have proposed connecting low-resolution peripheral vision and fast movement to transport the hand into the vicinity of an object, and using high-resolution foveated vision to achieve the accurate homing of the hand to th...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	218,060
2206.13803	FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification	Federated learning (FL), training deep models from decentralized data without privacy leakage, has shown great potential in medical image computing recently. However, considering the ubiquitous class imbalance in medical data, FL can exhibit performance degradation, especially for minority classes (e.g. rare diseases)....	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	305,087
1709.05789	On the Restricted Isometry of the Columnwise Khatri-Rao Product	The columnwise Khatri-Rao product of two matrices is an important matrix type, reprising its role as a structured sensing matrix in many fundamental linear inverse problems. Robust signal recovery in such inverse problems is often contingent on proving the restricted isometry property (RIP) of a certain system matrix e...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	80,956
2305.14986	Non-adversarial Robustness of Deep Learning Methods for Computer Vision	Non-adversarial robustness, also known as natural robustness, is a property of deep learning models that enables them to maintain performance even when faced with distribution shifts caused by natural variations in data. However, achieving this property is challenging because it is difficult to predict in advance the t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	367,374
1504.05225	The maximum-likelihood decoding threshold for graphic codes	For a class $\mathcal{C}$ of binary linear codes, we write $\theta_{\mathcal{C}}\colon (0,1) \to [0,\frac{1}{2}]$ for the maximum-likelihood decoding threshold function of $\mathcal{C}$, the function whose value at $R \in (0,1)$ is the largest bit-error rate $p$ that codes in $\mathcal{C}$ can tolerate with a negligibl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	42,240
1604.01358	Near Capacity Irregular Turbo Code	The purpose of this study is to construct a near capacity Irregular Turbo Code and to evaluate its performance over Gaussian channel. The methodology used to evaluate and measure the performance of the new design is by simulating the system by developing a software platform using Matlab. The simulation carried out by i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	54,187
2411.09403	Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning	Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. It seeks to revolutionize machine learning by harnessing the unique capabilities of quantum mechanics and employs machine learning techniques to advance quantum computing research. Thi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	508,236
0809.3447	An Exploratory Study of Calendar Use	In this paper, we report on findings from an ethnographic study of how people use their calendars for personal information management (PIM). Our participants were faculty, staff and students who were not required to use or contribute to any specific calendaring solution, but chose to do so anyway. The study was conduct...	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	2,384

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