{"list":"other","total":86,"rows":[{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1706.04599","title":"On Calibration of Modern Neural Networks","created":"2017-06-14","cited_by_count":4960},{"id":"1506.02142","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","created":"2015-06-06","cited_by_count":4448},{"id":"1612.01474","title":"Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles","created":"2016-12-05","cited_by_count":3926},{"id":"2010.08895","title":"Fourier Neural Operator for Parametric Partial Differential Equations","created":"2020-10-18","cited_by_count":2908},{"id":"1610.02136","title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks","created":"2016-10-07","cited_by_count":2385},{"id":"1703.04977","title":"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?","created":"2017-03-15","cited_by_count":2352},{"id":"1606.09375","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","created":"2016-06-30","cited_by_count":2089},{"id":"1807.03888","title":"A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks","created":"2018-07-10","cited_by_count":1485},{"id":"1601.00670","title":"Variational Inference: A Review for Statisticians","created":"2016-01-04","cited_by_count":1472},{"id":"1906.02530","title":"Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift","created":"2019-06-06","cited_by_count":1287},{"id":"1910.09457","title":"Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods","created":"2019-10-21","cited_by_count":913},{"id":"2011.06225","title":"A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges","created":"2020-11-12","cited_by_count":819},{"id":"2107.07511","title":"A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification","created":"2021-07-15","cited_by_count":792},{"id":"1707.06887","title":"A Distributional Perspective on Reinforcement Learning","created":"2017-07-21","cited_by_count":765},{"id":"1806.01768","title":"Evidential Deep Learning to Quantify Classification Uncertainty","created":"2018-06-05","cited_by_count":732},{"id":"2107.03342","title":"A Survey of Uncertainty in Deep Neural Networks","created":"2021-07-07","cited_by_count":680},{"id":"2109.01050","title":"Characterizing possible failure modes in physics-informed neural networks","created":"2021-09-02","cited_by_count":643},{"id":"2107.03502","title":"CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation","created":"2021-07-07","cited_by_count":582},{"id":"1802.10501","title":"Predictive Uncertainty Estimation via Prior Networks","created":"2018-02-28","cited_by_count":557},{"id":"2009.00236","title":"A Survey of Deep Active Learning","created":"2020-08-30","cited_by_count":554},{"id":"1710.10044","title":"Distributional Reinforcement Learning with Quantile Regression","created":"2017-10-27","cited_by_count":474},{"id":"1904.01685","title":"Measuring Calibration in Deep Learning","created":"2019-04-02","cited_by_count":446},{"id":"1511.02222","title":"Deep Kernel Learning","created":"2015-11-06","cited_by_count":445},{"id":"1805.10965","title":"Lipschitz regularity of deep neural networks: analysis and efficient estimation","created":"2018-05-28","cited_by_count":415},{"id":"1811.05320","title":"T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction","created":"2018-11-12","cited_by_count":401},{"id":"2006.10108","title":"Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness","created":"2020-06-17","cited_by_count":389},{"id":"2007.06823","title":"Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users","created":"2020-07-14","cited_by_count":338},{"id":"1905.11001","title":"On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks","created":"2019-05-27","cited_by_count":305},{"id":"2101.05982","title":"Randomized Ensembled Double Q-Learning: Learning Fast Without a Model","created":"2021-01-15","cited_by_count":288},{"id":"1806.05034","title":"A Probabilistic U-Net for Segmentation of Ambiguous Images","created":"2018-06-13","cited_by_count":283},{"id":"1806.03335","title":"Randomized Prior Functions for Deep Reinforcement Learning","created":"2018-06-08","cited_by_count":219},{"id":"2010.05788","title":"PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks","created":"2020-10-12","cited_by_count":211},{"id":"2012.14983","title":"Reducing conversational agents' overconfidence through linguistic calibration","created":"2020-12-30","cited_by_count":210},{"id":"1805.07810","title":"Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting","created":"2018-05-20","cited_by_count":201},{"id":"2209.15558","title":"Out-of-Distribution Detection and Selective Generation for Conditional Language Models","created":"2022-09-30","cited_by_count":197},{"id":"1503.01057","title":"Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)","created":"2015-03-03","cited_by_count":177},{"id":"2201.07766","title":"Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons","created":"2022-01-19","cited_by_count":170},{"id":"2006.09239","title":"Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts","created":"2020-06-16","cited_by_count":164},{"id":"2202.07282","title":"Adaptive Conformal Predictions for Time Series","created":"2022-02-15","cited_by_count":161},{"id":"1703.01961","title":"Multiplicative Normalizing Flows for Variational Bayesian Neural Networks","created":"2017-03-06","cited_by_count":157},{"id":"2006.13570","title":"Hyperparameter Ensembles for Robustness and Uncertainty Quantification","created":"2020-06-24","cited_by_count":150},{"id":"2003.11339","title":"Data Uncertainty Learning in Face Recognition","created":"2020-03-25","cited_by_count":134},{"id":"2008.05030","title":"Reliable Post hoc Explanations: Modeling Uncertainty in Explainability","created":"2020-08-11","cited_by_count":125},{"id":"2201.05273","title":"Pretrained Language Models for Text Generation: A Survey","created":"2022-01-14","cited_by_count":122},{"id":"1811.04026","title":"Adversarial Uncertainty Quantification in Physics-Informed Neural Networks","created":"2018-11-09","cited_by_count":120},{"id":"1802.07167","title":"High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach","created":"2018-02-20","cited_by_count":117},{"id":"2110.02034","title":"Dropout Q-Functions for Doubly Efficient Reinforcement Learning","created":"2021-10-05","cited_by_count":114},{"id":"1904.04620","title":"Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving","created":"2019-04-09","cited_by_count":105},{"id":"2203.15046","title":"AUC Maximization in the Era of Big Data and AI: A Survey","created":"2022-03-28","cited_by_count":100},{"id":"2107.10161","title":"Evidential Deep Learning for Open Set Action Recognition","created":"2021-07-21","cited_by_count":96},{"id":"1712.02390","title":"Noisy Natural Gradient as Variational Inference","created":"2017-12-06","cited_by_count":94},{"id":"2102.11409","title":"On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty","created":"2021-02-22","cited_by_count":87},{"id":"1811.05910","title":"Deep Bayesian Inversion","created":"2018-11-14","cited_by_count":61},{"id":"2006.08573","title":"Neural Ensemble Search for Uncertainty Estimation and Dataset Shift","created":"2020-06-15","cited_by_count":56},{"id":"2012.03058","title":"BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations","created":"2020-12-05","cited_by_count":51},{"id":"1901.11228","title":"Uncertainty Quantification in Deep MRI Reconstruction","created":"2019-01-31","cited_by_count":49},{"id":"2205.00403","title":"A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness","created":"2022-05-01","cited_by_count":49},{"id":"2210.09184","title":"Packed-Ensembles for Efficient Uncertainty Estimation","created":"2022-10-17","cited_by_count":49},{"id":"2105.11982","title":"Quantifying Uncertainty in Deep Spatiotemporal Forecasting","created":"2021-05-25","cited_by_count":48},{"id":"1805.03430","title":"Deep Directional Statistics: Pose Estimation with Uncertainty Quantification","created":"2018-05-09","cited_by_count":45},{"id":"1906.03842","title":"Analyzing the Role of Model Uncertainty for Electronic Health Records","created":"2019-06-10","cited_by_count":41},{"id":"2301.12736","title":"On Second-Order Scoring Rules for Epistemic Uncertainty Quantification","created":"2023-01-30","cited_by_count":38},{"id":"2202.03101","title":"Nonparametric Uncertainty Quantification for Single Deterministic Neural Network","created":"2022-02-07","cited_by_count":36},{"id":"1805.09460","title":"Cautious Deep Learning","created":"2018-05-24","cited_by_count":35},{"id":"1802.07079","title":"Structured Uncertainty Prediction Networks","created":"2018-02-20","cited_by_count":34},{"id":"1811.04504","title":"SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient","created":"2018-11-11","cited_by_count":34},{"id":"2010.02720","title":"Learnable Uncertainty under Laplace Approximations","created":"2020-10-06","cited_by_count":29},{"id":"2102.04399","title":"How to Stay Curious while Avoiding Noisy TVs using Aleatoric Uncertainty Estimation","created":"2021-02-08","cited_by_count":27},{"id":"2106.02993","title":"PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics","created":"2021-06-06","cited_by_count":21},{"id":"2110.15784","title":"Convergence of Uncertainty Sampling for Active Learning","created":"2021-10-29","cited_by_count":19},{"id":"1609.08203","title":"Variational Inference with Hamiltonian Monte Carlo","created":"2016-09-26","cited_by_count":17},{"id":"2102.13625","title":"Learning Prediction Intervals for Regression: Generalization and Calibration","created":"2021-02-26","cited_by_count":16},{"id":"2105.14594","title":"Sparse Uncertainty Representation in Deep Learning with Inducing Weights","created":"2021-05-30","cited_by_count":16},{"id":"2108.13054","title":"Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks","created":"2021-08-30","cited_by_count":16},{"id":"2201.13279","title":"UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs","created":"2022-01-31","cited_by_count":16},{"id":"2009.10235","title":"Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks","created":"2020-09-22","cited_by_count":15},{"id":"2007.09670","title":"Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles","created":"2020-07-19","cited_by_count":14},{"id":"1902.10189","title":"Variational Inference to Measure Model Uncertainty in Deep Neural Networks","created":"2019-02-26","cited_by_count":12},{"id":"2206.09319","title":"TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification","created":"2022-06-19","cited_by_count":11},{"id":"2204.01618","title":"Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting","created":"2022-04-04","cited_by_count":10},{"id":"2006.06270","title":"Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction","created":"2020-06-11","cited_by_count":9},{"id":"1909.07698","title":"Compositional uncertainty in deep Gaussian processes","created":"2019-09-17","cited_by_count":8},{"id":"1904.01334","title":"Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks","created":"2019-04-02","cited_by_count":7},{"id":"1710.05888","title":"Discriminative Learning of Prediction Intervals","created":"2017-10-16","cited_by_count":5}],"next_cursor":null}