{"list":"other","total":52,"rows":[{"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":"1812.11118","title":"Reconciling modern machine learning practice and the bias-variance trade-off","created":"2018-12-28","cited_by_count":927},{"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":"1412.1897","title":"Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images","created":"2014-12-05","cited_by_count":776},{"id":"2107.03342","title":"A Survey of Uncertainty in Deep Neural Networks","created":"2021-07-07","cited_by_count":680},{"id":"1912.02292","title":"Deep Double Descent: Where Bigger Models and More Data Hurt","created":"2019-12-04","cited_by_count":672},{"id":"1902.02476","title":"A Simple Baseline for Bayesian Uncertainty in Deep Learning","created":"2019-02-07","cited_by_count":560},{"id":"1906.11300","title":"Benign Overfitting in Linear Regression","created":"2019-06-26","cited_by_count":549},{"id":"1903.08560","title":"Surprises in High-Dimensional Ridgeless Least Squares Interpolation","created":"2019-03-19","cited_by_count":513},{"id":"1908.05355","title":"The generalization error of random features regression: Precise asymptotics and double descent curve","created":"2019-08-14","cited_by_count":413},{"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":"2106.14806","title":"Laplace Redux -- Effortless Bayesian Deep Learning","created":"2021-06-28","cited_by_count":365},{"id":"2002.09437","title":"Calibrating Deep Neural Networks using Focal Loss","created":"2020-02-21","cited_by_count":357},{"id":"2007.06823","title":"Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users","created":"2020-07-14","cited_by_count":338},{"id":"1812.05720","title":"Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem","created":"2018-12-13","cited_by_count":323},{"id":"2002.10118","title":"Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks","created":"2020-02-24","cited_by_count":231},{"id":"1507.03003","title":"High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification","created":"2015-07-10","cited_by_count":148},{"id":"1511.02476","title":"Statistical physics of inference: Thresholds and algorithms","created":"2015-11-08","cited_by_count":128},{"id":"2002.09339","title":"Generalisation error in learning with random features and the hidden manifold model","created":"2020-02-21","cited_by_count":127},{"id":"2102.08127","title":"Learning curves of generic features maps for realistic datasets with a teacher-student model","created":"2021-02-16","cited_by_count":126},{"id":"1708.03395","title":"Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models","created":"2017-08-10","cited_by_count":116},{"id":"2006.14709","title":"The Gaussian equivalence of generative models for learning with shallow neural networks","created":"2020-06-25","cited_by_count":113},{"id":"2003.01897","title":"Optimal Regularization Can Mitigate Double Descent","created":"2020-03-04","cited_by_count":109},{"id":"2003.01054","title":"Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime","created":"2020-03-02","cited_by_count":95},{"id":"1803.06964","title":"A modern maximum-likelihood theory for high-dimensional logistic regression","created":"2018-03-19","cited_by_count":81},{"id":"1911.05822","title":"A Model of Double Descent for High-dimensional Binary Linear Classification","created":"2019-11-13","cited_by_count":81},{"id":"1702.05419","title":"A Random Matrix Approach to Neural Networks","created":"2017-02-17","cited_by_count":80},{"id":"2202.08832","title":"Universality of empirical risk minimization","created":"2022-02-17","cited_by_count":75},{"id":"1810.09665","title":"A jamming transition from under- to over-parametrization affects loss landscape and generalization","created":"2018-10-22","cited_by_count":67},{"id":"1809.09349","title":"The jamming transition as a paradigm to understand the loss landscape of deep neural networks","created":"2018-09-25","cited_by_count":62},{"id":"1806.05451","title":"The committee machine: Computational to statistical gaps in learning a two-layers neural network","created":"2018-06-14","cited_by_count":58},{"id":"1805.09785","title":"Entropy and mutual information in models of deep neural networks","created":"2018-05-24","cited_by_count":51},{"id":"2006.06560","title":"Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization","created":"2020-06-11","cited_by_count":51},{"id":"1804.09753","title":"The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression","created":"2018-04-25","cited_by_count":46},{"id":"1303.7291","title":"A framework to characterize performance of LASSO algorithms","created":"2013-03-29","cited_by_count":40},{"id":"2102.07856","title":"Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification","created":"2021-02-15","cited_by_count":39},{"id":"2109.11905","title":"Graph-based Approximate Message Passing Iterations","created":"2021-09-24","cited_by_count":35},{"id":"2002.12903","title":"The estimation error of general first order methods","created":"2020-02-28","cited_by_count":33},{"id":"2103.01519","title":"Hessian Eigenspectra of More Realistic Nonlinear Models","created":"2021-03-02","cited_by_count":33},{"id":"1805.11916","title":"On the Spectrum of Random Features Maps of High Dimensional Data","created":"2018-05-30","cited_by_count":28},{"id":"1904.03090","title":"Eigenvalue distribution of nonlinear models of random matrices","created":"2019-04-05","cited_by_count":27},{"id":"2001.08049","title":"On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation","created":"2020-01-22","cited_by_count":23},{"id":"1905.12385","title":"The spiked matrix model with generative priors","created":"2019-05-29","cited_by_count":20},{"id":"2201.13383","title":"Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension","created":"2022-01-31","cited_by_count":19},{"id":"2202.03295","title":"Theoretical characterization of uncertainty in high-dimensional linear classification","created":"2022-02-07","cited_by_count":18},{"id":"2203.12094","title":"Learning curves for the multi-class teacher-student perceptron","created":"2022-03-22","cited_by_count":17},{"id":"2107.09028","title":"Structured Stochastic Gradient MCMC","created":"2021-07-19","cited_by_count":12},{"id":"2009.12939","title":"Strong replica symmetry for high-dimensional disordered log-concave Gibbs measures","created":"2020-09-27","cited_by_count":6},{"id":"2107.06936","title":"Performance of Bayesian linear regression in a model with mismatch","created":"2021-07-14","cited_by_count":6},{"id":"1902.05539","title":"A Parsimonious Tour of Bayesian Model Uncertainty","created":"2019-02-14","cited_by_count":5}],"next_cursor":null}