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Interplay between Bayesian Neural Networks and Deep Learning: A Survey

https://doi.org/10.2139/ssrn.5100162
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Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
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Pattern Recognition and Machine Learning
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Bayesian Learning for Neural Networks
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Rademacher Processes and Bounding the Risk of Function Learning
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Monte Carlo Statistical Methods
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Computer Vision
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Improving the Mean Field Approximation Via the Use of Mixture Distributions
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Approximation by superpositions of a sigmoidal function
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A survey on semi-supervised learning
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Zero variance Markov chain Monte Carlo for Bayesian estimators
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Hybrid Monte Carlo
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The theory of hybrid stochastic algorithms
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Multilayer feedforward networks are universal approximators
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On the relative expressiveness of Bayesian and neural networks
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Adaptive multi-stage integrators for optimal energy conservation in molecular simulations
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Probabilistic models of cognition: exploring representations and inductive biases
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Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
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Convex Optimization
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Bayesian Time Series Models
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An Introduction to Variational Methods for Graphical Models
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Learning representations by back-propagating errors
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A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit
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Equation of State Calculations by Fast Computing Machines
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Sparse subnetwork inference for neural network epistemic uncertainty estimation with improved Hessian approximation
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
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Understanding the Metropolis-Hastings Algorithm
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Variational Inference: A Review for Statisticians
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Stochastic Gradient Markov Chain Monte Carlo
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A Deep Generative Approach to Conditional Sampling
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Literature survey on low rank approximation of matrices
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Wide neural networks of any depth evolve as linear models under gradient descent *
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On asymptotic properties of predictive distributions
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Information Theory, Inference, and Learning Algorithms
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Correlated Input-Dependent Label Noise in Large-Scale Image Classification
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Defining and Quantifying the Emergence of Sparse Concepts in DNNs
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Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
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The Global Landscape of Neural Networks: An Overview
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Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks
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Multi-Objective Estimation of Optimal Prediction Intervals for Wind Power Forecasting
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Priors in Bayesian Deep Learning: A Review
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Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
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Regression Shrinkage and Selection Via the Lasso
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Control Functionals for Monte Carlo Integration
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Numerical Integrators for the Hybrid Monte Carlo Method
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On the Benefit of Width for Neural Networks: Disappearance of Basins
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Keeping the neural networks simple by minimizing the description length of the weights
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Some PAC-Bayesian theorems
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Understanding deep learning (still) requires rethinking generalization
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A differential approach to inference in Bayesian networks
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On “Natural” Learning and Pruning in Multilayered Perceptrons
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Backpropagation Applied to Handwritten Zip Code Recognition
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Neural Networks and the Bias/Variance Dilemma
inferredcites →https://doi.org/10.1162/neco.2008.08-07-592openalex-snapshot
The Variational Gaussian Approximation Revisited
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The Art of Data Augmentation
inferredcites →https://doi.org/10.1201/9781003453420-2openalex-snapshot
MCMC Using Hamiltonian Dynamics
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Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions
inferredcites →https://doi.org/10.1214/12-ejs675openalex-snapshot
The Bernstein-Von-Mises theorem under misspecification
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Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It
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On the rate of convergence of fully connected deep neural network regression estimates
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General Bayesian Inference over the Stiefel Manifold via the Givens Transform
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A Primer on Bayesian Neural Networks: Review and Debates
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On Information and Sufficiency
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Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors
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Uncertainty in Neural Networks: Approximately Bayesian Ensembling
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User-friendly Introduction to PAC-Bayes Bounds
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Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks
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Improving Bayesian Neural Networks by Adversarial Sampling
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Deep Convolutional Networks as shallow Gaussian Processes
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Statistical Learning Theory
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The Comparison and Evaluation of Forecasters
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Exponential Convergence of Langevin Distributions and Their Discrete Approximations
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The Foundations of Statistics
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The Prior Can Often Only Be Understood in the Context of the Likelihood
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Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
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Relational inductive biases, deep learning, and graph networks
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Variational Bayesian Inference with Stochastic Search
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Revisiting Natural Gradient for Deep Networks
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
inferredcites →https://doi.org/10.48550/arxiv.1402.1869openalex-snapshot
On the Number of Linear Regions of Deep Neural Networks
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Stochastic Gradient Hamiltonian Monte Carlo
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The Loss Surfaces of Multilayer Networks
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New insights and perspectives on the natural gradient method
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
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Optimizing Neural Networks with Kronecker-factored Approximate Curvature
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On the Expressive Power of Deep Learning: A Tensor Analysis
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The Power of Depth for Feedforward Neural Networks
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Fast Rates for General Unbounded Loss Functions: from ERM to Generalized Bayes
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
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Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
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What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
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Empirical Analysis of the Hessian of Over-Parametrized Neural Networks
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On Calibration of Modern Neural Networks
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Spectrally-normalized margin bounds for neural networks
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A Survey of Model Compression and Acceleration for Deep Neural Networks
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Deep Neural Networks as Gaussian Processes
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Vprop: Variational Inference using RMSprop
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Sensitivity and Generalization in Neural Networks: an Empirical Study
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Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
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Fast Approximate Natural Gradient Descent in a Kronecker-factored Eigenbasis
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Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
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Neural Tangent Kernel: Convergence and Generalization in Neural Networks
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The Limitations of Model Uncertainty in Adversarial Settings
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A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks
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Traditional and Heavy-Tailed Self Regularization in Neural Network Models
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A Simple Baseline for Bayesian Uncertainty in Deep Learning
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Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1902.04698openalex-snapshot
Identity Crisis: Memorization and Generalization under Extreme Overparameterization
inferredcites →https://doi.org/10.48550/arxiv.1903.05779openalex-snapshot
Functional Variational Bayesian Neural Networks
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Measuring Calibration in Deep Learning
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On Exact Computation with an Infinitely Wide Neural Net
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Practical Deep Learning with Bayesian Principles
inferredcites →https://doi.org/10.48550/arxiv.1906.02530openalex-snapshot
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
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Large Scale Structure of Neural Network Loss Landscapes
inferredcites →https://doi.org/10.48550/arxiv.1907.02911openalex-snapshot
Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape
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On the Expressiveness of Approximate Inference in Bayesian Neural Networks
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Deep Ensembles: A Loss Landscape Perspective
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Learning under Model Misspecification: Applications to Variational and Ensemble methods
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The Case for Bayesian Deep Learning
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Bayesian Deep Learning and a Probabilistic Perspective of Generalization
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Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks
inferredcites →https://doi.org/10.48550/arxiv.2006.04740openalex-snapshot
The Heavy-Tail Phenomenon in SGD
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Infinite attention: NNGP and NTK for deep attention networks
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On the role of data in PAC-Bayes bounds
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Dropout as a Regularizer of Interaction Effects
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Cold Posteriors and Aleatoric Uncertainty
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Improving predictions of Bayesian neural nets via local linearization
inferredcites →https://doi.org/10.48550/arxiv.2010.14689openalex-snapshot
Bayesian Deep Learning via Subnetwork Inference
inferredcites →https://doi.org/10.48550/arxiv.2011.09421openalex-snapshot
Understanding Variational Inference in Function-Space
inferredcites →https://doi.org/10.48550/arxiv.2012.10351openalex-snapshot
Universal Approximation in Dropout Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2102.12567openalex-snapshot
Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2104.14421openalex-snapshot
What Are Bayesian Neural Network Posteriors Really Like?
inferredcites →https://doi.org/10.48550/arxiv.2106.06596openalex-snapshot
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect
inferredcites →https://doi.org/10.48550/arxiv.2106.11905openalex-snapshot
Dangers of Bayesian Model Averaging under Covariate Shift
inferredcites →https://doi.org/10.48550/arxiv.2106.14806openalex-snapshot
Laplace Redux -- Effortless Bayesian Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.2202.11678openalex-snapshot
Bayesian Model Selection, the Marginal Likelihood, and Generalization
inferredcites →https://doi.org/10.48550/arxiv.2205.13900openalex-snapshot
How Tempering Fixes Data Augmentation in Bayesian Neural Networks
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Emergent Abilities of Large Language Models
inferredcites →https://doi.org/10.48550/arxiv.2206.11173openalex-snapshot
Cold Posteriors through PAC-Bayes
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SAM as an Optimal Relaxation of Bayes
inferredcites →https://doi.org/10.48550/arxiv.2211.06291openalex-snapshot
Do Bayesian Neural Networks Need To Be Fully Stochastic?
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Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts
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Explaining grokking through circuit efficiency
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Unified View of Grokking, Double Descent and Emergent Abilities: A Perspective from Circuits Competition
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On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
inferredcites →https://doi.org/10.52202/068431-2696openalex-snapshot
Is Out-Of-Distribution Detection Learnable?
inferredcites →https://doi.org/10.5555/2946645.2946652openalex-snapshot
Consistency and fluctuations for stochastic gradient Langevin dynamics
inferredcites →https://doi.org/10.5555/3122009.3208015openalex-snapshot
Stochastic Gradient Descent as Approximate Bayesian Inference
inferredcites →https://doi.org/10.56021/9780801869822openalex-snapshot
Algebra of Probable Inference
inferredcites →https://doi.org/10.61603/ceas.v2i1.33openalex-snapshot
Auto-Encoding Variational Bayes
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The Prior Can Often Only Be Understood in the Context of the Likelihood
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