{"total":89,"rows":[{"id":"2107.03342","title":"A Survey of Uncertainty in Deep Neural Networks","created":"2021-07-07","cited_by_count":680,"area":null},{"id":"2106.14806","title":"Laplace Redux -- Effortless Bayesian Deep Learning","created":"2021-06-28","cited_by_count":365,"area":null},{"id":"2104.04975","title":"Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning","created":"2021-04-11","cited_by_count":106,"area":null},{"id":"2105.06868","title":"Priors in Bayesian Deep Learning: A Review","created":"2021-05-14","cited_by_count":101,"area":null},{"id":"2107.04562","title":"The Bayesian Learning Rule","created":"2021-07-09","cited_by_count":99,"area":null},{"id":"2403.05175","title":"Continual Learning and Catastrophic Forgetting","created":"2024-03-08","cited_by_count":77,"area":null},{"id":"2206.08720","title":"Fast Finite Width Neural Tangent Kernel","created":"2022-06-17","cited_by_count":62,"area":null},{"id":"2402.00809","title":"Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI","created":"2024-02-01","cited_by_count":62,"area":null},{"id":"2203.09410","title":"A Framework and Benchmark for Deep Batch Active Learning for Regression","created":"2022-03-17","cited_by_count":49,"area":null},{"id":"2205.00403","title":"A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness","created":"2022-05-01","cited_by_count":49,"area":null},{"id":"2202.10638","title":"Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations","created":"2022-02-22","cited_by_count":42,"area":null},{"id":"2312.17199","title":"Tractable Function-Space Variational Inference in Bayesian Neural Networks","created":"2023-12-28","cited_by_count":41,"area":null},{"id":"2103.01439","title":"Fast Adaptation with Linearized Neural Networks","created":"2021-03-02","cited_by_count":32,"area":null},{"id":"2206.08900","title":"Adapting the Linearised Laplace Model Evidence for Modern Deep Learning","created":"2022-06-17","cited_by_count":29,"area":null},{"id":"2210.12642","title":"Accelerated Linearized Laplace Approximation for Bayesian Deep Learning","created":"2022-10-23","cited_by_count":28,"area":null},{"id":"2205.00165","title":"NeuralEF: Deconstructing Kernels by Deep Neural Networks","created":"2022-04-30","cited_by_count":27,"area":null},{"id":"2309.16314","title":"A Primer on Bayesian Neural Networks: Review and Debates","created":"2023-09-28","cited_by_count":27,"area":null},{"id":"2111.03577","title":"Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning","created":"2021-11-05","cited_by_count":25,"area":null},{"id":"2208.01565","title":"Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs","created":"2022-08-02","cited_by_count":25,"area":null},{"id":"2405.03425","title":"Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models","created":"2024-05-06","cited_by_count":20,"area":null},{"id":"2501.06773","title":"Pareto Set Learning for Multi-Objective Reinforcement Learning","created":"2025-01-12","cited_by_count":20,"area":"deeprl"},{"id":"2210.04994","title":"Sampling-based inference for large linear models, with application to linearised Laplace","created":"2022-10-10","cited_by_count":18,"area":null},{"id":"2302.12565","title":"Variational Linearized Laplace Approximation for Bayesian Deep Learning","created":"2023-02-24","cited_by_count":18,"area":null},{"id":"2406.03334","title":"Reparameterization invariance in approximate Bayesian inference","created":"2024-06-05","cited_by_count":17,"area":null},{"id":"2105.14594","title":"Sparse Uncertainty Representation in Deep Learning with Inducing Weights","created":"2021-05-30","cited_by_count":16,"area":null},{"id":"2310.19273","title":"The Memory Perturbation Equation: Understanding Model's Sensitivity to Data","created":"2023-10-30","cited_by_count":16,"area":null},{"id":"2305.16905","title":"Improving Neural Additive Models with Bayesian Principles","created":"2023-05-26","cited_by_count":15,"area":null},{"id":"2306.07158","title":"Riemannian Laplace approximations for Bayesian neural networks","created":"2023-06-12","cited_by_count":15,"area":null},{"id":"2111.01721","title":"Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees","created":"2021-11-02","cited_by_count":13,"area":null},{"id":"2109.09690","title":"Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes","created":"2021-09-20","cited_by_count":12,"area":null},{"id":"2406.05072","title":"Linearization Turns Neural Operators into Function-Valued Gaussian Processes","created":"2024-06-07","cited_by_count":12,"area":null},{"id":"2407.13711","title":"FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning","created":"2024-07-18","cited_by_count":11,"area":null},{"id":"2502.02870","title":"Uncertainty Quantification with the Empirical Neural Tangent Kernel","created":"2025-02-05","cited_by_count":11,"area":null},{"id":"2203.00479","title":"Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior","created":"2022-02-28","cited_by_count":10,"area":null},{"id":"2304.08309","title":"Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization","created":"2023-04-17","cited_by_count":10,"area":null},{"id":"2306.03968","title":"Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels","created":"2023-06-06","cited_by_count":10,"area":null},{"id":"2504.16555","title":"Confidence Sequences for Generalized Linear Models via Regret Analysis","created":"2025-04-23","cited_by_count":10,"area":null},{"id":"2410.05026","title":"Active Fine-Tuning of Multi-Task Policies","created":"2024-10-07","cited_by_count":9,"area":"offlinerl"},{"id":"2403.02251","title":"A prediction rigidity formalism for low-cost uncertainties in trained neural networks","created":"2024-03-04","cited_by_count":8,"area":null},{"id":"2403.10929","title":"Function-space Parameterization of Neural Networks for Sequential Learning","created":"2024-03-16","cited_by_count":8,"area":null},{"id":"2106.02613","title":"Bridging the Gap Between Target Networks and Functional Regularization","created":"2021-06-04","cited_by_count":7,"area":null},{"id":"2209.15208","title":"Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel","created":"2022-09-30","cited_by_count":7,"area":null},{"id":"2302.06544","title":"Probabilistic Circuits That Know What They Don't Know","created":"2023-02-13","cited_by_count":7,"area":null},{"id":"2302.09574","title":"Guided Deep Kernel Learning","created":"2023-02-19","cited_by_count":6,"area":null},{"id":"2305.13290","title":"Uncertainty and Structure in Neural Ordinary Differential Equations","created":"2023-05-22","cited_by_count":6,"area":null},{"id":"2310.00137","title":"On the Disconnect Between Theory and Practice of Neural Networks: Limits of the NTK Perspective","created":"2023-09-29","cited_by_count":6,"area":null},{"id":"2506.14262","title":"Knowledge Adaptation as Posterior Correction","created":"2025-06-17","cited_by_count":6,"area":null},{"id":"2307.07753","title":"Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks","created":"2023-07-15","cited_by_count":4,"area":null},{"id":"2310.10171","title":"On permutation symmetries in Bayesian neural network posteriors: a variational perspective","created":"2023-10-16","cited_by_count":4,"area":null},{"id":"2501.17325","title":"Connecting Federated ADMM to Bayes","created":"2025-01-28","cited_by_count":4,"area":null},{"id":"2201.12724","title":"Stochastic Neural Networks with Infinite Width are Deterministic","created":"2022-01-30","cited_by_count":3,"area":null},{"id":"2206.11173","title":"Cold Posteriors through PAC-Bayes","created":"2022-06-22","cited_by_count":3,"area":null},{"id":"2406.04317","title":"Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks","created":"2024-06-06","cited_by_count":3,"area":null},{"id":"2407.18707","title":"Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection","created":"2024-07-26","cited_by_count":3,"area":null},{"id":"2410.16901","title":"Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections","created":"2024-10-22","cited_by_count":3,"area":null},{"id":"2108.09507","title":"Shift-Curvature, SGD, and Generalization","created":"2021-08-21","cited_by_count":2,"area":null},{"id":"2307.06093","title":"Online Laplace Model Selection Revisited","created":"2023-07-12","cited_by_count":2,"area":null},{"id":"2310.20285","title":"Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty","created":"2023-10-31","cited_by_count":2,"area":null},{"id":"2402.15441","title":"Active Few-Shot Fine-Tuning","created":"2024-02-13","cited_by_count":2,"area":null},{"id":"2412.04177","title":"Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning","created":"2024-12-05","cited_by_count":2,"area":null},{"id":"2503.07482","title":"How does Bayesian Sampling help Membership Inference Attacks?","created":"2025-03-10","cited_by_count":2,"area":"safety"},{"id":"2512.01384","title":"CLAPS: Aleatoric-Epistemic Scaling via Last-Layer Laplace for Conformal Regression","created":"2025-12-01","cited_by_count":2,"area":null},{"id":"2203.07411","title":"On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel","created":"2022-03-14","cited_by_count":1,"area":null},{"id":"2205.00163","title":"Deep Ensemble as a Gaussian Process Approximate Posterior","created":"2022-04-30","cited_by_count":1,"area":null},{"id":"2211.06457","title":"The Implicit Delta Method","created":"2022-11-11","cited_by_count":1,"area":null},{"id":"2211.15475","title":"Introduction and Exemplars of Uncertainty Decomposition","created":"2022-11-17","cited_by_count":1,"area":null},{"id":"2301.09269","title":"M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion","created":"2023-01-23","cited_by_count":1,"area":null},{"id":"2303.02859","title":"Bayesian inference with finitely wide neural networks","created":"2023-03-06","cited_by_count":1,"area":null},{"id":"2309.02195","title":"Sparse Function-space Representation of Neural Networks","created":"2023-09-05","cited_by_count":1,"area":null},{"id":"2506.07088","title":"Pointwise confidence estimation in the non-linear $\\ell^2$-regularized least squares","created":"2025-06-08","cited_by_count":1,"area":null},{"id":"2507.20272","title":"Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence","created":"2025-07-27","cited_by_count":1,"area":null},{"id":"2604.03858","title":"A Bayesian Information-Theoretic Approach to Data Attribution","created":"2026-04-04","cited_by_count":1,"area":null},{"id":"2604.26673","title":"Laplace Approximation for Bayesian Tensor Network Kernel Machines","created":"2026-04-29","cited_by_count":1,"area":null},{"id":"2105.11376","title":"Can we imitate the principal investor's behavior to learn option price?","created":"2021-05-24","cited_by_count":0,"area":null},{"id":"2210.12282","title":"Bridging the Gap Between Target Networks and Functional Regularization","created":"2022-10-21","cited_by_count":0,"area":null},{"id":"2307.06055","title":"Function-Space Regularization for Deep Bayesian Classification","created":"2023-07-12","cited_by_count":0,"area":null},{"id":"2307.06424","title":"Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations","created":"2023-07-12","cited_by_count":0,"area":null},{"id":"2311.14371","title":"Federated Transformed Learning for a Circular, Secure, and Tiny AI","created":"2023-11-24","cited_by_count":0,"area":null},{"id":"2312.00232","title":"Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks","created":"2023-11-30","cited_by_count":0,"area":null},{"id":"2403.10671","title":"Variation Due to Regularization Tractably Recovers Bayesian Deep Learning","created":"2024-03-15","cited_by_count":0,"area":null},{"id":"2404.10481","title":"BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction","created":"2024-04-16","cited_by_count":0,"area":null},{"id":"2502.07273","title":"Variational Learning Induces Adaptive Label Smoothing","created":"2025-02-11","cited_by_count":0,"area":null},{"id":"2505.20235","title":"Variational Deep Learning via Implicit Regularization","created":"2025-05-26","cited_by_count":0,"area":null},{"id":"2602.03119","title":"Function-Space Empirical Bayes Regularisation with Large Vision-Language Model Priors","created":"2026-02-03","cited_by_count":0,"area":"mllm"},{"id":"2602.19964","title":"On the Equivalence of Random Network Distillation, Deep Ensembles, and Bayesian Inference","created":"2026-02-23","cited_by_count":0,"area":null},{"id":"2602.22015","title":"Function-Space Empirical Bayes Regularisation with Student's t Priors","created":"2026-02-25","cited_by_count":0,"area":null},{"id":"2605.29908","title":"Joint Model and Data Sparsification via the Marginal Likelihood","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2606.08718","title":"Deep Active Re-Labeling: Toward Noise-Resilient Annotation Efficiency","created":"2026-06-07","cited_by_count":0,"area":null},{"id":"2607.06776","title":"Efficient Bayesian Deep Ensembles via Analytic Predictive Inference","created":"2026-07-07","cited_by_count":0,"area":null}],"next_cursor":null}