{"total":15,"rows":[{"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":"2308.07870","title":"Brain-inspired Computational Intelligence via Predictive Coding","created":"2023-08-15","cited_by_count":30,"area":null},{"id":"2211.14133","title":"PipeFisher: Efficient Training of Large Language Models Using Pipelining and Fisher Information Matrices","created":"2022-11-25","cited_by_count":24,"area":null},{"id":"2305.04684","title":"ASDL: A Unified Interface for Gradient Preconditioning in PyTorch","created":"2023-05-08","cited_by_count":21,"area":null},{"id":"2303.05473","title":"Natural Gradient Methods: Perspectives, Efficient-Scalable Approximations, and Analysis","created":"2023-03-06","cited_by_count":8,"area":null},{"id":"2206.15143","title":"Scalable K-FAC Training for Deep Neural Networks with Distributed Preconditioning","created":"2022-06-30","cited_by_count":7,"area":null},{"id":"2107.06533","title":"Accelerating Distributed K-FAC with Smart Parallelism of Computing and Communication Tasks","created":"2021-07-14","cited_by_count":5,"area":null},{"id":"2508.13898","title":"Beyond the Mean: Fisher-Orthogonal Projection for Natural Gradient Descent in Large Batch Training","created":"2025-08-19","cited_by_count":5,"area":null},{"id":"2206.15397","title":"Randomized K-FACs: Speeding up K-FAC with Randomized Numerical Linear Algebra","created":"2022-06-30","cited_by_count":3,"area":null},{"id":"2210.08494","title":"Brand New K-FACs: Speeding up K-FAC with Online Decomposition Updates","created":"2022-10-16","cited_by_count":2,"area":null},{"id":"2310.04649","title":"Uncovering Model Processing Strategies with Non-Negative Per-Example Fisher Factorization","created":"2023-10-07","cited_by_count":2,"area":null},{"id":"2204.04718","title":"Rethinking Exponential Averaging of the Fisher","created":"2022-04-10","cited_by_count":1,"area":null},{"id":"2308.02123","title":"Eva: A General Vectorized Approximation Framework for Second-order Optimization","created":"2023-08-04","cited_by_count":1,"area":null},{"id":"2310.12298","title":"Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization","created":"2023-10-18","cited_by_count":0,"area":null},{"id":"2603.25976","title":"Second-Order, First-Class: A Composable Stack for Curvature-Aware Training","created":"2026-03-26","cited_by_count":0,"area":null}],"next_cursor":null}