stochastic weight averaging

Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging ICML 2025 Generalization Analysis of Stochastic Weight Averaging with General Sampling ICML 2024 Post-Hoc Reversal: Are We Selecting Models Prematurely? NeurIPS 2024 Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations NeurIPS 2024 Towards Scalable Bayesian Transformers: Investigating stochastic subset selection for NLP UAI 2024 Decoupled Training for Long-Tailed Classification With Stochastic Representations ICLR 2023 Deep Combinatorial Aggregation NeurIPS 2022 Improving Generalization of Pre-trained Language Models via Stochastic Weight Averaging EMNLP 2022 SpectraNet: Learned Recognition of Artificial Satellites From High Contrast Spectroscopic Imagery WACV 2022 When Do Flat Minima Optimizers Work? NeurIPS 2022 How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task EMNLP 2021 Language Recognition on Unknown Conditions: The LORIA-Inria-MULTISPEECH System for AP20-OLR Challenge Interspeech 2021 Learning Neural Network Subspaces ICML 2021 Robust Overfitting may be mitigated by properly learned smoothening ICLR 2021 SWAD: Domain Generalization by Seeking Flat Minima NeurIPS 2021 Stochastic Weight Averaging in Parallel: Large-Batch Training That Generalizes Well ICLR 2020 A Simple Baseline for Bayesian Uncertainty in Deep Learning NeurIPS 2019 On Model Stability as a Function of Random Seed CoNLL 2019 SWALP : Stochastic Weight Averaging in Low Precision Training ICML 2019 There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average ICLR 2019