The is the official implementation of ICML 2023 paper "Revisiting Weighted Aggregation in Federated Learning with Neural Networks".
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Updated
Aug 28, 2023 - Python
The is the official implementation of ICML 2023 paper "Revisiting Weighted Aggregation in Federated Learning with Neural Networks".
[NeurIPS 2023] The PyTorch Implementation of Scheduled (Stable) Weight Decay.
AdamW optimizer engine decoupling L2 weight decay regularization from adaptive gradient first and second moments.
AdamW optimizer engine decoupling L2 weight decay regularization from adaptive gradient first and second moments.
Advanced CIFAR-10 image classification using ResNet-inspired CNN with residual blocks, achieving 92%+ accuracy through comprehensive regularization, data augmentation, and professional ML engineering practices.
Investigating the Role of Weight Decay in Enhancing Nonconvex SGD, CVPR 2025
Code accompanying "Learning to Forget: Continual Learning with Adaptive Weight Decay"
Cheap online diagnostics for grokking transformers: weight-decay regimes, attention-head order parameters, data, and Lean 4 verification
Implementation of some new techniques from fastai and other papers which works with keras models
Super-Convergence on CIFAR10
Reproducibility artifacts for selection-sensitive, validation-free AdamW weight-decay control.
Folder contains implementation of Multi layer feed forward networks, Autoencoders, Sparse Autoencoders and many..
We proposed an order parameter for grokking. Then we killed it. A pre-registered falsification, with primary data, code, and a documented retraction.
Machine Learning university project
Code and figures for To Grok Grokking: Provable Grokking in Ridge Regression (ICML 2026), with ridge-regression, random-feature, NTK-style, and fully trained ReLU experiments.
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