Code for Arxiv Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle
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Updated
Nov 24, 2023 - Python
Code for Arxiv Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle
Explore the double-descent phenomena in the context of system identification. Companion code to the paper (https://arxiv.org/abs/2012.06341):
This repository is the official implementation of "Optimization Variance: Delve into the Epoch-Wise Double Descent of DNNs"
Double Descent results for FCNNs on MNIST, extended by Label Noise (Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-Off) [Python/PyTorch]..
Implementation of the double descent Deep Learning phenomenon from the article Grokking: Generalization beyond overfitting.
Interpolating Neural Networks in Asset Pricing Data. Supports Distributed Training in TensorFlow.
DSC 261 Responsible Data Science Project
Toy dataset to study double descent optimization patterns in machine learning.
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