double-descent

A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...

Code for Arxiv Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle

MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".

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]..

Explore the double-descent phenomena in the context of system identification. Companion code to the paper (https://arxiv.org/abs/2012.06341):

[ICIAP23] Sparse Double Descent in Vision Transformers: real or phantom threat?

This repository is the official implementation of "Optimization Variance: Delve into the Epoch-Wise Double Descent of DNNs"

Double descent experiments/repros on classical ML models and deep neural nets

[AAAI24] DSD2: Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free?

Assignments of my CST Part II Deep Neural Networks unit

This project outlines 4 experiments to explore the effects of several settings on the bias-variance tradeoff curve

A Review of Preetum Nakkiran's "More Data Can Hurt for Linear Regression: Sample-wise Double Descent"

ICLR 2022: Phenomenology of Double Descent in Finite-width Neural Networks

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.

Do sparse, one-class purchase data exhibit double descent? A study of NMF, weighted MF (iALS), and Logistic Matrix Factorization on real e-commerce transactions. Sapienza SML project.

This assignment repository demonstrates the existance of deep double descent phenomena for the deep learning models usually encorporated with a clinical context. For this, a famous ECG dataset, PTBXL has been employed.

Python empirical replication of the Double Descent risk curve in Machine Learning using from-scratch closed-form linear models.

DSC 261 Responsible Data Science Project

Toy dataset to study double descent optimization patterns in machine learning.