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2019_dataopen

2019 DATAOPEN held by Correlation One, sponsored by Citadel

adam

analyze optimizer properties

aipoincare

Counting the number of conservation laws from trajectory data

bimt

Brain-Inspired Modular Training (BIMT), a method for making neural networks more modular and interpretable.

cupt

expectation_maximization

expectation maximization

hydro_18_7_23

anti mapping of Hydro

hydrogan

kaggle

keras

Deep Learning for humans

kindxiaoming.github.io

website

mathematica

This repository is for notebooks of Mathematica.

ml_hiddensym

Machine Learning hidden symmetries

models

Models and examples built with TensorFlow

naive_bayes

Naive Bayes method

nnphd-github

Code for "Machine-Learning Non-Conservative Dynamics for New-Physics Detection" (arXiv: 2106.00026)

omnigrok

Omnigrok: Grokking Beyond Algorithmic Data

pykan

Kolmogorov Arnold Networks

qhmc

Quantum-inspired Hamiltonian Monte Carlo code

relu_network

resume

LIU ZIMING's resume

scaling-law-general

scaling-law-kerr

schrodinger-pca

Schrodinger Principal Component Analysis

seya

Bringing up some extra Cosmo to Keras.

sid

discovering interpretable conservation laws from differential equations

softwares

Used for storing softwares

spatial_transformer_networks

Implementation of spatial transformer networks in keras 2.0 using tensorflow 1.0 as backend.

stanford-tensorflow-tutorials

This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.

stupid-problems