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adversarial-robustness-toolbox

This is a library dedicated to adversarial machine learning. Its purpose is to allow rapid crafting and analysis of attacks and defense methods for machine learning models. The Adversarial Robustness Toolbox provides an implementation for many state-of-the-art methods for attacking and defending classifiers.

amazon_puzzles

Amazon 2013 challenges in Python

aubergedumonstre

study on a solution of Home Energy Management and Domotic

autism

Data Challenge on Autism Spectrum Disorder detection

c2lm

calculate-flops.pytorch

The calflops is designed to calculate FLOPs、MACs and Parameters in all various neural networks, such as Linear、 CNN、 RNN、 GCN、Transformer(Bert、LlaMA etc Large Language Model)

cumulo

a benchmark dataset for training and evaluating global cloud classification models.

dada

source code of AISTATS 2020 paper: Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs

daguerreotype

source code of the paper: DAG LEARNING ON THE PERMUTAHEDRON

dirichlet-margin-bound

Code source of "On Margins and Generalisation for Voting Classifiers" NeurIPS 2022 paper https://arxiv.org/abs/2206.04607

fine-classification

gittest

Testing github!

gpflow

Gaussian processes in TensorFlow

gpflowopt

Bayesian Optimization using GPflow

graal

XML processor

l3svms

new Machine Learning approach for classification, based on local SVMs

latenttrees

Code source of Learning Binary Trees by Argmin Differentiation.

multiviewlsvm

playing-with-dl

pyoptree

Python Implementation of Bertsimas's "Optimal classification trees".

stocmv

source code of NeurIPS 2021 paper: "Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound"

texts_classification

python scripts implementing different algorithms for text documents classification