jmamath

active-learning

Code base play with various active learning acquisition function

analytics-zoo

Distributed Tensorflow, Keras and BigDL on Apache Spark

augmix

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Bitcoin-investigation

Bottom-Sea-Detection

Deep Learning Approach to Classify Seafloor Type from Sonar Data

Deep-learning-MNIST-classifier

Master-thesis

Here is my master thesis in French on the topic :

ood-deep-learning

Repository for the project proposal "A Thorough Analysis of Deep Neural Networks under Distribution Shift"

Seafloor-Classification

Deep Learning Approach to Classify Seafloor Type from Sonar Data

wilds

A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.

jmamath/active-learning

Code base play with various active learning acquisition function

jmamath/Bottom-Sea-Detection

Deep Learning Approach to Classify Seafloor Type from Sonar Data

jmamath/ood-deep-learning

Repository for the project proposal "A Thorough Analysis of Deep Neural Networks under Distribution Shift"

jmamath/Master-thesis

Here is my master thesis in French on the topic :

jmamath/Deep-learning-MNIST-classifier

jmamath/Seafloor-Classification

Deep Learning Approach to Classify Seafloor Type from Sonar Data

jmamath/wilds

A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.

jmamath/analytics-zoo

Distributed Tensorflow, Keras and BigDL on Apache Spark

jmamath/augmix

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

jmamath/Bitcoin-investigation

jmamath/Demo-Djehuty

jmamath/DFL-Contributions

jmamath/DT1822

jmamath/Heat-diffusion-fins-radiator

jmamath/Indaba_Lab

Indaba X Sénégal workshop

jmamath/ISDA-for-Deep-Networks

An efficient implicit semantic augmentation method, complementary to existing non-semantic techniques.

jmamath/learning-approximate-invariance-requires-far-fewer-data

jmamath/MOOCS-Certifications

jmamath/Offre-stage

jmamath/OOD-Generalization

jmamath/ood_uncertainty_prediction

jmamath/otdd

Optimal Transport Dataset Distance

jmamath/prototypical-networks-tensorflow

Tensorflow implementation of NIPS 2017 Paper "Prototypical Networks for Few-shot Learning"

jmamath/Ressources

jmamath/resume

jmamath/UVS-Probabilite-Statistiques

Ressources pour le tronc commun probabilité et statistiques des master 1 Big Data et Intelligence Artificielle

jmamath/UVS_Proba_Stats

jmamath/Variational_Autoencoder

This is the final project of the MOOC Bayesian Methods in ML