gdikov/MNIST-challenge

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Python
License
MIT License
Created almost 8 years ago
Updated almost 8 years ago

gdikov/MNIST-challenge

gdikov

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The MNIST dataset tackled by several machine learning models coded from scratch

hypertunity

A toolset for black-box hyperparameter optimisation.

hybrid-stereo-matching

Framework for event- and frame-based stereo matching using SNNs and MRFs

Jupyter Notebook

adversarial-variational-bayes

A Keras/TensorFlow-based implementation of Adversarial Variational Bayes by L. Mescheder et al.

bayesian-architecture-learning

Implementation of "Bayesian Learning of Neural Network Architectures"

calibrated-adversarial-learning

Reproducing the toy example in "Calibrated Adversarial Refinement for Multimodal Semantic Segmentation" by Kassapis et al.

Jupyter Notebook

protein-function-prediction

stochastic-segmentation-networks

Tutorial reimplementation of Monteiro et al. (2020) on a toy problem.

Jupyter Notebook

gdikov.github.io

MiniML

Minimalistic implementations of several ML algorithms like NNs, SOMs, vector quantization and Hopfield nets

SpikingStereoMatching

SpiNNaker implementation of a spiking stereo matching neural network