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Working at the intersection of Machine Learning, Computational Neuroscience and Cognitive Science.
continual-learning. PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.
1.9kbrain-inspired-replay. A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).
252class-incremental-learning. PyTorch implementation of a VAE-based generative classifier, as well as other class-incremental learning methods that do not store data (DGR, BI-R, EWC, SI, CWR, CWR+, AR1, the "labels trick", SLDA).
81progressive-learning-pytorch. Python
6complex-synapses. Deep learning implementation of the Benna-Fusi complex synapse model.
6pytorch-deep-generative-replay. PyTorch implementation of "Continual Learning with Deep Generative Replay", NIPS 2017
5pytorch-ewc. PyTorch implementation of DeepMind's PNAS 2017 paper "Overcoming Catastrophic Forgetting"
3docker. Docker Files
1ContinualEvaluation. Continual evaluation for lifelong learning with neural networks.
1incremental_cifar100. Implementation of BI-R in T&E framework.
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