Pinned Repositories
hypercl
Continual Learning with Hypernetworks. A continual learning approach that has the flexibility to learn a dedicated set of parameters, fine-tuned for every task, that doesn't require an increase in the number of trainable weights and is robust against catastrophic forgetting.
awesome-hypernetworks
bayes-by-backprop
PyTorch implementation of "Weight Uncertainty in Neural Networks"
Bayesian-FlowNet
Bayesian FlowNetS in Tensorflow
flow-code-python
Python I/O for optical flow files (.flo)
La-MAML
Official Code Repository for La-MAML: Look-Ahead Meta-Learning for Continual Learning"
learning_where_to_learn
torchdiffeq
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
transformers-learn-in-context-by-gradient-descent
late-phase-weights
Johswald's Repositories
Johswald/flow-code-python
Python I/O for optical flow files (.flo)
Johswald/awesome-hypernetworks
Johswald/learning_where_to_learn
Johswald/Bayesian-FlowNet
Bayesian FlowNetS in Tensorflow
Johswald/La-MAML
Official Code Repository for La-MAML: Look-Ahead Meta-Learning for Continual Learning"
Johswald/torchdiffeq
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
Johswald/transformers-learn-in-context-by-gradient-descent
Johswald/bayes-by-backprop
PyTorch implementation of "Weight Uncertainty in Neural Networks"
Johswald/jaynes-blog
A blog for the reading club on Jayne's Probability Theory
Johswald/models
Models built with TensorFlow