5 citations · 13 across the 13 of their papers we have counts for
3 papers · 1 filter
GFlowOut: Dropout with Generative Flow Networks
Dianbo Liu, Moksh Jain, Bonaventure Dossou +10
Bayesian Inference offers principled tools to tackle many critical problems with modern neural networks such as poor calibration and generalization, and data inefficiency. However,…
FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data
Mike He Zhu, Léna Néhale Ezzine, Dianbo Liu +1
Federated learning is a distributed machine learning approach which enables a shared server model to learn by aggregating the locally-computed parameter updates with the training d…
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization
Dianbo Liu, Alex Lamb, Xu Ji +4
Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit. It has been theoretically and empirically sho…