Self Normalizing Flows

1 code implementation • 14 Nov 2020 • T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling

Efficient gradient computation of the Jacobian determinant term is a core problem in many machine learning settings, and especially so in the normalizing flow framework.

Integer Discrete Flows and Lossless Compression

1 code implementation • NeurIPS 2019 • Emiel Hoogeboom, Jorn W. T. Peters, Rianne van den Berg, Max Welling

For that reason, we introduce a flow-based generative model for ordinal discrete data called Integer Discrete Flow (IDF): a bijective integer map that can learn rich transformations on high-dimensional data.

Probabilistic Binary Neural Networks

1 code implementation • ICLR 2019 • Jorn W. T. Peters, Max Welling

Low bit-width weights and activations are an effective way of combating the increasing need for both memory and compute power of Deep Neural Networks.

HexaConv

1 code implementation • ICLR 2018 • Emiel Hoogeboom, Jorn W. T. Peters, Taco S. Cohen, Max Welling

We find that, due to the reduced anisotropy of hexagonal filters, planar HexaConv provides better accuracy than planar convolution with square filters, given a fixed parameter budget.

Aerial Scene Classification Scene Classification