no code implementations • 13 Sep 2018 • Thomas Bird, Julius Kunze, David Barber
These approaches are of particular interest because they are parallelizable.
no code implementations • 27 Jul 2019 • Mingtian Zhang, Thomas Bird, Raza Habib, Tianlin Xu, David Barber
Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific f-divergence between the model and data distribution.
no code implementations • ICLR 2021 • Thomas Bird, Friso H. Kingma, David Barber
In this work we show, for the first time, that we can successfully train generative models which utilize binary neural networks.
no code implementations • 26 Apr 2021 • Thomas Bird, Johannes Ballé, Saurabh Singh, Philip A. Chou
We unify these steps by directly compressing an implicit representation of the scene, a function that maps spatial coordinates to a radiance vector field, which can then be queried to render arbitrary viewpoints.
no code implementations • 27 Sep 2018 • Mingtian Zhang, Thomas Bird, Raza Habib, Tianlin Xu, David Barber
Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific form of f-divergence between the model and data distribution.
no code implementations • 10 Oct 2022 • Ahmet Iscen, Thomas Bird, Mathilde Caron, Alireza Fathi, Cordelia Schmid
We study class-incremental learning, a training setup in which new classes of data are observed over time for the model to learn from.
1 code implementation • ICLR 2020 • James Townsend, Thomas Bird, Julius Kunze, David Barber
We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model.