45 citations · 87 across the 8 of their papers we have counts for
8 papers
A Memory Transformer Network for Incremental Learning
Ahmet Iscen, Thomas Bird, Mathilde Caron +2
We study class-incremental learning, a training setup in which new classes of data are observed over time for the model to learn from. Despite the straightforward problem formulati…
3D Scene Compression through Entropy Penalized Neural Representation Functions
Thomas Bird, Johannes Ballé, Saurabh Singh +1
Some forms of novel visual media enable the viewer to explore a 3D scene from arbitrary viewpoints, by interpolating between a discrete set of original views. Compared to 2D imager…
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks
Thomas Bird, Friso H. Kingma, David Barber
Deep generative models provide a powerful set of tools to understand real-world data. But as these models improve, they increase in size and complexity, so their computational cost…
HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models
James Townsend, Thomas Bird, Julius Kunze +1
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…
Variational f-divergence Minimization
Mingtian Zhang, Thomas Bird, Raza Habib +2
Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific f-divergence between the model and data distribution. In light of recent su…
Practical Lossless Compression with Latent Variables using Bits Back Coding
James Townsend, Tom Bird, David Barber
Deep latent variable models have seen recent success in many data domains. Lossless compression is an application of these models which, despite having the potential to be highly u…