no code implementations • 30 Apr 2024 • Dylan Green, William Harvey, Saeid Naderiparizi, Matthew Niedoba, Yunpeng Liu, Xiaoxuan Liang, Jonathan Lavington, Ke Zhang, Vasileios Lioutas, Setareh Dabiri, Adam Scibior, Berend Zwartsenberg, Frank Wood
Current state-of-the-art methods for video inpainting typically rely on optical flow or attention-based approaches to inpaint masked regions by propagating visual information across frames.
1 code implementation • 28 Mar 2023 • William Harvey, Frank Wood
Recent progress with conditional image diffusion models has been stunning, and this holds true whether we are speaking about models conditioned on a text description, a scene layout, or a sketch.
1 code implementation • 20 Oct 2022 • Christian Weilbach, William Harvey, Frank Wood
We introduce a framework for automatically defining and learning deep generative models with problem-specific structure.
1 code implementation • 23 May 2022 • William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach, Frank Wood
We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in a variety of realistic environments.
1 code implementation • ICLR 2022 • William Harvey, Saeid Naderiparizi, Frank Wood
We present a conditional variational auto-encoder (VAE) which, to avoid the substantial cost of training from scratch, uses an architecture and training objective capable of leveraging a foundation model in the form of a pretrained unconditional VAE.
no code implementations • 1 Jan 2021 • William Harvey, Michael Teng, Frank Wood
We introduce methodology from the BOED literature to approximate this optimal behaviour, and use it to generate `near-optimal' sequences of attention locations.
no code implementations • 3 Oct 2020 • Andreas Munk, William Harvey, Frank Wood
Some of the most popular methods for improving the stability and performance of GANs involve constraining or regularizing the discriminator.
1 code implementation • 30 Mar 2020 • Frank Wood, Andrew Warrington, Saeid Naderiparizi, Christian Weilbach, Vaden Masrani, William Harvey, Adam Scibior, Boyan Beronov, John Grefenstette, Duncan Campbell, Ali Nasseri
In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models.
no code implementations • 25 Oct 2019 • William Harvey, Andreas Munk, Atılım Güneş Baydin, Alexander Bergholm, Frank Wood
We present a new approach to automatic amortized inference in universal probabilistic programs which improves performance compared to current methods.
no code implementations • pproximateinference AABI Symposium 2019 • Christian Weilbach, Boyan Beronov, William Harvey, Frank Wood
We introduce a more efficient neural architecture for amortized inference, which combines continuous and conditional normalizing flows using a principled choice of structure.
no code implementations • 13 Jun 2019 • William Harvey, Michael Teng, Frank Wood
We introduce methodology from the BOED literature to approximate this optimal behaviour, and use it to generate `near-optimal' sequences of attention locations.