no code implementations • 7 May 2024 • Jonathan Wilder Lavington, Ke Zhang, Vasileios Lioutas, Matthew Niedoba, Yunpeng Liu, Dylan Green, Saeid Naderiparizi, Xiaoxuan Liang, Setareh Dabiri, Adam Ścibior, Berend Zwartsenberg, Frank Wood
Moreover, because of the high variability between different problems presented in different autonomous systems, these simulators need to be easy to use, and easy to modify.
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.
no code implementations • 12 Feb 2024 • Matthew Niedoba, Dylan Green, Saeid Naderiparizi, Vasileios Lioutas, Jonathan Wilder Lavington, Xiaoxuan Liang, Yunpeng Liu, Ke Zhang, Setareh Dabiri, Adam Ścibior, Berend Zwartsenberg, Frank Wood
Score function estimation is the cornerstone of both training and sampling from diffusion generative models.
no code implementations • 31 Jul 2023 • Saeid Naderiparizi, Xiaoxuan Liang, Berend Zwartsenberg, Frank Wood
The maximum likelihood principle advocates parameter estimation via optimization of the data likelihood function.
1 code implementation • 24 May 2023 • Setareh Dabiri, Vasileios Lioutas, Berend Zwartsenberg, Yunpeng Liu, Matthew Niedoba, Xiaoxuan Liang, Dylan Green, Justice Sefas, Jonathan Wilder Lavington, Frank Wood, Adam Scibior
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to the distribution of real data.
no code implementations • 19 May 2023 • Yunpeng Liu, Vasileios Lioutas, Jonathan Wilder Lavington, Matthew Niedoba, Justice Sefas, Setareh Dabiri, Dylan Green, Xiaoxuan Liang, Berend Zwartsenberg, Adam Ścibior, Frank Wood
The development of algorithms that learn multi-agent behavioral models using human demonstrations has led to increasingly realistic simulations in the field of autonomous driving.
no code implementations • 17 Jun 2022 • Berend Zwartsenberg, Adam Ścibior, Matthew Niedoba, Vasileios Lioutas, Yunpeng Liu, Justice Sefas, Setareh Dabiri, Jonathan Wilder Lavington, Trevor Campbell, Frank Wood
We present a novel, conditional generative probabilistic model of set-valued data with a tractable log density.
no code implementations • 30 May 2022 • Vasileios Lioutas, Jonathan Wilder Lavington, Justice Sefas, Matthew Niedoba, Yunpeng Liu, Berend Zwartsenberg, Setareh Dabiri, Frank Wood, Adam Scibior
We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors.
no code implementations • 25 Oct 2019 • Andreas Munk, Berend Zwartsenberg, Adam Ścibior, Atılım Güneş Baydin, Andrew Stewart, Goran Fernlund, Anoush Poursartip, Frank Wood
Our surrogates target stochastic simulators where the number of random variables itself can be stochastic and potentially unbounded.