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.
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.
1 code implementation • ACL 2021 • Ahmad Rashid, Vasileios Lioutas, Mehdi Rezagholizadeh
We present, MATE-KD, a novel text-based adversarial training algorithm which improves the performance of knowledge distillation.
no code implementations • 22 Apr 2021 • Adam Scibior, Vasileios Lioutas, Daniele Reda, Peyman Bateni, Frank Wood
We develop a deep generative model built on a fully differentiable simulator for multi-agent trajectory prediction.
no code implementations • EMNLP 2021 • Ahmad Rashid, Vasileios Lioutas, Abbas Ghaddar, Mehdi Rezagholizadeh
Knowledge Distillation (KD) is a common knowledge transfer algorithm used for model compression across a variety of deep learning based natural language processing (NLP) solutions.
no code implementations • 21 Jun 2020 • Vasileios Lioutas
Several methods have recently been proposed for the Single Image Super-Resolution (SISR) problem.
1 code implementation • ICML 2020 • Vasileios Lioutas, Yuhong Guo
Some of these models use all the available sequence tokens to generate an attention distribution which results in time complexity of $O(n^2)$.
Ranked #12 on Machine Translation on WMT2014 English-French
no code implementations • Findings of the Association for Computational Linguistics 2020 • Vasileios Lioutas, Ahmad Rashid, Krtin Kumar, Md. Akmal Haidar, Mehdi Rezagholizadeh
Word-embeddings are vital components of Natural Language Processing (NLP) models and have been extensively explored.
no code implementations • 25 Sep 2019 • Vasileios Lioutas, Ahmad Rashid, Krtin Kumar, Md Akmal Haidar, Mehdi Rezagholizadeh
Word-embeddings are a vital component of Natural Language Processing (NLP) systems and have been extensively researched.
no code implementations • 23 May 2019 • Vasileios Lioutas, Andriy Drozdyuk
In this paper we provide the mathematical definition of attention and examine its application to sequence to sequence models.