Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Lioutas, Vasileios"'
Autor:
Lavington, Jonathan Wilder, Zhang, Ke, Lioutas, Vasileios, Niedoba, Matthew, Liu, Yunpeng, Green, Dylan, Naderiparizi, Saeid, Liang, Xiaoxuan, Dabiri, Setareh, Ścibior, Adam, Zwartsenberg, Berend, Wood, Frank
The training, testing, and deployment, of autonomous vehicles requires realistic and efficient simulators. Moreover, because of the high variability between different problems presented in different autonomous systems, these simulators need to be eas
Externí odkaz:
http://arxiv.org/abs/2405.04491
Autor:
Green, Dylan, Harvey, William, Naderiparizi, Saeid, Niedoba, Matthew, Liu, Yunpeng, Liang, Xiaoxuan, Lavington, Jonathan, Zhang, Ke, Lioutas, Vasileios, Dabiri, Setareh, Scibior, Adam, Zwartsenberg, Berend, Wood, Frank
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. While such approaches have led to significant progress on st
Externí odkaz:
http://arxiv.org/abs/2405.00251
Autor:
Niedoba, Matthew, Green, Dylan, Naderiparizi, Saeid, Lioutas, Vasileios, Lavington, Jonathan Wilder, Liang, Xiaoxuan, Liu, Yunpeng, Zhang, Ke, Dabiri, Setareh, Ścibior, Adam, Zwartsenberg, Berend, Wood, Frank
Score function estimation is the cornerstone of both training and sampling from diffusion generative models. Despite this fact, the most commonly used estimators are either biased neural network approximations or high variance Monte Carlo estimators
Externí odkaz:
http://arxiv.org/abs/2402.08018
Autor:
Niedoba, Matthew, Lavington, Jonathan Wilder, Liu, Yunpeng, Lioutas, Vasileios, Sefas, Justice, Liang, Xiaoxuan, Green, Dylan, Dabiri, Setareh, Zwartsenberg, Berend, Scibior, Adam, Wood, Frank
Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of prerecorded real-world traffic scenarios in simulation ensures realism but the rarity of safety critical events
Externí odkaz:
http://arxiv.org/abs/2309.12508
Autor:
Zwartsenberg, Berend, Ścibior, Adam, Niedoba, Matthew, Lioutas, Vasileios, Liu, Yunpeng, Sefas, Justice, Dabiri, Setareh, Lavington, Jonathan Wilder, Campbell, Trevor, Wood, Frank
We present a novel, conditional generative probabilistic model of set-valued data with a tractable log density. This model is a continuous normalizing flow governed by permutation equivariant dynamics. These dynamics are driven by a learnable per-set
Externí odkaz:
http://arxiv.org/abs/2206.09021
Autor:
Lioutas, Vasileios, Lavington, Jonathan Wilder, Sefas, Justice, Niedoba, Matthew, Liu, Yunpeng, Zwartsenberg, Berend, Dabiri, Setareh, Wood, Frank, Scibior, Adam
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. These heuristic factors, obtained from parametric approximations of the marginal like
Externí odkaz:
http://arxiv.org/abs/2205.15460
The advent of large pre-trained language models has given rise to rapid progress in the field of Natural Language Processing (NLP). While the performance of these models on standard benchmarks has scaled with size, compression techniques such as know
Externí odkaz:
http://arxiv.org/abs/2105.05912
We develop a deep generative model built on a fully differentiable simulator for multi-agent trajectory prediction. Agents are modeled with conditional recurrent variational neural networks (CVRNNs), which take as input an ego-centric birdview image
Externí odkaz:
http://arxiv.org/abs/2104.11212
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. In its regular manifestations, KD requires access to the teacher's tr
Externí odkaz:
http://arxiv.org/abs/2012.15495
Autor:
Lioutas, Vasileios
Several methods have recently been proposed for the Single Image Super-Resolution (SISR) problem. The current methods assume that a single low-resolution image can only yield a single high-resolution image. In addition, all of these methods use low-r
Externí odkaz:
http://arxiv.org/abs/2006.11708