no code implementations • 20 Apr 2023 • Baris Kayalibay, Atanas Mirchev, Ahmed Agha, Patrick van der Smagt, Justin Bayer
Partially-observable problems pose a trade-off between reducing costs and gathering information.
no code implementations • 6 Dec 2022 • Atanas Mirchev, Baris Kayalibay, Ahmed Agha, Patrick van der Smagt, Daniel Cremers, Justin Bayer
We introduce PRISM, a method for real-time filtering in a probabilistic generative model of agent motion and visual perception.
no code implementations • 25 Jan 2022 • Baris Kayalibay, Atanas Mirchev, Patrick van der Smagt, Justin Bayer
We introduce a method for real-time navigation and tracking with differentiably rendered world models.
no code implementations • ICLR Workshop SSL-RL 2021 • Baris Kayalibay, Atanas Mirchev, Patrick van der Smagt, Justin Bayer
We examine the effect of the conditioning gap on model-based reinforcement learning with variational world models.
Model-based Reinforcement Learning reinforcement-learning +2
no code implementations • ICLR 2021 • Justin Bayer, Maximilian Soelch, Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt
Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO).
no code implementations • ICLR 2021 • Atanas Mirchev, Baris Kayalibay, Patrick van der Smagt, Justin Bayer
We solve the problem of 6-DoF localisation and 3D dense reconstruction in spatial environments as approximate Bayesian inference in a deep state-space model.
no code implementations • 18 May 2018 • Atanas Mirchev, Baris Kayalibay, Maximilian Soelch, Patrick van der Smagt, Justin Bayer
Model-based approaches bear great promise for decision making of agents interacting with the physical world.
no code implementations • 26 Jan 2018 • Atanas Mirchev, Seyed-Ahmad Ahmadi
In recent years, Generative Adversarial Networks (GAN) have emerged as a powerful method for learning the mapping from noisy latent spaces to realistic data samples in high-dimensional space.
no code implementations • 13 Apr 2017 • Vladimir Golkov, Marcin J. Skwark, Atanas Mirchev, Georgi Dikov, Alexander R. Geanes, Jeffrey Mendenhall, Jens Meiler, Daniel Cremers
In this paper, we show that deep learning can predict biological function of molecules directly from their raw 3D approximated electron density and electrostatic potential fields.