3 papers
cs.LG2020
Optimizing the Neural Architecture of Reinforcement Learning Agents
N. Mazyavkina, S. Moustafa, I. Trofimov +1
Reinforcement learning (RL) enjoyed significant progress over the last years. One of the most important steps forward was the wide application of neural networks. However, architec…
cs.CV2018
Perceptual deep depth super-resolution
Oleg Voynov, Alexey Artemov, Vage Egiazarian +4
RGBD images, combining high-resolution color and lower-resolution depth from various types of depth sensors, are increasingly common. One can significantly improve the resolution o…
cs.CV2018
Latent Convolutional Models
ShahRukh Athar, Evgeny Burnaev, Victor Lempitsky
We present a new latent model of natural images that can be learned on large-scale datasets. The learning process provides a latent embedding for every image in the training datase…