Deep Reinforcement Learning and its Neuroscientific Implications In this paper, the authors provided a high-level introduction to deep RL, discussed some of its initial applications to neuroscience, and surveyed its wider implications for research on brain and behaviour and concluded with a list of opportunities for next-stage research. Although DeepRL seems to be promising, the authors wrote that it is still a work in progress and its implications in neuroscience should be looked at as a great opportunity. For instance, deep RL provides an agent-based framework for studying the way that reward shapes representation, and how representation, in turn, shapes learning and decision making — two issues which together span a large swath of what is most central to neuroscience.  Check the paper here.

Unsupervised Learning of Probably Symmetric Deformable 3D Objects Winner of the CVPR best paper award, in this work, the authors proposed a method to learn 3D deformable object categories from raw single-view images, without external supervision. This method uses an autoencoder that factored each input image into depth, albedo, viewpoint and illumination. The authors showcased that reasoning about illumination can be used to exploit the underlying object symmetry even if the appearance is not symmetric due to shading.

what librarians are to libraries, moderators and editors are to the realm of public ideas and discourse: balancers of freedom, inclusion, and safety.

most