Deep Learning
Learning and Processing the Ordinal Information of Temporal Sequences in Recurrent Neural Circuits
Temporal sequence processing is fundamental in brain cognitive functions.
Probabilistic Inference in Reinforcement Learning Done Right Jean T arbouriech Google DeepMind
A popular perspective in Reinforcement learning (RL) casts the problem as probabilistic inference on a graphical model of the Markov decision process (MDP). The core object of study is the probability of each state-action pair being visited under the optimal policy.
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
However, there is currently no framework to apply it to generic architectures, specifically ones with linear weight-sharing layers.
EgoDistill: Egocentric Head Motion Distillation for Efficient Video Understanding Shuhan T an
Recent advances in egocentric video understanding models are promising, but their heavy computational expense is a barrier for many real-world applications.
