Introduction of Back Propagation(BP).
My understanding is far from perfect,
how to use, what's for, what can be? doubts are remained.
However i get more interest through this approach.
I'll make effort more about this.
Introduction of Back Propagation(BP).
My understanding is far from perfect,
how to use, what's for, what can be? doubts are remained.
However i get more interest through this approach.
I'll make effort more about this.
Semi-Supervised Classification with Graph Convolutional Networks @ICLR2017読み会Eiji Sekiya
This document describes research on semi-supervised learning on graph-structured data using graph convolutional networks. It proposes a layer-wise propagation model for graph convolutions that is more efficient than previous methods. The model is tested on several datasets, achieving state-of-the-art results for semi-supervised node classification while training faster than alternative methods. Future work to address limitations regarding memory requirements, directed graphs, and locality assumptions is also discussed.
The document describes reinforcement learning algorithms. It defines equations for the policy, reward, and value functions in a reinforcement learning problem. It then derives the policy gradient theorem, which gives an expression for the gradient of expected returns with respect to the policy parameters that can be used to optimize the policy via gradient ascent. Subsequent equations adjust the policy gradient derivation for use in actor-critic methods.