A novel low-resource intrinsic metric to evaluate word
embedding quality based on graph modularity.

Building on the recent successes of distributed training of RL agents, R2D2 is an RL approach that trains a RNN-based RL agents from distributed prioritized experience replay. 
Using a single network architecture and fixed set of hyperparameters, Recurrent Replay Distributed DQN quadrupled the previous state of the art on Atari-57, and matches the state of the art on DMLab-30. 
It was the first agent to exceed human-level performance in 52 of the 57 Atari games.

R2D2

Recurrent Experience Replay in Distributed Reinforcement Learning

Categorical Modularity

Evaluating Word Embeddings with Categorical Modularity

**Skim and Intensive Reading Model**, or **SIRM**, is a deep neural network for figuring out implied textual meaning. It consists of two main components, namely the skim reading component and intensive reading component. N-gram features are quickly extracted from the skim reading component, which is a combination of several convolutional neural networks, as skim (entire) information. An intensive reading component enables a hierarchical investigation for both local (sentence) and global (paragraph) representation, which encapsulates the current embedding and the contextual information with a dense connection.

Source	Evaluating Word Embeddings with Categorical Modularity
Year	2000
Data Source	Papers with Code archive (CC BY-SA)