**OPT-IML** is a version of OPT fine-tuned on a large collection of 1500+ NLP tasks divided into various task categories.

**UCNet** is a probabilistic framework for RGB-D Saliency Detection that employs uncertainty by learning from the data labelling process. It utilizes conditional variational autoencoders to model human annotation uncertainty and generate multiple saliency maps for each input image by sampling in the latent space.

UCNet

UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders

OPT-IML

OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization

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.

Source	OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com