Non-parametric approximation of Q-values by storing all visited states and doing inference through k-Nearest Neighbors.

OSCAR is a new learning method that uses object tags detected in images as anchor points to ease the learning of image-text alignment. The model take a triple as input (word-tag-region) and pre-trained with two losses (masked token loss over words and tags, and a contrastive loss between tags and others). OSCAR represents an image-text pair into semantic space via dictionary lookup. Object tags are used as anchor points to align image regions with word embeddings of pre-trained language models. The model is then fine-tuned for understanding and generation tasks.

OSCAR

Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

MFEC

Model-Free Episodic Control

**GRLIA** is an incident aggregation framework for online service systems based on graph representation learning over the cascading graph of cloud failures. A representation vector is learned for each unique type of incident in an unsupervised and unified manner, which is able to simultaneously encode the topological and temporal correlations among incidents.

Source	Model-Free Episodic Control
Year	2000
Data Source	Papers with Code archive (CC BY-SA)