This repo covers the implementation for CMC (as well as Momentum Contrast and Instance Discrimination), which learns representations from multiview data in a self-supervised way (by multiview, we mean multiple sensory, multiple modal data, or literally multiple viewpoint data.

Estimating individual treatment effect: generalization bounds and algorithms.

This repository provides experiments, scripts, and instructions for reproducing the experiments in our paper, Low-Latency Graph Streaming Using Compressed Purely-Functional Trees. Our paper introduces Aspen, a graph-streaming system based on compressed purely-functional trees. Aspen is designed for maintaining a dynamic graph subject to updates by a single writer, while supporting multiple concurrent readers. Due to the fact that the graph is purely-functional, all operations in Aspen are strictly serializable.