Automatic Evals for LLMs

Synthetic Data curation for post-training and structured data extraction

Duke Machine Learning Virtual Summer School 2021

Proceedings of AISTATS 2021

Code for AAAI 2019 paper: Graph Representation Learning via Ladder Gamma Variational Autoencoders

A simple probabilistic programming language.

Code for ICML2020 paper - CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information

NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.

An unofficial userspace driver for HID++ Logitech devices

Repository for course materials for ECE 590 Scalable Reinforcement Learning

Indian Buffet Process; Variational Inference

Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

Code for "How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks"

Implementation of VLAE

TikZ library for drawing Bayesian networks, graphical models and (directed) factor graphs in LaTeX.

NumPy implementation of infinite latent feature model (aka Indian Buffet Process or IBP)

Representation learning on large graphs using stochastic graph convolutions.

mean-field and structured VAEs for the IBP

Implementation of Graph Auto-Encoders in TensorFlow

Gaussian processes in TensorFlow

starter from "How to Train a GAN?" at NIPS2016

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.