ae-foster

ae-foster.github.io

cresp

Code for 'On Contrastive Representations of Stochastic Processes' https://arxiv.org/abs/2106.10052

dad

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design

invclr

Improving Transformation Invariance in Contrastive Representation Learning

pyro

Deep universal probabilistic programming with Python and PyTorch

pytorch-simclr

A PyTorch reproduction of 'A Simple Framework for Contrastive Learning of Visual Representations' by Ting Chen, et al.

rdbgenerate

Utility for generating Redis dump (.rdb) files from Python native objects

deepqmc

Deep learning quantum Monte Carlo for electrons in real space

csuite

CSuite: A Suite of Benchmark Datasets for Causality

pyro

Deep universal probabilistic programming with Python and PyTorch

ae-foster/pytorch-simclr

A PyTorch reproduction of 'A Simple Framework for Contrastive Learning of Visual Representations' by Ting Chen, et al.

ae-foster/dad

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design

ae-foster/invclr

Improving Transformation Invariance in Contrastive Representation Learning

ae-foster/cresp

Code for 'On Contrastive Representations of Stochastic Processes' https://arxiv.org/abs/2106.10052

ae-foster/pyro

Deep universal probabilistic programming with Python and PyTorch

ae-foster/ae-foster.github.io

ae-foster/rdbgenerate

Utility for generating Redis dump (.rdb) files from Python native objects

ae-foster/bntl_presentation

ae-foster/easteregg

Follow a trail of picture postcard clues around the world

ae-foster/hutgroup

The Hut Group Challenge Repo

ae-foster/badapted

Bayesian ADAPTive Experimental Design

ae-foster/BetaNeutralToTheLeft.jl

Code to accompany UAI paper 'Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks'

ae-foster/bntl_poster

ae-foster/deepqmc

Deep learning quantum Monte Carlo for electrons in real space

ae-foster/dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

ae-foster/gensim

Topic Modeling with multiple corpora - an extension of gensim to support mLDA

ae-foster/moco

PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722

ae-foster/pysvihmm

Implementation of stochastic variational inference for Bayesian hidden Markov models.

ae-foster/RL-BOED

ae-foster/SarcasmBot

Bot for detecting sarcasm

ae-foster/sgboed-gp

Standalone repo for the blog posts about combining GPs and SGBOED

ae-foster/simclr

SimCLR - A Simple Framework for Contrastive Learning of Visual Representations

ae-foster/sploodl

Sploodl | Split bills

ae-foster/thesis_main

ae-foster/transfer

ae-foster/vboed_poster

Poster for 'Variational Bayesian Optimal Experimental Design'