Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
calibrated_uncertainty		calibrated_uncertainty
clustering_HMMs		clustering_HMMs
forcasting_time_series		forcasting_time_series
implicit_models		implicit_models
info_vae		info_vae
multimodal_VAE		multimodal_VAE
normalizing_flow		normalizing_flow
practical_bounds		practical_bounds
prior_networks		prior_networks
r_hat		r_hat
stein_variational_gradient_descent		stein_variational_gradient_descent
stochastic_gradient_hmc		stochastic_gradient_hmc
style_transfer		style_transfer
subspace_inference		subspace_inference
subspaces_in_VAE		subspaces_in_VAE
what_uncertainties		what_uncertainties
wormhole_hmc		wormhole_hmc
README.md		README.md

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AM207 Fall 2019 Course Projects

Accurate Uncertainties for Deap Learning Using Calibrated Regression

Team 1: Anthony Rentsch, Abhimanyu Vasishth

Team 2: Dmitry Vukolov, Benjamin Yuen, Piotr Pekala, Alp Kutlualp

Predictive Uncertainty Estimation via Prior Networks

Team 1: Simon Batzner, Theo Guenais, Rylan Schaeffer, Dimitris Vamvourellis

Team 2: Tianhao Wang, Zhao Lyu, Zhenru Wang

Subspace Inference for Bayesian Deep Learning

Team 1: Nicholas Beasley, Ralph Aurel Tigoumo Ngoudjou, Andrew Fu, Nam Luu Nhat

Team 2: Hari Kothapalli, Roshan Padaki

Team 3: Yuting Kou, Yiming Xu, Yizhou Wang, Ziyi Zhou

Team 4: Phoebe Wong, Nicholas Stern, Claire Stolz

Multimodal Generative Models for Scalable Weakly-Supervised Learning

Team 1: Michael Zhang, Rajath Salegame, Jonathan Chu

Learning Latent Subspaces in Variational Autoencoders

Team 1: Pat Sukhum, Rachel Moon, Nathan Einstein, Catherine Ding

Team 2: Karina Huang, Lipika Ramaswamy, Erin Williams

Stein Variational Gradient Descent

Team 1: Michael Downs, Andrew Chia

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Team 1: Qing Zhang, Xiaoxuan Liu, Zeyuan Hu

Team 2: Eric Sun, Shangda Xu

Team 3: Ria Cheruvu, Akshat Sinha, Haitao Shang, Michel Atoudem Kana

Stochastic Gradient Hamiltonian Monte Carlo

Team 1: Alex Chin, Jason Huang, Taras Holovko, Tyler Yan

Team 2: William Palmer, Paul-Emile Landrin

Team 3: Kezi Cheng, Michael Lee, Daniel Olal, Victor Sheng

Rank-normalization, folding, and localization: An improved R for assessing convergence of MCMC

Team 1: Tanveer Karim, Ian Weaver

Variational Inference with Normalizing Flows

Team 1: Julien Laasri, Abhimanyu Talwar, Feng (Nick) Qian

Team 2: Brian Chu, Jovin Leong, Cooper Lorsung

Team 3: Benjamin Levy, Sujay Thakur

Energy optimization in image style transfer via texture synthesis

Team 1: Lin Zhu, Alice (Anqi) Li

Hierarchical Implicit Models and Likelihood-Free Variational Inference

Team 1: Adam Nitido, Yiming Qin

Practical Posterior Error Bounds from Variational Objectives

Team 1: Michael Jetsupphasuk, Thabo Samakhoana, Qiuyang Yin, Chuqiao Yuan

The Variational Hierarchical EM Algorithm for Clustering Hidden Markov Models

Team 1: Benton Liang, Maddy Nakada, Hurlink Vongsachang, Michael Yue

Forecasting "High" and "Low" of financial time series by Particle systems and Kalman filters

Team 1: Chih-Kang Chang, Yuying Qian, Jose Antonio Alatorre Sanchez

Wormhole Hamiltonian Monte Carlo

Team 1: Alexander Wong, Smarak Maity, Sachin Mathur

Infovae: Balancing learning and inference in variational autoencoders

Team 1: Daniel J. Drennan

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