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