Juhan Bae
Introduction
I am a PhD student in the Machine Learning Group at the University of Toronto and the Vector Institute, supervised by Roger Grosse. I received my HBSc in computer science and statistics from the same university in 2019. I previously interned at Microsoft Research.
I'm interested in understanding wide range of applications in deep learning. I'm currently working on hyperparameter optimization, optimization, and interpretability of deep learning.
Publications
[C10] Efficient Parametric Approximations of Neural Network Function Space Distance
ICML 2023
Paper Code
[C9] Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve
ICLR 2023 (Oral Presentation)
Paper Code
[C8] If Influence Functions are the Answer, Then What is the Question?
NeurIPS 2022
Paper Code 1 Code 2
[C7] Amortized Proximal Optimization
NeurIPS 2022
Paper Code
[C6] Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
ICML 2021
Paper Code
[C5] Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians
NeurIPS 2020
Paper Code
[W4] On Monotonic Linear Interpolation of Neural Network Parameters
NeurIPS 2020, Optimization for Machine Learning Workshop
Paper Code
[W3] Eigenvalue Corrected Noisy Natural Gradient
NeurIPS 2019, Bayesian Deep Learning Workshop
Paper Code
[C2] Fast 6DOF Pose Estimation with Synthetic Textureless CAD Model for Mobile Applications
ICIP 2019
Paper
[W1] Learnable Pooling Methods for Video Classification
ECCV 2018, Workshop on YouTube-8M Large-Scale Video Understanding (Oral Presentation)
Paper Code
Teaching