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
[P1] Efficient Parametric Approximations of Neural Network Function Space Distance
arXiv 2023
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[C6] Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve
ICLR 2023 (Oral Presentation)
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[C5] If Influence Functions are the Answer, Then What is the Question?
NeurIPS 2022
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[C4] Amortized Proximal Optimization
NeurIPS 2022
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[C3] Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
ICML 2021
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[C2] Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians
NeurIPS 2020
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[W3] On Monotonic Linear Interpolation of Neural Network Parameters
NeurIPS 2020, Optimization for Machine Learning Workshop
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[W2] Eigenvalue Corrected Noisy Natural Gradient
NeurIPS 2019, Bayesian Deep Learning Workshop
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[C1] Fast 6DOF Pose Estimation with Synthetic Textureless CAD Model for Mobile Applications
ICIP 2019
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[W1] Learnable Pooling Methods for Video Classification
ECCV 2018, Workshop on YouTube-8M Large-Scale Video Understanding (Oral Presentation)
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Teaching