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2020 – today
- 2025
[c17]Priya Kasimbeg, Frank Schneider, Runa Eschenhagen, Juhan Bae, Chandramouli Shama Sastry, Mark Saroufim, Boyuan Feng, Less Wright, Edward Z. Yang, Zachary Nado, Sourabh Medapati, Philipp Hennig, Michael Rabbat, George E. Dahl:
Accelerating neural network training: An analysis of the AlgoPerf competition. ICLR 2025- [c16]Bruno Kacper Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner:
Influence Functions for Scalable Data Attribution in Diffusion Models. ICLR 2025 - [c15]Laura Ruis, Maximilian Mozes, Juhan Bae, Siddhartha Rao Kamalakara, Dwaraknath Gnaneshwar, Acyr Locatelli, Robert Kirk, Tim Rocktäschel, Edward Grefenstette, Max Bartolo:
Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models. ICLR 2025 - [i22]Deric Cheng, Juhan Bae, Justin Bullock, David Kristofferson:
Training Data Attribution (TDA): Examining Its Adoption & Use Cases. CoRR abs/2501.12642 (2025) - [i21]Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Roger B. Grosse:
Spectral-factorized Positive-definite Curvature Learning for NN Training. CoRR abs/2502.06268 (2025) - [i20]Priya Kasimbeg, Frank Schneider, Runa Eschenhagen, Juhan Bae, Chandramouli Shama Sastry, Mark Saroufim, Boyuan Feng, Less Wright, Edward Z. Yang, Zachary Nado, Sourabh Medapati, Philipp Hennig, Michael Rabbat, George E. Dahl:
Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition. CoRR abs/2502.15015 (2025) - [i19]Zachary Coalson, Juhan Bae, Nicholas Carlini, Sanghyun Hong:
IF-GUIDE: Influence Function-Guided Detoxification of LLMs. CoRR abs/2506.01790 (2025) - [i18]Andrew Wang, Elisa Nguyen, Runshi Yang, Juhan Bae, Sheila A. McIlraith, Roger B. Grosse:
Better Training Data Attribution via Better Inverse Hessian-Vector Products. CoRR abs/2507.14740 (2025) - [i17]Shiyuan Zhang, Junwei Deng, Juhan Bae, Jiaqi Ma:
Exploring Training Data Attribution under Limited Access Constraints. CoRR abs/2509.12581 (2025) - 2024
- [c14]Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani:
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. ICML 2024: 29949-29973 - [c13]Juhan Bae, Wu Lin, Jonathan Lorraine, Roger B. Grosse:
Training Data Attribution via Approximate Unrolling. NeurIPS 2024 - [i16]Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani:
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. CoRR abs/2402.03496 (2024) - [i15]Juhan Bae, Wu Lin, Jonathan Lorraine, Roger B. Grosse:
Training Data Attribution via Approximate Unrolled Differentiation. CoRR abs/2405.12186 (2024) - [i14]Sang Keun Choe, Hwijeen Ahn, Juhan Bae, Kewen Zhao, Minsoo Kang, Youngseog Chung, Adithya Pratapa, Willie Neiswanger, Emma Strubell, Teruko Mitamura, Jeff G. Schneider, Eduard H. Hovy
, Roger B. Grosse, Eric P. Xing:
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions. CoRR abs/2405.13954 (2024) - [i13]Bruno Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner:
Influence Functions for Scalable Data Attribution in Diffusion Models. CoRR abs/2410.13850 (2024) - [i12]Laura Ruis, Maximilian Mozes, Juhan Bae, Siddhartha Rao Kamalakara, Dwarak Talupuru, Acyr Locatelli, Robert Kirk, Tim Rocktäschel, Edward Grefenstette, Max Bartolo:
Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models. CoRR abs/2411.12580 (2024) - 2023
- [c12]Juhan Bae, Michael R. Zhang, Michael Ruan, Eric Wang, So Hasegawa, Jimmy Ba, Roger Baker Grosse:
Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve. ICLR 2023 - [c11]Nikita Dhawan, Sicong Huang, Juhan Bae, Roger Baker Grosse:
Efficient Parametric Approximations of Neural Network Function Space Distance. ICML 2023: 7795-7812 - [i11]Nikita Dhawan, Sicong Huang, Juhan Bae, Roger B. Grosse:
Efficient Parametric Approximations of Neural Network Function Space Distance. CoRR abs/2302.03519 (2023) - [i10]George E. Dahl, Frank Schneider, Zachary Nado, Naman Agarwal, Chandramouli Shama Sastry, Philipp Hennig, Sourabh Medapati, Runa Eschenhagen, Priya Kasimbeg, Daniel Suo, Juhan Bae, Justin Gilmer, Abel L. Peirson, Bilal Khan, Rohan Anil, Mike Rabbat, Shankar Krishnan, Daniel Snider, Ehsan Amid, Kongtao Chen, Chris J. Maddison, Rakshith Vasudev, Michal Badura, Ankush Garg, Peter Mattson:
Benchmarking Neural Network Training Algorithms. CoRR abs/2306.07179 (2023) - [i9]Roger B. Grosse, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner, Dustin Li, Esin Durmus, Ethan Perez, Evan Hubinger, Kamile Lukosiute, Karina Nguyen, Nicholas Joseph, Sam McCandlish, Jared Kaplan, Samuel R. Bowman:
Studying Large Language Model Generalization with Influence Functions. CoRR abs/2308.03296 (2023) - [i8]Michael R. Zhang, Nishkrit Desai, Juhan Bae, Jonathan Lorraine, Jimmy Ba:
Using Large Language Models for Hyperparameter Optimization. CoRR abs/2312.04528 (2023) - 2022
- [c10]Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger B. Grosse:
If Influence Functions are the Answer, Then What is the Question? NeurIPS 2022 - [c9]Juhan Bae, Paul Vicol, Jeff Z. HaoChen, Roger B. Grosse:
Amortized Proximal Optimization. NeurIPS 2022 - [i7]Juhan Bae, Paul Vicol, Jeff Z. HaoChen, Roger B. Grosse:
Amortized Proximal Optimization. CoRR abs/2203.00089 (2022) - [i6]Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, Roger B. Grosse:
If Influence Functions are the Answer, Then What is the Question? CoRR abs/2209.05364 (2022) - [i5]Juhan Bae, Michael R. Zhang, Michael Ruan, Eric Wang, So Hasegawa, Jimmy Ba, Roger B. Grosse:
Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve. CoRR abs/2212.03905 (2022) - 2021
- [c8]James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse:
On Monotonic Linear Interpolation of Neural Network Parameters. ICML 2021: 7168-7179 - [i4]James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse:
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes. CoRR abs/2104.11044 (2021) - 2020
- [c7]Juhan Bae, Roger B. Grosse:
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians. NeurIPS 2020 - [i3]Juhan Bae, Roger B. Grosse:
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians. CoRR abs/2010.13514 (2020)
2010 – 2019
- 2019
- [c6]Bowen Chen, Juhan Bae, Dibyendu Mukherjee:
Fast 6DOF Pose Estimation with Synthetic Textureless CAD Model for Mobile Applications. ICIP 2019: 2541-2545 - 2018
- [c5]Juhan Bae, Jeongyeon Lim, So-Ki Jung:
Study on HDR/WCG Service Model for UHD Service. APSIPA 2018: 68-72 - [c4]Sebastian Kmiec, Juhan Bae, Ruijian An:
Learnable Pooling Methods for Video Classification. ECCV Workshops (4) 2018: 229-238 - [i2]Sebastian Kmiec, Juhan Bae, Ruijian An:
Learnable Pooling Methods for Video Classification. CoRR abs/1810.00530 (2018) - [i1]Juhan Bae, Guodong Zhang, Roger B. Grosse:
Eigenvalue Corrected Noisy Natural Gradient. CoRR abs/1811.12565 (2018) - 2015
- [c3]Euntae Hong, Juhan Bae, Jongwoo Lim:
Robust visual tracking through deep learning-based confidence evaluation. URAI 2015: 581-584 - 2014
- [c2]Juhan Bae, Youngbae Hwang, Jongwoo Lim:
Semi-online video stabilization using probabilistic keyframe update and inter-keyframe motion smoothing. ICIP 2014: 5786-5790 - 2013
- [c1]Juhan Bae, Youngbae Hwang, Byeongho Choi:
Background subtraction using edge cues and color difference for stabilized CMOS images. ICCE 2013: 165-166
Coauthor Index
aka: Roger Baker Grosse
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last updated on 2026-02-10 22:59 CET by the dblp team
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