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2020 – today
- 2024
[j1]Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila R. Fiete:
Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building. Trans. Mach. Learn. Res. 2024 (2024)- [c9]Raymond Wang, Jaedong Hwang, Akhilan Boopathy, Ila R. Fiete:
Rapid Learning without Catastrophic Forgetting in the Morris Water Maze. ICML 2024 - [c8]Akhilan Boopathy, Aneesh Muppidi, Peggy Yang, Abhiram Iyer, William Yue, Ila Fiete:
Resampling-free Particle Filters in High-dimensions. ICRA 2024: 16292-16298 - [c7]Akhilan Boopathy, William Yue, Jaedong Hwang, Abhiram Iyer, Ila Fiete:
Towards Exact Computation of Inductive Bias. IJCAI 2024: 3733-3741 - [i12]Akhilan Boopathy, Aneesh Muppidi, Peggy Yang, Abhiram Iyer, William Yue, Ila Fiete:
Resampling-free Particle Filters in High-dimensions. CoRR abs/2404.13698 (2024) - [i11]Akhilan Boopathy, William Yue, Jaedong Hwang, Abhiram Iyer, Ila Fiete:
Towards Exact Computation of Inductive Bias. CoRR abs/2406.15941 (2024) - [i10]Akhilan Boopathy, Sunshine Jiang, William Yue, Jaedong Hwang, Abhiram Iyer, Ila Fiete:
Breaking Neural Network Scaling Laws with Modularity. CoRR abs/2409.05780 (2024) - [i9]Akhilan Boopathy, Ila Fiete:
Unified Neural Network Scaling Laws and Scale-time Equivalence. CoRR abs/2409.05782 (2024) - 2023
- [c6]Akhilan Boopathy, Kevin Liu, Jaedong Hwang, Shu Ge, Asaad Mohammedsaleh, Ila Fiete:
Model-agnostic Measure of Generalization Difficulty. ICML 2023: 2857-2884 - [i8]Rylan Schaeffer, Mikail Khona, Zachary Robertson, Akhilan Boopathy, Kateryna Pistunova, Jason W. Rocks, Ila Rani Fiete, Oluwasanmi Koyejo:
Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle. CoRR abs/2303.14151 (2023) - [i7]Akhilan Boopathy, Kevin Liu, Jaedong Hwang, Shu Ge, Asaad Mohammedsaleh, Ila Fiete:
Model-agnostic Measure of Generalization Difficulty. CoRR abs/2305.01034 (2023) - [i6]Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete:
Neuro-Inspired Efficient Map Building via Fragmentation and Recall. CoRR abs/2307.05793 (2023) - [i5]Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete:
Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity. CoRR abs/2310.17537 (2023) - 2022
- [c5]Akhilan Boopathy, Ila Fiete:
How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective. ICML 2022: 2178-2205 - 2021
- [c4]Akhilan Boopathy, Lily Weng, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, Luca Daniel:
Fast Training of Provably Robust Neural Networks by SingleProp. AAAI 2021: 6803-6811 - [i4]Akhilan Boopathy, Tsui-Wei Weng, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, Luca Daniel:
Fast Training of Provably Robust Neural Networks by SingleProp. CoRR abs/2102.01208 (2021) - [i3]Akhilan Boopathy, Ila Fiete:
Gradient-trained Weights in Wide Neural Networks Align Layerwise to Error-scaled Input Correlations. CoRR abs/2106.08453 (2021) - 2020
- [c3]Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, Luca Daniel:
Proper Network Interpretability Helps Adversarial Robustness in Classification. ICML 2020: 1014-1023 - [i2]Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, Luca Daniel:
Proper Network Interpretability Helps Adversarial Robustness in Classification. CoRR abs/2006.14748 (2020)
2010 – 2019
- 2019
- [c2]Akhilan Boopathy, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks. AAAI 2019: 3240-3247 - [c1]Lily Weng, Pin-Yu Chen, Lam M. Nguyen, Mark S. Squillante, Akhilan Boopathy, Ivan V. Oseledets, Luca Daniel:
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach. ICML 2019: 6727-6736 - 2018
- [i1]Akhilan Boopathy, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks. CoRR abs/1811.12395 (2018)
Coauthor Index
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