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Brian Cheung
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2020 – today
- 2024
- [i16]Minyoung Huh, Brian Cheung, Jeremy Bernstein, Phillip Isola, Pulkit Agrawal:
Training Neural Networks from Scratch with Parallel Low-Rank Adapters. CoRR abs/2402.16828 (2024) - 2023
- [j6]Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola:
The Low-Rank Simplicity Bias in Deep Networks. Trans. Mach. Learn. Res. 2023 (2023) - [c24]Ileana Rugina, Rumen Dangovski, Mark Veillette, Pooya Khorrami, Brian Cheung, Olga Simek, Marin Soljacic:
Meta-Learning and Self-Supervised Pretraining for Storm Event Imagery Translation. HPEC 2023: 1-9 - [c23]Yena Han, Tomaso A. Poggio, Brian Cheung:
System Identification of Neural Systems: If We Got It Right, Would We Know? ICML 2023: 12430-12444 - [c22]Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola:
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks. ICML 2023: 14096-14113 - [c21]Jiayun Wang, Yubei Chen, Stella X. Yu, Brian Cheung, Yann LeCun:
Compact and Optimal Deep Learning with Recurrent Parameter Generators. WACV 2023: 3889-3899 - [i15]Yena Han, Tomaso A. Poggio, Brian Cheung:
System identification of neural systems: If we got it right, would we know? CoRR abs/2302.06677 (2023) - [i14]Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola:
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks. CoRR abs/2305.08842 (2023) - [i13]Utkarsh Singhal, Brian Cheung, Kartik Chandra, Jonathan Ragan-Kelley, Joshua B. Tenenbaum, Tomaso A. Poggio, Stella X. Yu:
How to guess a gradient. CoRR abs/2312.04709 (2023) - 2022
- [c20]Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic:
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations. ICLR 2022 - 2021
- [c19]Pooya Khorrami, Olga Simek, Brian Cheung, Mark Veillette, Rumen Dangovski, Ileana Rugina, Marin Soljacic, Pulkit Agrawal:
Adapting Deep Learning Models to New Meteorological Contexts Using Transfer Learning. IEEE BigData 2021: 4169-4177 - [i12]Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola:
The Low-Rank Simplicity Bias in Deep Networks. CoRR abs/2103.10427 (2021) - [i11]Jiayun Wang, Yubei Chen, Stella X. Yu, Brian Cheung, Yann LeCun:
Recurrent Parameter Generators. CoRR abs/2107.07110 (2021) - [i10]Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic:
Equivariant Contrastive Learning. CoRR abs/2111.00899 (2021) - [i9]Ileana Rugina, Rumen Dangovski, Mark Veillette, Pooya Khorrami, Brian Cheung, Olga Simek, Marin Soljacic:
Meta-Learning and Self-Supervised Pretraining for Real World Image Translation. CoRR abs/2112.11929 (2021) - 2020
- [c18]Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman:
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. ICML 2020: 11055-11065 - [i8]Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman:
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. CoRR abs/2008.06622 (2020)
2010 – 2019
- 2019
- [c17]Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Meta-Learning Update Rules for Unsupervised Representation Learning. ICLR 2019 - [c16]Brian Cheung, Alexander Terekhov, Yubei Chen, Pulkit Agrawal, Bruno A. Olshausen:
Superposition of many models into one. NeurIPS 2019: 10867-10876 - [i7]Brian Cheung, Alexander Terekhov, Yubei Chen, Pulkit Agrawal, Bruno A. Olshausen:
Superposition of many models into one. CoRR abs/1902.05522 (2019) - [i6]Juexiao Zhang, Yubei Chen, Brian Cheung, Bruno A. Olshausen:
Word Embedding Visualization Via Dictionary Learning. CoRR abs/1910.03833 (2019) - 2018
- [j5]Manuel Garcia-Garcia, Aki Nikolaidis, Pierre Bellec, R. Cameron Craddock, Brian Cheung, Francisco X. Castellanos, Michael P. Milham:
Detecting stable individual differences in the functional organization of the human basal ganglia. NeuroImage 170: 68-82 (2018) - [c15]Brian Cheung, Mark Rutten, Samuel J. Davey, Greg Cohen:
Probabilistic Multi Hypothesis Tracker for an Event Based Sensor. FUSION 2018: 1-8 - [c14]Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Learning to Learn Without Labels. ICLR (Workshop) 2018 - [c13]Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein:
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans. NeurIPS 2018: 3914-3924 - [i5]Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian J. Goodfellow, Jascha Sohl-Dickstein:
Adversarial Examples that Fool both Human and Computer Vision. CoRR abs/1802.08195 (2018) - [i4]Shariq Mobin, Brian Cheung, Bruno A. Olshausen:
Convolutional vs. Recurrent Neural Networks for Audio Source Separation. CoRR abs/1803.08629 (2018) - [i3]Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein:
Learning Unsupervised Learning Rules. CoRR abs/1804.00222 (2018) - 2017
- [c12]Brian Cheung, Eric Weiss, Bruno A. Olshausen:
Emergence of foveal image sampling from learning to attend in visual scenes. ICLR (Poster) 2017 - 2016
- [i2]Brian Cheung, Eric Weiss, Bruno A. Olshausen:
Emergence of foveal image sampling from learning to attend in visual scenes. CoRR abs/1611.09430 (2016) - 2015
- [c11]Samuel J. Davey, Travis Bessell, Brian Cheung, Mark Rutten:
Track before Detect for Space Situation Awareness. DICTA 2015: 1-7 - [c10]Samuel J. Davey, Brian Cheung:
A Linear Complexity Approximate Method for Multi-Target Particle Filter Track before Detect. DICTA 2015: 1-6 - [c9]Brian Cheung, Jesse A. Livezey, Arjun K. Bansal, Bruno A. Olshausen:
Discovering Hidden Factors of Variation in Deep Networks. ICLR (Workshop) 2015 - 2014
- [j4]Brian Cheung, Samuel J. Davey, Douglas A. Gray:
PMHT with timing uncertainty. IEEE Trans. Aerosp. Electron. Syst. 50(4): 2960-2973 (2014) - 2013
- [j3]Chao-Gan Yan, Brian Cheung, Clare Kelly, Stanley J. Colcombe, R. Cameron Craddock, Adriana Di Martino, Qingyang Li, Xi-Nian Zuo, F. Xavier Castellanos, Michael P. Milham:
A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage 76: 183-201 (2013) - [i1]Bryan R. Conroy, Jennifer M. Walz, Brian Cheung, Paul Sajda:
Fast Simultaneous Training of Generalized Linear Models (FaSTGLZ). CoRR abs/1307.8430 (2013) - 2012
- [j2]Samuel J. Davey, Mark G. Rutten, Brian Cheung:
Using Phase to Improve Track-Before-Detect. IEEE Trans. Aerosp. Electron. Syst. 48(1): 832-849 (2012) - [c8]Brian Cheung:
Convolutional Neural Networks Applied to Human Face Classification. ICMLA (2) 2012: 580-583 - 2011
- [c7]Brian Cheung, Carl Sable:
Hybrid Evolution of Convolutional Networks. ICMLA (1) 2011: 293-297 - 2010
- [c6]Brian Cheung, Samuel J. Davey, Douglas A. Gray:
PMHT for tracking with timing uncertainty. FUSION 2010: 1-6 - [c5]Brian Cheung, Samuel J. Davey, Douglas A. Gray:
Comparison of the PMHT path planning algorithm with the Genetic Algorithm for multiple platforms. FUSION 2010: 1-8
2000 – 2009
- 2009
- [c4]Hassan Edrees, Brian Cheung, McCullen Sandora, David B. Nummey, Deian Stefan:
Hardware-Optimized Ziggurat Algorithm for High-Speed Gaussian Random Number Generators. ERSA 2009: 254-260 - [c3]Brian Cheung, Samuel J. Davey, Douglas A. Gray:
Combining PMHT with classifications to perform SLAM. FUSION 2009: 324-331 - [c2]Samuel J. Davey, Brian Cheung, Mark G. Rutten:
Track-Before-Detect for sensors with complex measurements. FUSION 2009: 618-625 - 2008
- [j1]Samuel J. Davey, Mark G. Rutten, Brian Cheung:
A Comparison of Detection Performance for Several Track-before-Detect Algorithms. EURASIP J. Adv. Signal Process. 2008 (2008) - [c1]Samuel J. Davey, Mark G. Rutten, Brian Cheung:
A comparison of detection performance for several Track-Before-Detect algorithms. FUSION 2008: 1-8
Coauthor Index
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last updated on 2024-05-09 00:28 CEST by the dblp team
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