Brian Cheung

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 31

Top Topics

Vector Quantized

Situation Aware

Top Venues

ICLR (Workshop)

Publications

Minyoung Huh, Brian Cheung, Tongzhou Wang, Phillip Isola
The Platonic Representation Hypothesis. CoRR (2024)
Minyoung Huh, Brian Cheung, Jeremy Bernstein, Phillip Isola, Pulkit Agrawal
Training Neural Networks from Scratch with Parallel Low-Rank Adapters. CoRR (2024)
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)
Utkarsh Singhal, Brian Cheung, Kartik Chandra, Jonathan Ragan-Kelley, Joshua B. Tenenbaum, Tomaso A. Poggio, Stella X. Yu
How to guess a gradient. CoRR (2023)
Jiayun Wang, Yubei Chen, Stella X. Yu, Brian Cheung, Yann LeCun
Compact and Optimal Deep Learning with Recurrent Parameter Generators. WACV (2023)
Yena Han, Tomaso A. Poggio, Brian Cheung
System identification of neural systems: If we got it right, would we know? CoRR (2023)
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)
Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks. CoRR (2023)
Yena Han, Tomaso A. Poggio, Brian Cheung
System Identification of Neural Systems: If We Got It Right, Would We Know? ICML (2023)
Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks. ICML (2023)
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)
Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic
Equivariant Contrastive Learning. CoRR (2021)
Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola
The Low-Rank Simplicity Bias in Deep Networks. CoRR (2021)
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)
Jiayun Wang, Yubei Chen, Stella X. Yu, Brian Cheung, Yann LeCun
Recurrent Parameter Generators. CoRR (2021)
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 (2021)
Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. ICML (2020)
Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. CoRR (2020)
Juexiao Zhang, Yubei Chen, Brian Cheung, Bruno A. Olshausen
Word Embedding Visualization Via Dictionary Learning. CoRR (2019)
Brian Cheung, Alexander Terekhov, Yubei Chen, Pulkit Agrawal, Bruno A. Olshausen
Superposition of many models into one. NeurIPS (2019)
Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein
Meta-Learning Update Rules for Unsupervised Representation Learning. ICLR (2019)
Brian Cheung, Alexander Terekhov, Yubei Chen, Pulkit Agrawal, Bruno A. Olshausen
Superposition of many models into one. CoRR (2019)
Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein
Learning Unsupervised Learning Rules. CoRR (2018)
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)
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 (2018)
Shariq Mobin, Brian Cheung, Bruno A. Olshausen
Convolutional vs. Recurrent Neural Networks for Audio Source Separation. CoRR (2018)
Brian Cheung, Mark Rutten, Samuel J. Davey, Greg Cohen
Probabilistic Multi Hypothesis Tracker for an Event Based Sensor. FUSION (2018)
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 (2018)
Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein
Learning to Learn Without Labels. ICLR (Workshop) (2018)
Brian Cheung, Eric Weiss, Bruno A. Olshausen
Emergence of foveal image sampling from learning to attend in visual scenes. ICLR (Poster) (2017)
Brian Cheung, Eric Weiss, Bruno A. Olshausen
Emergence of foveal image sampling from learning to attend in visual scenes. CoRR (2016)
Samuel J. Davey, Travis Bessell, Brian Cheung, Mark Rutten
Track before Detect for Space Situation Awareness. DICTA (2015)
Samuel J. Davey, Brian Cheung
A Linear Complexity Approximate Method for Multi-Target Particle Filter Track before Detect. DICTA (2015)
Brian Cheung, Jesse A. Livezey, Arjun K. Bansal, Bruno A. Olshausen
Discovering Hidden Factors of Variation in Deep Networks. ICLR (Workshop) (2015)
Brian Cheung, Samuel J. Davey, Douglas A. Gray
PMHT with timing uncertainty. IEEE Trans. Aerosp. Electron. Syst. 50 (4) (2014)
Bryan R. Conroy, Jennifer M. Walz, Brian Cheung, Paul Sajda
Fast Simultaneous Training of Generalized Linear Models (FaSTGLZ). CoRR (2013)
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 (2013)
Samuel J. Davey, Mark G. Rutten, Brian Cheung
Using Phase to Improve Track-Before-Detect. IEEE Trans. Aerosp. Electron. Syst. 48 (1) (2012)
Brian Cheung
Convolutional Neural Networks Applied to Human Face Classification. ICMLA (2) (2012)
Brian Cheung, Carl Sable
Hybrid Evolution of Convolutional Networks. ICMLA (1) (2011)
Brian Cheung, Samuel J. Davey, Douglas A. Gray
Comparison of the PMHT path planning algorithm with the Genetic Algorithm for multiple platforms. FUSION (2010)
Brian Cheung, Samuel J. Davey, Douglas A. Gray
PMHT for tracking with timing uncertainty. FUSION (2010)
Samuel J. Davey, Brian Cheung, Mark G. Rutten
Track-Before-Detect for sensors with complex measurements. FUSION (2009)
Hassan Edrees, Brian Cheung, McCullen Sandora, David B. Nummey, Deian Stefan
Hardware-Optimized Ziggurat Algorithm for High-Speed Gaussian Random Number Generators. ERSA (2009)
Brian Cheung, Samuel J. Davey, Douglas A. Gray
Combining PMHT with classifications to perform SLAM. FUSION (2009)
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)
Samuel J. Davey, Mark G. Rutten, Brian Cheung
A comparison of detection performance for several Track-Before-Detect algorithms. FUSION (2008)