Multi-resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking. - J. Nathan Kutz; Xing Fu; Steven L. Brunton; N. Benjamin Erichson (ICCV Workshops, 2015)

Compressed Dynamic Mode Decomposition for Real-Time Object Detection. - N. Benjamin Erichson; Steven L. Brunton; J. Nathan Kutz (CoRR, 2015)

Randomized Matrix Decompositions using R. - N. Benjamin Erichson; Sergey Voronin; Steven L. Brunton; J. Nathan Kutz (CoRR, 2016)

Randomized low-rank Dynamic Mode Decomposition for motion detection. - N. Benjamin Erichson; Carl Donovan (Comput. Vis. Image Underst., 2016)

Compressed Singular Value Decomposition for Image and Video Processing. - N. Benjamin Erichson; Steven L. Brunton; J. Nathan Kutz (ICCV Workshops, 2017)

Dynamic Mode Decomposition for Background Modeling. - Seth D. Pendergrass; Steven L. Brunton; J. Nathan Kutz; N. Benjamin Erichson; Travis Askham (ICCV Workshops, 2017)

Randomized Dynamic Mode Decomposition. - N. Benjamin Erichson; Steven L. Brunton; J. Nathan Kutz (CoRR, 2017)

Diffusion Maps meet Nyström. - N. Benjamin Erichson; Lionel Mathelin; Steven L. Brunton; J. Nathan Kutz (CoRR, 2018)

Sparse Principal Component Analysis via Variable Projection. - N. Benjamin Erichson; Peng Zeng; Krithika Manohar; Steven L. Brunton; J. Nathan Kutz; Aleksandr Y. Aravkin (CoRR, 2018)

Randomized nonnegative matrix factorization. - N. Benjamin Erichson; Ariana Mendible; Sophie Wihlborn; J. Nathan Kutz (Pattern Recognit. Lett., 2018)

Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data. - N. Benjamin Erichson; Lionel Mathelin; Zhewei Yao; Steven L. Brunton; Michael W. Mahoney; J. Nathan Kutz (CoRR, 2019)

Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction. - N. Benjamin Erichson; Michael Muehlebach; Michael W. Mahoney (CoRR, 2019)

Randomized methods to characterize large-scale vortical flow network. - Zhe Bai; N. Benjamin Erichson; Muralikrishnan Gopalakrishnan Meena; Kunihiko Taira; Steven L. Brunton (CoRR, 2019)

Bootstrapping the Operator Norm in High Dimensions: Error Estimation for Covariance Matrices and Sketching. - Miles E. Lopes; N. Benjamin Erichson; Michael W. Mahoney (CoRR, 2019)

Compressed dynamic mode decomposition for background modeling. - N. Benjamin Erichson; Steven L. Brunton; J. Nathan Kutz (J. Real Time Image Process., 2019)

Randomized Dynamic Mode Decomposition. - N. Benjamin Erichson; Lionel Mathelin; J. Nathan Kutz; Steven L. Brunton (SIAM J. Appl. Dyn. Syst., 2019)

RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology. - Chen Gong; N. Benjamin Erichson; John P. Kelly; Laura C. Trutoiu; Brian T. Schowengerdt; Steven L. Brunton; Eric J. Seibel (IEEE Trans. Medical Imaging, 2019)

Forecasting Sequential Data Using Consistent Koopman Autoencoders. - Omri Azencot; N. Benjamin Erichson; Vanessa Lin; Michael W. Mahoney (ICML, 2020)

Error Estimation for Sketched SVD via the Bootstrap. - Miles E. Lopes; N. Benjamin Erichson; Michael W. Mahoney (ICML, 2020)

JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks. - N. Benjamin Erichson; Zhewei Yao; Michael W. Mahoney (ICPRAM, 2020)

Lipschitz Recurrent Neural Networks. - N. Benjamin Erichson; Omri Azencot; Alejandro F. Queiruga; Michael W. Mahoney (CoRR, 2020)

Adversarially-Trained Deep Nets Transfer Better. - Francisco Utrera; Evan Kravitz; N. Benjamin Erichson; Rajiv Khanna; Michael W. Mahoney (CoRR, 2020)

Noise-response Analysis for Rapid Detection of Backdoors in Deep Neural Networks. - N. Benjamin Erichson; Dane Taylor; Qixuan Wu; Michael W. Mahoney (CoRR, 2020)

Continuous-in-Depth Neural Networks. - Alejandro F. Queiruga; N. Benjamin Erichson; Dane Taylor; Michael W. Mahoney (CoRR, 2020)

Randomized CP tensor decomposition. - N. Benjamin Erichson; Krithika Manohar; Steven L. Brunton; J. Nathan Kutz (Mach. Learn. Sci. Technol., 2020)

Sparse Principal Component Analysis via Variable Projection. - N. Benjamin Erichson; Peng Zheng 002; Krithika Manohar; Steven L. Brunton; J. Nathan Kutz; Aleksandr Y. Aravkin (SIAM J. Appl. Math., 2020)

Lipschitz Recurrent Neural Networks. - N. Benjamin Erichson; Omri Azencot; Alejandro F. Queiruga; Liam Hodgkinson; Michael W. Mahoney (ICLR, 2021)

Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification. - Francisco Utrera; Evan Kravitz; N. Benjamin Erichson; Rajiv Khanna; Michael W. Mahoney (ICLR, 2021)

Noisy Recurrent Neural Networks. - Soon Hoe Lim; N. Benjamin Erichson; Liam Hodgkinson; Michael W. Mahoney (NeurIPS, 2021)

Stateful ODE-Nets using Basis Function Expansions. - Alejandro F. Queiruga; N. Benjamin Erichson; Liam Hodgkinson; Michael W. Mahoney (NeurIPS, 2021)

Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware. - N. Benjamin Erichson; Dane Taylor; Qixuan Wu; Michael W. Mahoney (SDM, 2021)

A Differential Geometry Perspective on Orthogonal Recurrent Models. - Omri Azencot; N. Benjamin Erichson; Mirela Ben-Chen; Michael W. Mahoney (CoRR, 2021)

Compressing Deep ODE-Nets using Basis Function Expansions. - Alejandro F. Queiruga; N. Benjamin Erichson; Liam Hodgkinson; Michael W. Mahoney (CoRR, 2021)

Cluster-and-Conquer: A Framework For Time-Series Forecasting. - Reese Pathak; Rajat Sen; Nikhil Rao 001; N. Benjamin Erichson; Michael I. Jordan; Inderjit S. Dhillon (CoRR, 2021)

Noisy Feature Mixup. - Soon Hoe Lim; N. Benjamin Erichson; Francisco Utrera; Winnie Xu; Michael W. Mahoney (ICLR, 2022)

Long Expressive Memory for Sequence Modeling. - T. Konstantin Rusch; Siddhartha Mishra; N. Benjamin Erichson; Michael W. Mahoney (ICLR, 2022)

NoisyMix: Boosting Robustness by Combining Data Augmentations, Stability Training, and Noise Injections. - N. Benjamin Erichson; Soon Hoe Lim; Francisco Utrera; Winnie Xu; Ziang Cao; Michael W. Mahoney (CoRR, 2022)

Learning continuous models for continuous physics. - Aditi S. Krishnapriyan; Alejandro F. Queiruga; N. Benjamin Erichson; Michael W. Mahoney (CoRR, 2022)

Error Estimation for Random Fourier Features. - Junwen Yao; N. Benjamin Erichson; Miles E. Lopes (AISTATS, 2023)

Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software. - Riley Murray; James Demmel; Michael W. Mahoney; N. Benjamin Erichson; Maksim Melnichenko; Osman Asif Malik; Laura Grigori; Piotr Luszczek; Michal Derezinski; Miles E. Lopes; Tianyu Liang; Hengrui Luo; et al. (CoRR, 2023)

SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning. - Pu Ren; N. Benjamin Erichson; Shashank Subramanian; Omer San; Zarija Lukic; Michael W. Mahoney (CoRR, 2023)

NoisyMix: Boosting Model Robustness to Common Corruptions. - N. Benjamin Erichson; Soon Hoe Lim; Winnie Xu; Francisco Utrera; Ziang Cao; Michael W. Mahoney (AISTATS, 2024)

Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs. - Ilan Naiman; N. Benjamin Erichson; Pu Ren; Michael W. Mahoney; Omri Azencot (ICLR, 2024)

Robustifying State-space Models for Long Sequences via Approximate Diagonalization. - Annan Yu; Arnur Nigmetov; Dmitriy Morozov; Michael W. Mahoney; N. Benjamin Erichson (ICLR, 2024)

There is HOPE to Avoid HiPPOs for Long-memory State Space Models. - Annan Yu; Michael W. Mahoney; N. Benjamin Erichson (CoRR, 2024)

WaveCastNet: An AI-enabled Wavefield Forecasting Framework for Earthquake Early Warning. - Dongwei Lyu; Rie Nakata; Pu Ren; Michael W. Mahoney; Arben Pitarka; Nori Nakata; N. Benjamin Erichson (CoRR, 2024)

Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling. - Pu Ren; Rie Nakata; Maxime Lacour; Ilan Naiman; Nori Nakata; Jialin Song; Zhengfa Bi; Osman Asif Malik; Dmitriy Morozov; Omri Azencot; N. Benjamin Erichson; Michael W. Mahoney (CoRR, 2024)

Emoji Attack: A Method for Misleading Judge LLMs in Safety Risk Detection. - Zhipeng Wei; Yuqi Liu; N. Benjamin Erichson (CoRR, 2024)

Gated Recurrent Neural Networks with Weighted Time-Delay Feedback. - N. Benjamin Erichson; Soon Hoe Lim; Michael W. Mahoney (AISTATS, 2025)

Tuning Frequency Bias of State Space Models. - Annan Yu; Dongwei Lyu; Soon Hoe Lim; Michael W. Mahoney; N. Benjamin Erichson (ICLR, 2025)

HOPE for a Robust Parameterization of Long-memory State Space Models. - Annan Yu; Michael W. Mahoney; N. Benjamin Erichson (ICLR, 2025)

Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting. - Soon Hoe Lim; Yijin Wang; Annan Yu; Emma Hart; Michael W. Mahoney; Xiaoye Sherry Li; N. Benjamin Erichson (Trans. Mach. Learn. Res., 2025)

A Deep State Space Model for Rainfall-Runoff Simulations. - Yihan Wang; Lujun Zhang; Annan Yu; N. Benjamin Erichson; Tiantian Yang (CoRR, 2025)

Powerformer: A Transformer with Weighted Causal Attention for Time-series Forecasting. - Kareem Hegazy; Michael W. Mahoney; N. Benjamin Erichson (CoRR, 2025)

MatterChat: A Multi-Modal LLM for Material Science. - Yingheng Tang; Wenbin Xu; Jie Cao 010; Jianzhu Ma; Weilu Gao; Steve Farrell; N. Benjamin Erichson; Michael W. Mahoney; Andy Nonaka; Zhi Yao (CoRR, 2025)

Removing Watermarks with Partial Regeneration using Semantic Information. - Krti Tallam; John Kevin Cava; Caleb Geniesse; N. Benjamin Erichson; Michael W. Mahoney (CoRR, 2025)

Block-Biased Mamba for Long-Range Sequence Processing. - Annan Yu; N. Benjamin Erichson (CoRR, 2025)

FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems. - N. Benjamin Erichson; Vinicius Mikuni; Dongwei Lyu; Yang Gao; Omri Azencot; Soon Hoe Lim; Michael W. Mahoney (CoRR, 2025)