Sparsity penalties in dynamical system estimation. - Adam S. Charles; Muhammad Salman Asif; Justin K. Romberg; Christopher J. Rozell (CISS, 2011)

Estimation and dynamic updating of time-varying signals with sparse variations. - Muhammad Salman Asif; Adam S. Charles; Justin K. Romberg; Christopher J. Rozell (ICASSP, 2011)

Learning Sparse Codes for Hyperspectral Imagery. - Adam S. Charles; Bruno A. Olshausen; Christopher J. Rozell (IEEE J. Sel. Top. Signal Process., 2011)

The Restricted Isometry Property for Echo State Networks with applications to sequence memory capacity. - Han Lun Yap; Adam S. Charles; Christopher J. Rozell (SSP, 2012)

Low Power Sparse Approximation on Reconfigurable Analog Hardware. - Samuel A. Shapero; Adam S. Charles; Christopher J. Rozell; Paul E. Hasler (IEEE J. Emerg. Sel. Topics Circuits Syst., 2012)

A Common Network Architecture Efficiently Implements a Variety of Sparsity-Based Inference Problems. - Adam S. Charles; Pierre Garrigues; Christopher J. Rozell (Neural Comput., 2012)

Dynamic filtering of sparse signals using reweighted ℓ1. - Adam S. Charles; Christopher J. Rozell (ICASSP, 2013)

Convergence of basis pursuit de-noising with dynamic filtering. - Adam S. Charles; Christopher J. Rozell (GlobalSIP, 2014)

Can random linear networks store multiple long input streams? - Adam S. Charles; Dong Yin; Christopher J. Rozell (GlobalSIP, 2014)

Spectral Superresolution of Hyperspectral Imagery Using Reweighted ℓ1 Spatial Filtering. - Adam S. Charles; Christopher J. Rozell (IEEE Geosci. Remote. Sens. Lett., 2014)

Short-Term Memory Capacity in Networks via the Restricted Isometry Property. - Adam S. Charles; Han Lun Yap; Christopher J. Rozell (Neural Comput., 2014)

Dynamics and correlations in sparse signal acquisition. - Adam S. Charles (2015)

Dynamic Filtering of Time-Varying Sparse Signals via ℓ1 Minimization. - Adam S. Charles; Aurele Balavoine; Christopher J. Rozell (IEEE Trans. Signal Process., 2016)

Earth-Mover's distance as a tracking regularizer. - Adam S. Charles; Nicholas P. Bertrand; John Lee 006; Christopher J. Rozell (CAMSAP, 2017)

Stochastic filtering of two-photon imaging using reweighted ℓ1. - Adam S. Charles; Alexander Song; Sue Ann Koay; David W. Tank; Jonathan W. Pillow (ICASSP, 2017)

Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks. - Adam S. Charles; Dong Yin; Christopher J. Rozell (J. Mach. Learn. Res., 2017)

Sparse Dynamic Filtering via Earth Mover's Distance Regularization. - Nicholas P. Bertrand; John Lee 006; Adam S. Charles; Pavel Dunn; Christopher J. Rozell (ICASSP, 2018)

Interpreting Deep Learning: The Machine Learning Rorschach Test? - Adam S. Charles (CoRR, 2018)

Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability. - Adam S. Charles; Mijung Park; J. Patrick Weller; Gregory D. Horwitz; Jonathan W. Pillow (Neural Comput., 2018)

Learning Spatially-correlated Temporal Dictionaries for Calcium Imaging. - Gal Mishne; Adam S. Charles (ICASSP, 2019)

Visualizing the PHATE of Neural Networks. - Scott Gigante; Adam S. Charles; Smita Krishnaswamy; Gal Mishne (NeurIPS, 2019)

Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield. - John S. Choi; Krishan Kumar; Mohammad Khazali; Katie Wingel; Mahdi Choudhury; Adam S. Charles; Bijan Pesaran (NeurIPS, 2020)

Efficient Tracking of Sparse Signals via an Earth Mover's Distance Dynamics Regularizer. - Nicholas P. Bertrand; Adam S. Charles; John Lee 006; Pavel Dunn; Christopher J. Rozell (IEEE Signal Process. Lett., 2020)

An Efficient and Flexible Spike Train Model Via Empirical Bayes. - Qi She; Xiaoli Wu; Beth Jelfs; Adam S. Charles; Rosa H. M. Chan (IEEE Trans. Signal Process., 2021)

Prospective Learning: Back to the Future. - Joshua T. Vogelstein; Timothy D. Verstynen; Konrad P. Kording; Leyla Isik; John W. Krakauer; Ralph Etienne-Cummings; Elizabeth L. Ogburn; Carey E. Priebe; Randal C. Burns; Kwame S. Kutten; James J. Knierim; James B. Potash; et al. (CoRR, 2022)

Multi-Lingual DALL-E Storytime. - Noga Mudrik; Adam S. Charles (CoRR, 2022)

GraFT: Graph Filtered Temporal Dictionary Learning for Functional Neural Imaging. - Adam S. Charles; Nathan Cermak; Rifqi O. Affan; Benjamin B. Scott; Jackie Schiller; Gal Mishne (IEEE Trans. Image Process., 2022)

Prospective Learning: Principled Extrapolation to the Future. - Ashwin De Silva; Rahul Ramesh; Lyle H. Ungar; Marshall G. Hussain Shuler; Noah J. Cowan; Michael L. Platt; Chen Li; Leyla Isik; Seung-Eon Roh; Adam S. Charles; Archana Venkataraman; Brian Caffo; et al. (CoLLAs, 2023)

Cellular-Resolution Image-Guided Localization in the Primate Brain. - Jacob Stefanowicz; John S. Choi; Katie Wingel; Jarl Haggerty; Adam S. Charles; Bijan Pesaran (EMBC, 2024)

SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States. - Noga Mudrik; Gal Mishne; Adam S. Charles (ICML, 2024)

Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics. - Yenho Chen; Noga Mudrik; Kyle A. Johnsen; Sankaraleengam Alagapan; Adam S. Charles; Christopher Rozell (NeurIPS, 2024)

realSEUDO for real-time calcium imaging analysis. - Iuliia Dmitrieva; Sergey Babkin; Adam S. Charles (NeurIPS, 2024)

Deep and shallow data science for multi-scale optical neuroscience. - Gal Mishne; Adam S. Charles (CoRR, 2024)

Multiway Multislice PHATE: Visualizing Hidden Dynamics of RNNs through Training. - Jiancheng Xie; Lou C. Kohler Voinov; Noga Mudrik; Gal Mishne; Adam S. Charles (CoRR, 2024)

Unsupervised discovery of the shared and private geometry in multi-view data. - Sai Koukuntla; Joshua B. Julian; Jesse C. Kaminsky; Manuel Schottdorf; David W. Tank; Carlos D. Brody; Adam S. Charles (CoRR, 2024)

Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics. - Noga Mudrik; Yenho Chen; Eva Yezerets; Christopher J. Rozell; Adam S. Charles (J. Mach. Learn. Res., 2024)

Sparse-Coding Variational Autoencoders. - Victor Geadah; Gabriel Barello; C. Daniel Greenidge; Adam S. Charles; Jonathan W. Pillow (Neural Comput., 2024)

LINOCS: Lookahead Inference of Networked Operators for Continuous Stability. - Noga Mudrik; Eva Yezerets; Yenho Chen; Christopher John Rozell; Adam Shabti Charles (Trans. Mach. Learn. Res., 2024)

CREIMBO: Cross-Regional Ensemble Interactions in Multi-view Brain Observations. - Noga Mudrik; Ryan Ly; Oliver Rübel; Adam Shabti Charles (ICLR, 2025)

Data mining the functional architecture of the brain's circuitry. - Adam S. Charles (SDM, 2025)