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Adam S. Charles
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2020 – today
- 2024
- [j12]Noga Mudrik, Yenho Chen, Eva Yezerets, Christopher J. Rozell, Adam S. Charles:
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics. J. Mach. Learn. Res. 25: 59:1-59:44 (2024) - [c13]Noga Mudrik, Gal Mishne, Adam S. Charles:
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States. ICML 2024 - [i12]Gal Mishne, Adam S. Charles:
Deep and shallow data science for multi-scale optical neuroscience. CoRR abs/2402.08811 (2024) - [i11]Noga Mudrik, Eva Yezerets, Yenho Chen, Christopher Rozell, Adam S. Charles:
LINOCS: Lookahead Inference of Networked Operators for Continuous Stability. CoRR abs/2404.18267 (2024) - [i10]Jiancheng Xie, Lou C. Kohler Voinov, Noga Mudrik, Gal Mishne, Adam S. Charles:
Multiway Multislice PHATE: Visualizing Hidden Dynamics of RNNs through Training. CoRR abs/2406.01969 (2024) - [i9]Sai Koukuntla, Joshua B. Julian, Jesse C. Kaminsky, Manuel Schottdorf, David W. Tank, Carlos D. Brody, Adam S. Charles:
Unsupervised discovery of the shared and private geometry in multi-view data. CoRR abs/2408.12091 (2024) - [i8]Yenho Chen, Noga Mudrik, Kyle A. Johnsen, Sankaraleengam Alagapan, Adam S. Charles, Christopher J. Rozell:
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics. CoRR abs/2408.16862 (2024) - 2023
- [i7]Noga Mudrik, Gal Mishne, Adam S. Charles:
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States. CoRR abs/2306.04817 (2023) - 2022
- [j11]Adam S. Charles
, Nathan Cermak, Rifqi O. Affan
GraFT: Graph Filtered Temporal Dictionary Learning for Functional Neural Imaging. IEEE Trans. Image Process. 31: 3509-3524 (2022) - [i6]Noga Mudrik, Yenho Chen, Eva Yezerets
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics. CoRR abs/2206.02972 (2022) - [i5]Noga Mudrik, Adam S. Charles:
Multi-Lingual DALL-E Storytime. CoRR abs/2212.11985 (2022) - 2021
- [j10]Qi She
An Efficient and Flexible Spike Train Model Via Empirical Bayes. IEEE Trans. Signal Process. 69: 3236-3251 (2021) - 2020
- [j9]Nicholas P. Bertrand, Adam S. Charles
Efficient Tracking of Sparse Signals via an Earth Mover's Distance Dynamics Regularizer. IEEE Signal Process. Lett. 27: 1120-1124 (2020) - [c12]John S. Choi, Krishan Kumar, Mohammad Khazali, Katie Wingel, Mahdi Choudhury, Adam S. Charles, Bijan Pesaran:
Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield. NeurIPS 2020
2010 – 2019
- 2019
- [c11]Gal Mishne
Learning Spatially-correlated Temporal Dictionaries for Calcium Imaging. ICASSP 2019: 1065-1069 - [c10]Scott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal Mishne:
Visualizing the PHATE of Neural Networks. NeurIPS 2019: 1840-1851 - [i4]Scott Gigante
Visualizing the PHATE of Neural Networks. CoRR abs/1908.02831 (2019) - 2018
- [j8]Adam S. Charles, Mijung Park, J. Patrick Weller, Gregory D. Horwitz, Jonathan W. Pillow:
Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability. Neural Comput. 30(4) (2018) - [c9]Nicholas P. Bertrand, John Lee, Adam S. Charles, Pavel Dunn, Christopher J. Rozell:
Sparse Dynamic Filtering via Earth Mover's Distance Regularization. ICASSP 2018: 4334-4338 - [i3]Adam S. Charles:
Interpreting Deep Learning: The Machine Learning Rorschach Test? CoRR abs/1806.00148 (2018) - 2017
- [j7]Adam S. Charles, Dong Yin, Christopher J. Rozell:
Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks. J. Mach. Learn. Res. 18: 7:1-7:37 (2017) - [c8]Adam S. Charles, Nicholas P. Bertrand, John Lee, Christopher J. Rozell:
Earth-Mover's distance as a tracking regularizer. CAMSAP 2017: 1-5 - [c7]Adam S. Charles, Alexander Song, Sue Ann Koay, David W. Tank, Jonathan W. Pillow:
Stochastic filtering of two-photon imaging using reweighted ℓ1. ICASSP 2017: 1038-1042 - 2016
- [j6]Adam S. Charles
Dynamic Filtering of Time-Varying Sparse Signals via ℓ1 Minimization. IEEE Trans. Signal Process. 64(21): 5644-5656 (2016) - [i2]Adam S. Charles, Dong Yin, Christopher J. Rozell:
Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks. CoRR abs/1605.08346 (2016) - 2014
- [j5]Adam S. Charles, Christopher J. Rozell:
Spectral Superresolution of Hyperspectral Imagery Using Reweighted ℓ1 Spatial Filtering. IEEE Geosci. Remote. Sens. Lett. 11(3): 602-606 (2014) - [j4]Adam S. Charles, Han Lun Yap, Christopher J. Rozell
Short-Term Memory Capacity in Networks via the Restricted Isometry Property. Neural Comput. 26(6): 1198-1235 (2014) - [c6]Adam S. Charles, Christopher J. Rozell:
Convergence of basis pursuit de-noising with dynamic filtering. GlobalSIP 2014: 374-378 - [c5]Adam S. Charles, Dong Yin, Christopher J. Rozell:
Can random linear networks store multiple long input streams? GlobalSIP 2014: 379-383 - 2013
- [c4]Adam S. Charles, Christopher J. Rozell:
Dynamic filtering of sparse signals using reweighted ℓ1. ICASSP 2013: 6451-6455 - [i1]Adam S. Charles, Han Lun Yap, Christopher J. Rozell:
Short Term Memory Capacity in Networks via the Restricted Isometry Property. CoRR abs/1307.7970 (2013) - 2012
- [j3]Samuel A. Shapero, Adam S. Charles, Christopher J. Rozell, Paul E. Hasler:
Low Power Sparse Approximation on Reconfigurable Analog Hardware. IEEE J. Emerg. Sel. Topics Circuits Syst. 2(3): 530-541 (2012) - [j2]Adam S. Charles, Pierre Garrigues, Christopher J. Rozell
A Common Network Architecture Efficiently Implements a Variety of Sparsity-Based Inference Problems. Neural Comput. 24(12): 3317-3339 (2012) - [c3]Han Lun Yap, Adam S. Charles, Christopher J. Rozell:
The Restricted Isometry Property for Echo State Networks with applications to sequence memory capacity. SSP 2012: 580-583 - 2011
- [j1]Adam S. Charles, Bruno A. Olshausen, Christopher J. Rozell:
Learning Sparse Codes for Hyperspectral Imagery. IEEE J. Sel. Top. Signal Process. 5(5): 963-978 (2011) - [c2]Adam S. Charles, Muhammad Salman Asif
Sparsity penalties in dynamical system estimation. CISS 2011: 1-6 - [c1]Muhammad Salman Asif
Estimation and dynamic updating of time-varying signals with sparse variations. ICASSP 2011: 3908-3911
Coauthor Index
aka: Christopher Rozell
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last updated on 2024-10-07 21:20 CEST by the dblp team
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