Eva L. Dyer
TitleCited byYear
Greedy feature selection for subspace clustering
EL Dyer, AC Sankaranarayanan, RG Baraniuk
Journal of Machine Learning Research 14 (1), 2487-2517, 2013
1392013
Rapid FPGA characterzation using clock synthesis and signal sparsity
M Majzoobi, E Dyer, A Elnably, F Koushanfar
IEEE International Test Conference (ITC), Austin, TX, 2010
29*2010
Quantifying mesoscale neuroanatomy using x-ray microtomography
EL Dyer, WG Roncal, JA Prasad, HL Fernandes, D Gürsoy, V De Andrade, ...
eneuro 4 (5), 2017
21*2017
Low-dose x-ray tomography through a deep convolutional neural network
X Yang, V De Andrade, W Scullin, EL Dyer, N Kasthuri, F De Carlo, ...
Scientific reports 8 (1), 2575, 2018
152018
Deterministic Column Sampling for Low-Rank Matrix Approximation: Nyström vs. Incomplete Cholesky Decomposition
R Patel, T Goldstein, E Dyer, A Mirhoseini, R Baraniuk
Proceedings of the 2016 SIAM International Conference on Data Mining, 594-602, 2016
14*2016
Self-expressive decompositions for matrix approximation and clustering
EL Dyer, TA Goldstein, R Patel, KP Kording, RG Baraniuk
arXiv preprint arXiv:1505.00824, 2015
142015
RankMap: A Framework for Distributed Learning From Dense Data Sets
A Mirhoseini, EL Dyer, EM Songhori, R Baraniuk, F Koushanfar
IEEE transactions on neural networks and learning systems 29 (7), 2717-2730, 2017
11*2017
A robust and efficient method to recover neural events from noisy and corrupted data
EL Dyer, C Studer, JT Robinson, RG Baraniuk
2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), 593-596, 2013
112013
Validation system of MR image overlay and other needle insertion techniques
JD Westwood
Medicine Meets Virtual Reality 15: In Vivo, in Vitro, in Silico: Designing …, 2007
11*2007
Recovering spikes from noisy neuronal calcium signals via structured sparse approximation
EL Dyer, MF Duarte, DH Johnson, RG Baraniuk
International Conference on Latent Variable Analysis and Signal Separation …, 2010
92010
Latent factors and dynamics in motor cortex and their application to brain–machine interfaces
C Pandarinath, KC Ames, AA Russo, A Farshchian, LE Miller, EL Dyer, ...
Journal of Neuroscience 38 (44), 9390-9401, 2018
82018
Approximating cellular densities from high-resolution neuroanatomical imaging data
TJ LaGrow, MG Moore, JA Prasad, MA Davenport, EL Dyer
2018 40th Annual International Conference of the IEEE Engineering in …, 2018
62018
Subspace clustering with dense representations
EL Dyer, C Studer, RG Baraniuk
2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013
62013
A cryptography-based approach for movement decoding
EL Dyer, MG Azar, MG Perich, HL Fernandes, S Naufel, LE Miller, ...
Nature biomedical engineering 1 (12), 967, 2017
52017
An automated pipeline for the collection, transfer, and processing of large-scale tomography data
M Du, R Vescovi, R Chard, N Kasthuri, C Jacobsen, E Dyer, D Gürsoy
Optics and the Brain, BF4C. 2, 2018
32018
Endogenous sparse recovery
EL Dyer
Rice University, 2012
32012
Hybrid modeling of non-stationary process variations
E Dyer, M Majzoobi, F Koushanfar
Proceedings of the 48th Design Automation Conference, 194-199, 2011
32011
Generative models and abstractions for large-scale neuroanatomy datasets
D Rolnick, EL Dyer
Current opinion in neurobiology 55, 112-120, 2019
22019
Convex relaxation regression: Black-box optimization of smooth functions by learning their convex envelopes
MG Azar, E Dyer, K Kording
arXiv preprint arXiv:1602.02191, 2016
22016
Learning hybrid linear models via sparse recovery
EL Dyer, AC Sankaranarayanan, RG Baraniuk
SPARS 2011 Proceedings, 2011
22011
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Articles 1–20