Gonzalo Mena
Gonzalo Mena
Data Science Initiative Postdoctoral Fellow, Harvard University
Verified email at columbia.edu - Homepage
Title
Cited by
Cited by
Year
Learning Latent Permutations With Gumbel-Sinkhorn Networks
G Mena, D Belanger, S Linderman, J Snoek
The Sixth International Conference on Learning Representations (ICLR), 2018
492018
Reparameterizing The Birkhoff Polytope for Variational Permutation Inference
SW Linderman, GE Mena, H Cooper, L Paninski, JP Cunningham
The 21nd International Conference on Artificial Intelligence and Statistics …, 2017
302017
Electrical Stimulus Artifact Cancellation and Neural Spike Detection on Large Multi-Electrode Arrays
GE Mena, LE Grosberg, S Madugula, P Hottowy, A Litke, J Cunningham, ...
PLoS computational biology 13 (11), e1005842, 2017
182017
Statistical Bounds for Entropic Optimal Transport: Sample Complexity and the Central Limit Theorem
G Mena, J Weed
Advances in Neural Information Processing Systems 32, 2019
72019
On Quadrature Methods for Refractory Point Process Likelihoods
G Mena, L Paninski
Neural computation 26 (12), 2790-2797, 2014
72014
Optimization of electrical stimulation for a high-fidelity artificial retina
NP Shah, S Madugula, L Grosberg, G Mena, P Tandon, P Hottowy, A Sher, ...
2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), 714-718, 2019
52019
NeuroPAL: A Neuronal Polychromatic Atlas of Landmarks for Whole-Brain Imaging in C. elegans
E Yemini, A Lin, A Nejatbakhsh, E Varol, R Sun, GE Mena, ADT Samuel, ...
BioRxiv, 676312, 2019
52019
Large-scale Multi Electrode Array Spike Sorting Algorithm Introducing Concurrent Recording and Stimulation
G Mena, L Grosberg, F Kellison-Linn, E Chichilnisky, L Paninski
NIPS workshop on Statistical Methods for Understanding Neural Systems, 2015
32015
Sinkhorn Networks: Using Optimal Transport Techniques to Learn Permutations
G Mena, D Belanger, G Muņoz, J Snoek
NIPS workshop on Optimal Transport & Machine Learning, 2017
22017
Toward Bayesian Permutation Inference for Identifying Neurons in C. elegans.
G Mena, S Linderman, D Belanger, J Snoek, J Cunningham, L Paninski
NIPS workshop on Worm's Neural Information Processing (WNIP)., 2017
1*2017
Sinkhorn Permutation Variational Marginal Inference
G Mena, E Varol, A Nejatbakhsh, E Yemini, L Paninski
2nd Symposium on Advances in Approximate Bayesian Inference, 2019
2019
Semi-automated cell identification in NeuroPAL C. elegans strains
G Mena, A Nejatbakhsh, R Sun, E Varol, E Yemini, L Paninski
2019
Statistical Machine Learning Methods for the Large Scale Analysis of Neural Data
GE Mena
Columbia University, 2018
2018
Supplement to “Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem”
G Mena, J Niles-Weed
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference: Supplementary Material
SW Linderman, GE Mena, H Cooper, L Paninski, JP Cunningham
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Articles 1–15