Gonzalo Mena
Gonzalo Mena
University of Oxford
Verified email at columbia.edu - Homepage
Cited by
Cited by
Learning Latent Permutations With Gumbel-Sinkhorn Networks
G Mena, D Belanger, S Linderman, J Snoek
The Sixth International Conference on Learning Representations (ICLR), 2018
NeuroPAL: a multicolor atlas for Whole-Brain neuronal identification in C. elegans
E Yemini, A Lin, A Nejatbakhsh, E Varol, R Sun, GE Mena, ADT Samuel, ...
Cell 184 (1), 272-288. e11, 2021
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
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
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
Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile
GE Mena, PP Martinez, AS Mahmud, PA Marquet, CO Buckee, ...
Science 372 (6545), 2021
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
On Quadrature Methods for Refractory Point Process Likelihoods
G Mena, L Paninski
Neural computation 26 (12), 2790-2797, 2014
Statistical Atlas of C. elegans Neurons
E Varol, A Nejatbakhsh, R Sun, G Mena, E Yemini, O Hobert, L Paninski
International Conference on Medical Image Computing and Computer-Assisted …, 2020
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
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
Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transport
G Mena, A Nejatbakhsh, E Varol, J Niles-Weed
arXiv preprint arXiv:2006.16548, 2020
Sinkhorn Permutation Variational Marginal Inference
G Mena, E Varol, A Nejatbakhsh, E Yemini, L Paninski
2nd Symposium on Advances in Approximate Bayesian Inference, 2019
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
A unified framework for de-duplication and population size estimation (contributed discussion)
N Ju, N Biswas, PE Jacob, G Mena, J O'Leary, E Pompe
Bayesian Analysis 15 (2), 2020
Semi-automated cell identification in NeuroPAL C. elegans strains
G Mena, A Nejatbakhsh, R Sun, E Varol, E Yemini, L Paninski
Statistical Machine Learning Methods for the Large-Scale Analysis of Neural Data
GE Mena
Columbia University, 2018
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference: Supplementary Material
SW Linderman, GE Mena, H Cooper, L Paninski, JP Cunningham
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