Gonzalo Mena
Gonzalo Mena
Data Science Initiative Postdoctoral Fellow, Harvard University
Verified email at columbia.edu - Homepage
TitleCited byYear
Learning Latent Permutations With Gumbel-Sinkhorn Networks
G Mena, D Belanger, S Linderman, J Snoek
The Sixth International Conference on Learning Representations (ICLR), 2018
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
On Quadrature Methods for Refractory Point Process Likelihoods
G Mena, L Paninski
Neural computation 26 (12), 2790-2797, 2014
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
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
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
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 Networks: Using Optimal Transport Techniques to Learn Permutations
G Mena, D Belanger, G Muņoz, J Snoek
NIPS workshop on Optimal Transport & Machine Learning, 2017
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
Sinkhorn Permutation Variational Marginal Inference
G Mena, E Varol, A Nejatbakhsh, E Yemini, L Paninski
2nd Symposium on Advances in Approximate Bayesian Inference, 2019
Statistical Machine Learning Methods for the Large Scale Analysis of Neural Data
GE Mena
Columbia University, 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 1–14