Gonzalo Mena
Citat de
Citat de
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), eabg5298, 2021
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
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
Integrated multimodal cell atlas of Alzheimer’s disease
MI Gabitto, KJ Travaglini, VM Rachleff, ES Kaplan, B Long, J Ariza, Y Ding, ...
Research Square, 2023
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
Sinkhorn Networks: Using Optimal Transport Techniques to Learn Permutations
G Mena, D Belanger, G Muñoz, J Snoek
NIPS workshop on Optimal Transport & Machine Learning, 2017
Statistical Atlas of C. elegans Neurons
E Varol, A Nejatbakhsh, R Sun, G Mena, E Yemini, O Hobert, L Paninski
Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020
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
Unequal impact of the COVID-19 pandemic in 2020 on life expectancy across urban areas in Chile: a cross-sectional demographic study
G Mena, JM Aburto
BMJ open 12 (8), e059201, 2022
On Quadrature Methods for Refractory Point Process Likelihoods
G Mena, L Paninski
Neural computation 26 (12), 2790-2797, 2014
Quantifying the impact of SARS-CoV-2 temporal vaccination trends and disparities on disease control
SL Larsen, I Shin, J Joseph, H West, R Anorga, GE Mena, AS Mahmud, ...
Science Advances 9 (31), eadh9920, 2023
Sinkhorn Permutation Variational Marginal Inference
G Mena, E Varol, A Nejatbakhsh, E Yemini, L Paninski
2nd Symposium on Advances in Approximate Bayesian Inference, 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
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
Learning Permutations with gradient descent and the sinkhorn operator
G Mena, D Belanger, S Linderman, JR Snoek
Proc. Int. Conf. on Learning Representations (ICLR). Vancouver, Canada …, 2018
Hierarchical Bayesian inference to model continuous phenotypical progression in Alzheimer's Disease
A Agrawal, VM Rachleff, KJ Travaglini, S Mukherjee, P Crane, ...
bioRxiv, 2024.06. 10.597236, 2024
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