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 | 491 | 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 | 260 | 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 | 207* | 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 | 177 | 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 | 57 | 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 | 50 | 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 | 44 | 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 | 32 | 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 | 26* | 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 | 24 | 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 | 16 | 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 | 13 | 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 | 11 | 2022 |
On Quadrature Methods for Refractory Point Process Likelihoods G Mena, L Paninski Neural computation 26 (12), 2790-2797, 2014 | 8 | 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 | 6 | 2023 |
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 | 5 | 2015 |
Sinkhorn Permutation Variational Marginal Inference G Mena, E Varol, A Nejatbakhsh, E Yemini, L Paninski 2nd Symposium on Advances in Approximate Bayesian Inference, 2019 | 4 | 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 | 2 | 2017 |
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 | | 2024 |
On model-based clustering with entropic optimal transport G Mena 2023 IMS International Conference on Statistics and Data Science (ICSDS), 103, 2023 | | 2023 |