| Linear dynamical neural population models through nonlinear embeddings Y Gao, EW Archer, L Paninski, JP Cunningham Advances in neural information processing systems 29, 2016 | 183 | 2016 |
| Black box variational inference for state space models E Archer, IM Park, L Buesing, J Cunningham, L Paninski arXiv preprint arXiv:1511.07367, 2015 | 176 | 2015 |
| Bayesian Entropy Estimation for Countable Discrete Distributions E Archer, IM Park, JW Pillow Journal of Machine Learning Research 15, 2833-2868, 2014 | 109 | 2014 |
| Low-dimensional models of neural population activity in sensory cortical circuits E Archer, U Koster, JW Pillow, JH Macke Advances in Neural Information Processing Systems, 343-351, 2014 | 74 | 2014 |
| Spectral methods for neural characterization using generalized quadratic models IM Park, E Archer, N Priebe, JW Pillow Advances in Neural Information Processing Systems 26, 2454--2462, 2013 | 74 | 2013 |
| Fast amortized inference of neural activity from calcium imaging data with variational autoencoders A Speiser, J Yan, EW Archer, L Buesing, SC Turaga, JH Macke Advances in neural information processing systems 30, 2017 | 60 | 2017 |
| Bayesian and quasi-Bayesian estimators for mutual information from discrete data E Archer, IM Park, JW Pillow Entropy 15 (5), 1738-1755, 2013 | 52 | 2013 |
| Bayesian entropy estimation for binary spike train data using parametric prior knowledge E Archer, IM Park, JW Pillow Advances in Neural Information Processing Systems 26, 1700--1708, 2013 | 43 | 2013 |
| Bayesian estimation of discrete entropy with mixtures of stick-breaking priors E Archer, IM Park, JW Pillow Advances in Neural Information Processing Systems 25, 2024--2032, 2012 | 22 | 2012 |
| Universal models for binary spike patterns using centered Dirichlet processes IM Park, E Archer, K Latimer, JW Pillow Advances in Neural Information Processing Systems 26, 2463--2471, 2013 | 16 | 2013 |
| Value Function Decomposition for Iterative Design of Reinforcement Learning Agents J MacGlashan, E Archer, A Devlic, T Seno, C Sherstan, P Wurman, ... arXiv preprint arXiv:2206.13901, 2022 | 8 | 2022 |
| Scalable variational inference for super resolution microscopy R Sun, E Archer, L Paninski Artificial Intelligence and Statistics, 1057-1065, 2017 | 6 | 2017 |
| Amortized inference for fast spike prediction from calcium imaging data A Speiser, S Turaga, E Archer, JH Macke Computational and Systems Neuroscience Meeting (COSYNE 2017), 207-208, 2017 | | 2017 |
| Canonical correlations reveal co-variability between spike trains and local field potentials in area MT J Yates, E Archer, AC Huk, IM Park BMC Neuroscience 16, 1-2, 2015 | | 2015 |
| Low Dimensional Dynamical Models of Neural Populations with Common Input EW Archer, J Pillow, J Macke 15th Conference of Junior Neuroscientists of Tübingen (NeNa 2014): The …, 2014 | | 2014 |
| Low-dimensional dynamical neural population models with shared stimulus drive EW Archer, JW Pillow, JH Macke Bernstein Conference 2014, 72-73, 2014 | | 2014 |
| Low-dimensional models of neural population recordings with complex stimulus selectivity EW Archer, JW Pillow, JH Macke Computational and Systems Neuroscience Meeting (COSYNE 2014), 162-162, 2014 | | 2014 |
| Scalable nonparametric models for binary spike patterns IM Park, EW Archer, K Latimer, JW Pillow Computational and Systems Neuroscience Meeting (COSYNE 2014), 162-163, 2014 | | 2014 |
| Bayesian entropy estimators for spike trains IM Park, E Archer, JW Pillow BMC Neuroscience 14 (Suppl 1), P316, 2013 | | 2013 |
Il Memming ParkGroup Leader, Champalimaud Research在 research.fchampalimaud.org 的电子邮件经过验证
