Zobrazeno 1 - 10
of 361
pro vyhledávání: '"Paninski, Liam"'
Multi-animal pose estimation is essential for studying animals' social behaviors in neuroscience and neuroethology. Advanced approaches have been proposed to support multi-animal estimation and achieve state-of-the-art performance. However, these mod
Externí odkaz:
http://arxiv.org/abs/2204.07072
Autor:
Wang, Yueqi, Lee, Yoonho, Basu, Pallab, Lee, Juho, Teh, Yee Whye, Paninski, Liam, Pakman, Ari
Learning community structures in graphs has broad applications across scientific domains. While graph neural networks (GNNs) have been successful in encoding graph structures, existing GNN-based methods for community detection are limited by requirin
Externí odkaz:
http://arxiv.org/abs/2010.15727
Autor:
Couto, Joao, Musall, Simon, Sun, Xiaonan R, Khanal, Anup, Gluf, Steven, Saxena, Shreya, Kinsella, Ian, Abe, Taiga, Cunningham, John P., Paninski, Liam, Churchland, Anne K
Measurements of neuronal activity across brain areas are important for understanding the neural correlates of cognitive and motor processes like attention, decision-making, and action selection. However, techniques that allow cellular resolution meas
Externí odkaz:
http://arxiv.org/abs/2010.15191
Autor:
Wei, Xue-Xin, Zhou, Ding, Grosmark, Andres, Ajabi, Zaki, Sparks, Fraser, Zhou, Pengcheng, Brandon, Mark, Losonczy, Attila, Paninski, Liam
Publikováno v:
Neurons, Behavior, Data Analysis, and Theory, 2020
Calcium imaging is a critical tool for measuring the activity of large neural populations. Much effort has been devoted to developing "pre-processing" tools for calcium video data, addressing the important issues of e.g., motion correction, denoising
Externí odkaz:
http://arxiv.org/abs/2006.03737
Publikováno v:
ECML 2020
The Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) has been used widely as a natural Bayesian nonparametric extension of the classical Hidden Markov Model for learning from sequential and time-series data. A sticky extension of the HDP-
Externí odkaz:
http://arxiv.org/abs/2004.03019
Publikováno v:
The Journal of Machine Learning Research, 2021
Gaussian Processes (GPs) provide powerful probabilistic frameworks for interpolation, forecasting, and smoothing, but have been hampered by computational scaling issues. Here we investigate data sampled on one dimension (e.g., a scalar or vector time
Externí odkaz:
http://arxiv.org/abs/2003.05554
Publikováno v:
Published in Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 119, 2020
Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces. For these models, posterior inference methods can be inaccurate and/
Externí odkaz:
http://arxiv.org/abs/1901.00409
Autor:
Pakman, Ari, Paninski, Liam
We develop methods for efficient amortized approximate Bayesian inference over posterior distributions of probabilistic clustering models, such as Dirichlet process mixture models. The approach is based on mapping distributed, symmetry-invariant repr
Externí odkaz:
http://arxiv.org/abs/1811.09747
Autor:
Hernandez, Daniel, Moretti, Antonio Khalil, Wei, Ziqiang, Saxena, Shreya, Cunningham, John, Paninski, Liam
Latent variable models have been widely applied for the analysis of time series resulting from experimental neuroscience techniques. In these datasets, observations are relatively smooth and possibly nonlinear. We present Variational Inference for No
Externí odkaz:
http://arxiv.org/abs/1811.02459
Autor:
Buchanan, E. Kelly, Kinsella, Ian, Zhou, Ding, Zhu, Rong, Zhou, Pengcheng, Gerhard, Felipe, Ferrante, John, Ma, Ying, Kim, Sharon, Shaik, Mohammed, Liang, Yajie, Lu, Rongwen, Reimer, Jacob, Fahey, Paul, Muhammad, Taliah, Dempsey, Graham, Hillman, Elizabeth, Ji, Na, Tolias, Andreas, Paninski, Liam
Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution. The resulting datasets are quite large, which has presented a barrier to routine open sharing of this data,
Externí odkaz:
http://arxiv.org/abs/1807.06203