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Hierarchical Inducing Point Gaussian Process for Inter-domian Observations.

, , , , , and . AISTATS, volume 130 of Proceedings of Machine Learning Research, page 2926-2934. PMLR, (2021)

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Workshop summary: Numerical mathematics in machine learning., , and . ICML, volume 382 of ACM International Conference Proceeding Series, page 9. ACM, (2009)Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax., , and . CoRR, (2019)Annular Augmentation Sampling., , and . AISTATS, volume 54 of Proceedings of Machine Learning Research, page 139-147. PMLR, (2017)Empirical models of spiking in neural populations., , , , , and . NIPS, page 1350-1358. (2011)The continuous Bernoulli: fixing a pervasive error in variational autoencoders., and . NeurIPS, page 13266-13276. (2019)Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity., , , , , and . NIPS, page 1881-1888. Curran Associates, Inc., (2008)Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations., , , , and . NeurIPS, (2020)Posterior Collapse and Latent Variable Non-identifiability., , and . NeurIPS, page 5443-5455. (2021)Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome., , and . NeurIPS, (2022)Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning., , , and . ICBINB@NeurIPS, volume 137 of Proceedings of Machine Learning Research, page 1-10. PMLR, (2020)