Abstract is missing.
- HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous MarginalsChao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard E. Turner, José Miguel Hernández-Lobato, Cheng Zhang. 1-8 [doi]
- Approximate Inference for Fully Bayesian Gaussian Process RegressionVidhi Lalchand, Carl Edward Rasmussen. 1-12 [doi]
- Information in Infinite Ensembles of Infinitely-Wide Neural NetworksRavid Shwartz-Ziv, Alexander A. Alemi. 1-17 [doi]
- AdvancedHMC.jl: A robust, modular and e cient implementation of advanced HMC algorithmsKai Xu, Hong Ge, Will Tebbutt, Mohamed Tarek, Martin Trapp 0001, Zoubin Ghahramani. 1-10 [doi]
- Pseudo-Bayesian Learning via Direct Loss Minimization with Applications to Sparse Gaussian Process ModelsRishit Sheth, Roni Khardon. 1-18 [doi]
- MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic ProgrammingYura Perov, Logan Graham, Kostis Gourgoulias, Jonathan G. Richens, Ciarán M. Lee, Adam Baker, Saurabh Johri. 1-36 [doi]
- Rapid Model Comparison by Amortizing Across ModelsLily H. Zhang, Michael C. Hughes. 1-11 [doi]
- MMD-Bayes: Robust Bayesian Estimation via Maximum Mean DiscrepancyBadr-Eddine Chérief-Abdellatif, Pierre Alquier. 1-21 [doi]
- Variational Predictive Information BottleneckAlexander A. Alemi. 1-6 [doi]
- GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process ModelsPavel Berkovich, Eric Perim, Wessel Bruinsma. 1-14 [doi]
- Normalizing Constant Estimation with Gaussianized Bridge SamplingHe Jia, Uros Seljak. 1-14 [doi]
- Improving Sequential Latent Variable Models with Autoregressive FlowsJoseph Marino, Lei Chen, Jiawei He, Stephan Mandt. 1-16 [doi]
- Variational Bayesian Methods for Stochastically Constrained System Design ProblemsPrateek Jaiswal, Harsha Honnappa, Vinayak A. Rao. 1-12 [doi]
- The Gaussian Process Prior VAE for Interpretable Latent Dynamics from PixelsMichael Pearce. 1-12 [doi]
- Characterizing and Avoiding Problematic Global Optima of Variational AutoencodersYaniv Yacoby, Weiwei Pan, Finale Doshi-Velez. 1-17 [doi]
- Scalable Gradients and Variational Inference for Stochastic Differential EquationsXuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Kristjanson Duvenaud. 1-28 [doi]
- Neural Permutation ProcessesAri Pakman, Yueqi Wang, Liam Paninski. 1-7 [doi]
- Variational Gaussian Process Models without Matrix InversesMark van der Wilk, S. T. John, Artem Artemev, James Hensman. 1-9 [doi]
- Sinkhorn Permutation Variational Marginal InferenceGonzalo E. Mena, Erdem Varol, Amin Nejatbakhsh, Eviatar Yemini, Liam Paninski. 1-9 [doi]
- Variational Selective AutoencoderYu Gong, Hossein Hajimirsadeghi, Jiawei He, Megha Nawhal, Thibaut Durand, Greg Mori. 1-17 [doi]
- Bijectors.jl: Flexible transformations for probability distributionsTor Erlend Fjelde, Kai Xu, Mohamed Tarek, Sharan Yalburgi, Hong Ge. 1-17 [doi]