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| Fully probabilistic deep models for forward and inverse problems in parametric PDEs | Aug 9, 2022 | Variational Inference | —Unverified | 0 |
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| Deep Probabilistic Models to Detect Data Poisoning Attacks | Dec 3, 2019 | Data PoisoningVariational Inference | —Unverified | 0 |
| Learning to Dequantise with Truncated Flows | Sep 29, 2021 | Variational Inference | —Unverified | 0 |
| Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes | Dec 27, 2024 | Gaussian ProcessesGPU | —Unverified | 0 |
| Learning to Learn Dense Gaussian Processes for Few-Shot Learning | Dec 1, 2021 | Few-Shot LearningGaussian Processes | —Unverified | 0 |
| Deep Probabilistic Video Compression | Sep 27, 2018 | DiversityImage Compression | —Unverified | 0 |
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| Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems | May 18, 2022 | parameter estimationTime Series | —Unverified | 0 |
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| Less Suboptimal Learning and Control in Variational POMDPs | Mar 9, 2021 | Model-based Reinforcement Learningreinforcement-learning | —Unverified | 0 |
| Leveraging Bayesian Analysis To Improve Accuracy of Approximate Models | May 20, 2019 | Uncertainty QuantificationVariational Inference | —Unverified | 0 |
| Learning Deep Latent-variable MRFs with Amortized Bethe Free Energy Minimization | Mar 27, 2019 | Variational Inference | —Unverified | 0 |
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| Marginal Likelihood Gradient for Bayesian Neural Networks | Nov 23, 2020 | Variational Inference | —Unverified | 0 |
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| Learning Causally-Generated Stationary Time Series | Feb 22, 2018 | Time SeriesTime Series Analysis | —Unverified | 0 |