marginal likelihood

Last Layer Marginal Likelihood for Invariance Learning AISTATS 2022 Learning invariant weights in neural networks UAI 2022 Score-Based Diffusion meets Annealed Importance Sampling NeurIPS 2022 A Hybrid Approximation to the Marginal Likelihood AISTATS 2021 Differentiable Annealed Importance Sampling and the Perils of Gradient Noise NeurIPS 2021 Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding ICML 2021 Learning Adjustment Sets from Observational and Limited Experimental Data AAAI 2021 Marginalised Gaussian Processes with Nested Sampling NeurIPS 2021 Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning ICML 2021 You should evaluate your language model on marginal likelihood over tokenisations EMNLP 2021 A Bayesian Perspective on Training Speed and Model Selection NeurIPS 2020 Bayesian Image Classification with Deep Convolutional Gaussian Processes AISTATS 2020 Bayesian Network Model Averaging Classifiers by Subbagging PGM 2020 Biomedical Entity Representations with Synonym Marginalization ACL 2020 Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation ACL 2020 Generalized probabilistic principal component analysis of correlated data JMLR 2020 Identifying through Flows for Recovering Latent Representations ICLR 2020 Do Deep Generative Models Know What They Don't Know? ICLR 2019 Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse NeurIPS 2019 Fast Automatic Smoothing for Generalized Additive Models JMLR 2019 Improving Quadrature for Constrained Integrands AISTATS 2019 Iterative Search for Weakly Supervised Semantic Parsing NAACL 2019 Augment and Reduce: Stochastic Inference for Large Categorical Distributions ICML 2018 Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization ICML 2018 Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference ICLR 2018