dirichlet prior

COME: Test-time Adaption by Conservatively Minimizing Entropy ICLR 2025 Label Calibration in Source Free Domain Adaptation WACV 2025 Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts CVPR 2024 Double Spike Dirichlet Priors for Structured Weighting JMLR 2022 Advances in Using Grammars with Latent Annotations for Discontinuous Parsing ACL 2020 Co-Occurrence Estimation from Aggregated Data with Auxiliary Information AAAI 2020 Neural Topic Modeling with Cycle-Consistent Adversarial Training EMNLP 2020 BeamSeg: A Joint Model for Multi-Document Segmentation and Topic Identification CoNLL 2019 Open Event Extraction from Online Text using a Generative Adversarial Network EMNLP 2019 Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness NeurIPS 2019 Topic Modeling with Wasserstein Autoencoders ACL 2019 Minimax Optimal Bayes Mixtures for Memoryless Sources over Large Alphabets ALT 2018 An Empirical-Bayes Score for Discrete Bayesian Networks PGM 2016 Achievability of Asymptotic Minimax Regret by Horizon-Dependent and Horizon-Independent Strategies JMLR 2015 Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians ICML 2014 Bayesian entropy estimation for binary spike train data using parametric prior knowledge NeurIPS 2013 On Algorithms for Sparse Multi-factor NMF NeurIPS 2013 Marginal Likelihood Integrals for Mixtures of Independence Models JMLR 2009 Rethinking LDA: Why Priors Matter NeurIPS 2009 Dirichlet-Enhanced Spam Filtering based on Biased Samples NeurIPS 2006