variational inference

Learning Informative Representations of Biomedical Relations with Latent Variable Models EMNLP 2020 Learning Optimal Representations with the Decodable Information Bottleneck NeurIPS 2020 Learning Robot Skills with Temporal Variational Inference ICML 2020 Learning Sparse Prototypes for Text Generation NeurIPS 2020 Learning spectrograms with convolutional spectral kernels AISTATS 2020 Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks ICLR 2020 Learning to Learn Kernels with Variational Random Features ICML 2020 Learning to Learn Variational Semantic Memory NeurIPS 2020 Learning To Stop While Learning To Predict ICML 2020 Learning under Model Misspecification: Applications to Variational and Ensemble methods NeurIPS 2020 Learning with Labeled and Unlabeled Multi-Step Transition Data for Recovering Markov Chain from Incomplete Transition Data IJCAI 2020 Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations NeurIPS 2020 Lifted Hybrid Variational Inference IJCAI 2020 Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder NeurIPS 2020 Manifold GPLVMs for discovering non-Euclidean latent structure in neural data NeurIPS 2020 Markovian Score Climbing: Variational Inference with KL(p||q) NeurIPS 2020 Meta-Amortized Variational Inference and Learning AAAI 2020 Meta-Learning with Shared Amortized Variational Inference ICML 2020 Mixed-Membership Stochastic Block Models for Weighted Networks UAI 2020 Model Fusion with Kullback-Leibler Divergence ICML 2020 Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows NeurIPS 2020 Modeling Evolution of Message Interaction for Rumor Resolution COLING 2020 Multi-label Contrastive Predictive Coding NeurIPS 2020 Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence NeurIPS 2020 Narrative Text Generation with a Latent Discrete Plan EMNLP 2020