| Wasserstein Uncertainty Estimation for Adversarial Domain Matching | 0 | 0.34 | 2022 |
| Predicting in-hospital length of stay: a two-stage modeling approach to account for highly skewed data | 0 | 0.34 | 2022 |
| Capturing actionable dynamics with structured latent ordinary differential equations. | 0 | 0.34 | 2022 |
| Gradient Importance Learning for Incomplete Observations | 0 | 0.34 | 2022 |
| Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images | 1 | 0.35 | 2021 |
| SpanPredict: Extraction of Predictive Document Spans with Neural Attention | 0 | 0.34 | 2021 |
| Wasserstein Contrastive Representation Distillation | 0 | 0.34 | 2021 |
| Counterfactual Representation Learning with Balancing Weights | 0 | 0.34 | 2021 |
| Advancing weakly supervised cross-domain alignment with optimal transport. | 0 | 0.34 | 2020 |
| Sequence Generation With Optimal-Transport-Enhanced Reinforcement Learning | 0 | 0.34 | 2020 |
| Communication-Efficient Stochastic Gradient MCMC for Neural Networks | 0 | 0.34 | 2019 |
| Classifying abnormalities in computed tomography radiology reports with rule-based and natural language processing models. | 0 | 0.34 | 2019 |
| Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods | 0 | 0.34 | 2019 |
| Improving Textual Network Learning with Variational Homophilic Embeddings | 0 | 0.34 | 2019 |
| Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports. | 0 | 0.34 | 2019 |
| Joint Embedding of Words and Labels for Text Classification. | 0 | 0.34 | 2018 |
| Chi-square Generative Adversarial Network. | 2 | 0.35 | 2018 |
| Deconvolutional Latent-Variable Model for Text Sequence Matching | 11 | 0.49 | 2018 |
| Joint Embedding Of Words And Labels For Text Classification | 7 | 0.41 | 2018 |
| Nash: Toward End-To-End Neural Architecture For Generative Semantic Hashing | 2 | 0.36 | 2018 |
| Variational Inference and Model Selection with Generalized Evidence Bounds. | 2 | 0.37 | 2018 |
| Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms. | 0 | 0.34 | 2018 |
| Adversarial Time-to-Event Modeling. | 0 | 0.34 | 2018 |
| Baseline Needs More Love: On Simple Word-Embedding-Based Models And Associated Pooling Mechanisms | 9 | 0.45 | 2018 |
| NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing. | 0 | 0.34 | 2018 |
| Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment. | 0 | 0.34 | 2018 |
| JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets. | 3 | 0.37 | 2018 |
| VAE Learning via Stein Variational Gradient Descent. | 12 | 0.64 | 2017 |
| Gaussian process based independent analysis for temporal source separation in fMRI. | 0 | 0.34 | 2017 |
| Adversarial Symmetric Variational Autoencoder | 12 | 0.54 | 2017 |
| Learning Generic Sentence Representations Using Convolutional Neural Networks. | 15 | 0.65 | 2017 |
| ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching | 7 | 0.42 | 2017 |
| Adversarial Feature Matching for Text Generation. | 36 | 0.95 | 2017 |
| Deconvolutional Paragraph Representation Learning | 13 | 0.57 | 2017 |
| Towards Unifying Hamiltonian Monte Carlo and Slice Sampling. | 0 | 0.34 | 2016 |
| Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization. | 3 | 0.39 | 2016 |
| Bayesian Dictionary Learning with Gaussian Processes and Sigmoid Belief Networks. | 1 | 0.35 | 2016 |
| Triply Stochastic Variational Inference for Non-linear Beta Process Factor Analysis | 0 | 0.34 | 2016 |
| Electronic Health Record Analysis via Deep Poisson Factor Models. | 0 | 0.34 | 2016 |
| Variational Autoencoder for Deep Learning of Images, Labels and Captions. | 45 | 1.33 | 2016 |
| Dynamic Poisson Factor Analysis | 0 | 0.34 | 2016 |
| Learning a Hybrid Architecture for Sequence Regression and Annotation | 0 | 0.34 | 2016 |
| Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings | 10 | 0.49 | 2015 |
| Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood | 0 | 0.34 | 2015 |
| Scalable Deep Poisson Factor Analysis for Topic Modeling | 22 | 0.84 | 2015 |
| Deep Temporal Sigmoid Belief Networks for Sequence Modeling | 18 | 0.82 | 2015 |
| A Multitask Point Process Predictive Model | 13 | 0.70 | 2015 |
| Deep Poisson Factor Modeling | 6 | 0.44 | 2015 |
| Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling. | 9 | 0.53 | 2014 |
| A flexible statistical model for alignment of label-free proteomics data - incorporating ion mobility and product ion information. | 5 | 0.34 | 2013 |
