Generative Models

Improving Inference for Neural Image Compression NeurIPS 2020 Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation AISTATS 2020 Improving Molecular Design by Stochastic Iterative Target Augmentation ICML 2020 Improving Opus Low Bit Rate Quality with Neural Speech Synthesis Interspeech 2020 Improving Speech Intelligibility Through Speaker Dependent and Independent Spectral Style Conversion Interspeech 2020 Improving Text Generation with Student-Forcing Optimal Transport EMNLP 2020 Improving Unsupervised Sparsespeech Acoustic Models with Categorical Reparameterization Interspeech 2020 Inception LSTM for Next-frame Video Prediction (Student Abstract) AAAI 2020 Inducing Hierarchical Compositional Model by Sparsifying Generator Network CVPR 2020 Inference Strategies for Machine Translation with Conditional Masking EMNLP 2020 InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs ICML 2020 Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip AAAI 2020 Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models ICLR 2020 Instance Selection for GANs NeurIPS 2020 Integrating Deep Learning with Logic Fusion for Information Extraction AAAI 2020 Intelligent Home 3D: Automatic 3D-House Design From Linguistic Descriptions Only CVPR 2020 Intensity-Free Learning of Temporal Point Processes ICLR 2020 Interactive Dual Generative Adversarial Networks for Image Captioning AAAI 2020 Interpretable Deep Gaussian Processes with Moments AISTATS 2020 Interpreting the Latent Space of GANs for Semantic Face Editing CVPR 2020 Interview: Large-scale Modeling of Media Dialog with Discourse Patterns and Knowledge Grounding EMNLP 2020 IntroVNMT: An Introspective Model for Variational Neural Machine Translation AAAI 2020 Intuitive, Interactive Beard and Hair Synthesis With Generative Models CVPR 2020 Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax NeurIPS 2020 Invertible generative models for inverse problems: mitigating representation error and dataset bias ICML 2020