normalizing flow

Approximation Capabilities of Neural ODEs and Invertible Residual Networks ICML 2020 Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics ICML 2020 C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds CVPR 2020 Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow NeurIPS 2020 Coping With Simulators That Don’t Always Return AISTATS 2020 Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators NeurIPS 2020 Deep Residual Flow for Out of Distribution Detection CVPR 2020 Differentiable Causal Discovery from Interventional Data NeurIPS 2020 Efficient WaveGlow: An Improved WaveGlow Vocoder with Enhanced Speed Interspeech 2020 Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities ICML 2020 Exchangeable Generative Models with Flow Scans AAAI 2020 Fast and Flexible Temporal Point Processes with Triangular Maps NeurIPS 2020 Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests NeurIPS 2020 Flexible Approximate Inference via Stratified Normalizing Flows UAI 2020 Flows for simultaneous manifold learning and density estimation NeurIPS 2020 FlowSynth: Simplifying Complex Audio Generation Through Explorable Latent Spaces with Normalizing Flows IJCAI 2020 Gaussianization Flows AISTATS 2020 Generative Flows with Matrix Exponential ICML 2020 Gradient Boosted Normalizing Flows NeurIPS 2020 Hamiltonian Generative Networks ICLR 2020 Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows NeurIPS 2020 Intensity-Free Learning of Temporal Point Processes ICLR 2020 Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax NeurIPS 2020 Invertible Generative Modeling using Linear Rational Splines AISTATS 2020 Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings ICLR 2020