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Generative Models

7,221 papers

Papers per year

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Papers

The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization ICML 2020
Invertible generative models for inverse problems: mitigating representation error and dataset bias ICML 2020
Latent Variable Modelling with Hyperbolic Normalizing Flows ICML 2020
On Breaking Deep Generative Model-based Defenses and Beyond ICML 2020
Fair Generative Modeling via Weak Supervision ICML 2020
Scalable Deep Generative Modeling for Sparse Graphs ICML 2020
How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization ICML 2020
Stochastic Latent Residual Video Prediction ICML 2020
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks ICML 2020
A Free-Energy Principle for Representation Learning ICML 2020
Multilinear Latent Conditioning for Generating Unseen Attribute Combinations ICML 2020
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation ICML 2020
Evaluating Lossy Compression Rates of Deep Generative Models ICML 2020
Source Separation with Deep Generative Priors ICML 2020
T-GD: Transferable GAN-generated Images Detection Framework ICML 2020
Hierarchical Generation of Molecular Graphs using Structural Motifs ICML 2020
Multi-Objective Molecule Generation using Interpretable Substructures ICML 2020
On Relativistic f-Divergences ICML 2020
Distribution Augmentation for Generative Modeling ICML 2020
On the Power of Compressed Sensing with Generative Models ICML 2020
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities ICML 2020
On Implicit Regularization in $β$-VAEs ICML 2020
SGD Learns One-Layer Networks in WGANs ICML 2020
ACFlow: Flow Models for Arbitrary Conditional Likelihoods ICML 2020
Learning from Irregularly-Sampled Time Series: A Missing Data Perspective ICML 2020