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Generative Models

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Papers

Uncertainty Principles of Encoding GANs ICML 2021
Understanding Noise Injection in GANs ICML 2021
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions ICML 2021
Hierarchical VAEs Know What They Don’t Know ICML 2021
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes ICML 2021
Latent Programmer: Discrete Latent Codes for Program Synthesis ICML 2021
Generative Adversarial Transformers ICML 2021
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions ICML 2021
Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization ICML 2021
Instance-Optimal Compressed Sensing via Posterior Sampling ICML 2021
Fairness for Image Generation with Uncertain Sensitive Attributes ICML 2021
Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics ICML 2021
Provable Lipschitz Certification for Generative Models ICML 2021
A Differentiable Point Process with Its Application to Spiking Neural Networks ICML 2021
Prior Image-Constrained Reconstruction using Style-Based Generative Models ICML 2021
Self Normalizing Flows ICML 2021
Functional Space Analysis of Local GAN Convergence ICML 2021
Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech ICML 2021
Representational aspects of depth and conditioning in normalizing flows ICML 2021
NeRF-VAE: A Geometry Aware 3D Scene Generative Model ICML 2021
Run-Sort-ReRun: Escaping Batch Size Limitations in Sliced Wasserstein Generative Models ICML 2021
HyperHyperNetwork for the Design of Antenna Arrays ICML 2021
Neural-Pull: Learning Signed Distance Function from Point clouds by Learning to Pull Space onto Surface ICML 2021
Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold ICML 2021
Generating images with sparse representations ICML 2021