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OpenAI Sparse Transformer Improves Predictable Sequence Length by 30x | by Synced | SyncedReview | Medium
OpenAI Sparse Transformer Improves Predictable Sequence Length by 30x | by Synced | SyncedReview | Medium

Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever · Distribution Augmentation for Generative Modeling · SlidesLive
Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever · Distribution Augmentation for Generative Modeling · SlidesLive

DO DEEP GENERATIVE MODELS KNOW WHAT THEY DON'T KNOW?
DO DEEP GENERATIVE MODELS KNOW WHAT THEY DON'T KNOW?

Ramin Raziperchikolaei and Miguel´A. Carreira-Perpi ˜n ´an, UC Merced
Ramin Raziperchikolaei and Miguel´A. Carreira-Perpi ˜n ´an, UC Merced

Results of BPD (bits per dim) on CIFAR10 and ImageNet32 datasets.... | Download Scientific Diagram
Results of BPD (bits per dim) on CIFAR10 and ImageNet32 datasets.... | Download Scientific Diagram

DO DEEP GENERATIVE MODELS KNOW WHAT THEY DON'T KNOW?
DO DEEP GENERATIVE MODELS KNOW WHAT THEY DON'T KNOW?

Review: Image Transformer. Image Generation and Super Resolution… | by Sik-Ho Tsang | Medium
Review: Image Transformer. Image Generation and Super Resolution… | by Sik-Ho Tsang | Medium

PDF] Distribution Augmentation for Generative Modeling | Semantic Scholar
PDF] Distribution Augmentation for Generative Modeling | Semantic Scholar

CIFAR-10 Benchmark (Image Generation) | Papers With Code
CIFAR-10 Benchmark (Image Generation) | Papers With Code

Deep Learning with CIFAR-10. Neural Networks are the programmable… | by Aarya Brahmane | Towards Data Science
Deep Learning with CIFAR-10. Neural Networks are the programmable… | by Aarya Brahmane | Towards Data Science

Figure 5 from Flow-GAN: Bridging implicit and prescribed learning in generative models | Semantic Scholar
Figure 5 from Flow-GAN: Bridging implicit and prescribed learning in generative models | Semantic Scholar

Autoregressive Generative Modeling with Noise Conditional Maximum Likelihood Estimation | DeepAI
Autoregressive Generative Modeling with Noise Conditional Maximum Likelihood Estimation | DeepAI

Experiment on CIFAR with PixelCNN as family P. Meaning of plots is... | Download Scientific Diagram
Experiment on CIFAR with PixelCNN as family P. Meaning of plots is... | Download Scientific Diagram

Bits per pixel for models (lower is better) using logit transforms on... | Download Scientific Diagram
Bits per pixel for models (lower is better) using logit transforms on... | Download Scientific Diagram

PDF] Invertible Residual Networks | Semantic Scholar
PDF] Invertible Residual Networks | Semantic Scholar

Distribution Augmentation for Generative Modeling
Distribution Augmentation for Generative Modeling

How Can We Make Robotics More like Generative Modeling? | Eric Jang
How Can We Make Robotics More like Generative Modeling? | Eric Jang

PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

Results of BPD (bits per dim) on CIFAR10 and ImageNet32 datasets.... | Download Scientific Diagram
Results of BPD (bits per dim) on CIFAR10 and ImageNet32 datasets.... | Download Scientific Diagram

Variational Diffusion Models | DeepAI
Variational Diffusion Models | DeepAI

Bytepawn - Marton Trencseni – Solving CIFAR-10 with Pytorch and SKL
Bytepawn - Marton Trencseni – Solving CIFAR-10 with Pytorch and SKL

Normalizing Flows with Multi-Scale Autoregressive Priors | DeepAI
Normalizing Flows with Multi-Scale Autoregressive Priors | DeepAI

CIFAR-10 Benchmark (Image Generation) | Papers With Code
CIFAR-10 Benchmark (Image Generation) | Papers With Code

a) Density estimation performance of each model for the CIFAR10 dataset... | Download Scientific Diagram
a) Density estimation performance of each model for the CIFAR10 dataset... | Download Scientific Diagram

arXiv:2106.03802v1 [cs.LG] 7 Jun 2021
arXiv:2106.03802v1 [cs.LG] 7 Jun 2021

BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling | DeepAI
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling | DeepAI