- See Also
-
Links
- “Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion”, Zhang et al 2023
- “CodeFusion: A Pre-Trained Diffusion Model for Code Generation”, Singh et al 2023
- “Bayesian Flow Networks”, Graves et al 2023
- “Difformer: Empowering Diffusion Model on Embedding Space for Text Generation”, Gao et al 2022
- “Score-Based Continuous-Time Discrete Diffusion Models”, Sun et al 2022
- “CDCD: Continuous Diffusion for Categorical Data”, Dieleman et al 2022
- “Self-Conditioned Embedding Diffusion for Text Generation”, Strudel et al 2022
- “DiffusER: Discrete Diffusion via Edit-Based Reconstruction”, Reid et al 2022
- “Analog Bits: Generating Discrete Data Using Diffusion Models With Self-Conditioning”, Chen et al 2022
- “Diffusion-LM Improves Controllable Text Generation”, Li et al 2022
- “Time Control: Language Modeling via Stochastic Processes”, Wang et al 2022
- “Step-Unrolled Denoising Autoencoders for Text Generation”, Savinov et al 2021
- “Zero-Shot Translation Using Diffusion Models”, Nachmani & Dovrat 2021
- “Autoregressive Diffusion Models”, Hoogeboom et al 2021
- “Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models”, Johnson et al 2021
- “Structured Denoising Diffusion Models in Discrete State-Spaces”, Austin et al 2021
- “Symbolic Music Generation With Diffusion Models”, Mittal et al 2021
- “Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions”, Hoogeboom et al 2021
- Sort By Magic
- Miscellaneous
- Link Bibliography
See Also
Links
“Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion”, Zhang et al 2023
Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
“CodeFusion: A Pre-Trained Diffusion Model for Code Generation”, Singh et al 2023
CodeFusion: A Pre-trained Diffusion Model for Code Generation
“Bayesian Flow Networks”, Graves et al 2023
“Difformer: Empowering Diffusion Model on Embedding Space for Text Generation”, Gao et al 2022
Difformer: Empowering Diffusion Model on Embedding Space for Text Generation
“Score-Based Continuous-Time Discrete Diffusion Models”, Sun et al 2022
“CDCD: Continuous Diffusion for Categorical Data”, Dieleman et al 2022
“Self-Conditioned Embedding Diffusion for Text Generation”, Strudel et al 2022
“DiffusER: Discrete Diffusion via Edit-Based Reconstruction”, Reid et al 2022
“Analog Bits: Generating Discrete Data Using Diffusion Models With Self-Conditioning”, Chen et al 2022
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
“Diffusion-LM Improves Controllable Text Generation”, Li et al 2022
“Time Control: Language Modeling via Stochastic Processes”, Wang et al 2022
“Step-Unrolled Denoising Autoencoders for Text Generation”, Savinov et al 2021
“Zero-Shot Translation Using Diffusion Models”, Nachmani & Dovrat 2021
“Autoregressive Diffusion Models”, Hoogeboom et al 2021
“Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models”, Johnson et al 2021
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models
“Structured Denoising Diffusion Models in Discrete State-Spaces”, Austin et al 2021
Structured Denoising Diffusion Models in Discrete State-Spaces
“Symbolic Music Generation With Diffusion Models”, Mittal et al 2021
“Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions”, Hoogeboom et al 2021
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Sort By Magic
Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
generative-models
discrete-diffusion
diffusion-generation
Miscellaneous
Link Bibliography
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https://arxiv.org/abs/2311.01017
: “Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion”,