- See Also
-
Links
- “CLLMs: Consistency Large Language Models”, Kou et al 2024
- “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
- “Accelerating Transformer Inference for Translation via Parallel Decoding”, Santilli 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
- Miscellaneous
- Link Bibliography
See Also
Links
“CLLMs: Consistency Large Language Models”, Kou et al 2024
“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
“Accelerating Transformer Inference for Translation via Parallel Decoding”, Santilli et al 2023
Accelerating Transformer Inference for Translation via Parallel Decoding
“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
Miscellaneous
Link Bibliography
-
https://arxiv.org/abs/2311.01017: “Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion”,