- See Also
-
Links
- “Finite Scalar Quantization (FSQ): VQ-VAE Made Simple”, Mentzer et al 2023
- “Finding Neurons in a Haystack: Case Studies With Sparse Probing”, Gurnee et al 2023
- “TANGO: Text-to-Audio Generation Using Instruction-Tuned LLM and Latent Diffusion Model”, Ghosal et al 2023
- “ACT: Learning Fine-Grained Bimanual Manipulation With Low-Cost Hardware”, Zhao et al 2023
- “IRIS: Transformers Are Sample-Efficient World Models”, Micheli et al 2022
- “Vector Quantized Image-to-Image Translation”, Chen et al 2022
- “Draft-and-Revise: Effective Image Generation With Contextual RQ-Transformer”, Lee et al 2022
- “UViM: A Unified Modeling Approach for Vision With Learned Guiding Codes”, Kolesnikov et al 2022
- “Closing the Gap: Exact Maximum Likelihood Training of Generative Autoencoders Using Invertible Layers (AEF)”, Silvestri et al 2022
- “AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars”, Hong et al 2022
- “NaturalSpeech: End-to-End Text to Speech Synthesis With Human-Level Quality”, Tan et al 2022
- “VQGAN-CLIP: Open Domain Image Generation and Editing With Natural Language Guidance”, Crowson et al 2022
- “TATS: Long Video Generation With Time-Agnostic VQGAN and Time-Sensitive Transformer”, Ge et al 2022
- “Diffusion Probabilistic Modeling for Video Generation”, Yang et al 2022
- “Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values”, Humayun et al 2022
- “Variational Autoencoders Without the Variation”, Daly et al 2022
- “Vector-quantized Image Modeling With Improved VQGAN”, Yu et al 2022
- “Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders”, Zheng et al 2022
- “MLR: A Model of Working Memory for Latent Representations”, Hedayati et al 2022
- “CM3: A Causal Masked Multimodal Model of the Internet”, Aghajanyan et al 2022
- “Design Guidelines for Prompt Engineering Text-to-Image Generative Models”, Liu & Chilton 2022b
- “DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents”, Pandey et al 2022
- “ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation”, Zhang et al 2021
- “Discovering State Variables Hidden in Experimental Data”, Chen et al 2021
- “High-Resolution Image Synthesis With Latent Diffusion Models”, Rombach et al 2021
- “VQ-DDM: Global Context With Discrete Diffusion in Vector Quantized Modelling for Image Generation”, Hu et al 2021
- “Vector Quantized Diffusion Model for Text-to-Image Synthesis”, Gu et al 2021
- “L-Verse: Bidirectional Generation Between Image and Text”, Kim et al 2021
- “Passive Non-Line-of-Sight Imaging Using Optimal Transport”, Geng et al 2021
- “Unsupervised Deep Learning Identifies Semantic Disentanglement in Single Inferotemporal Face Patch Neurons”, Higgins et al 2021
- “Telling Creative Stories Using Generative Visual Aids”, Ali & Parikh 2021
- “Illiterate DALL·E Learns to Compose”, Singh et al 2021
- “Score-based Generative Modeling in Latent Space”, Vahdat et al 2021
- “Vector Quantized Models for Planning”, Ozair et al 2021
- “NWT: Towards Natural Audio-to-video Generation With Representation Learning”, Mama et al 2021
- “VideoGPT: Video Generation Using VQ-VAE and Transformers”, Yan et al 2021
- “Symbolic Music Generation With Diffusion Models”, Mittal et al 2021
- “Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models”, Bond-Taylor et al 2021
- “Greedy Hierarchical Variational Autoencoders (GHVAEs) for Large-Scale Video Prediction”, Wu et al 2021
- “CW-VAE: Clockwork Variational Autoencoders”, Saxena et al 2021
- “Denoising Diffusion Implicit Models”, Song et al 2021
- “DALL·E 1: Creating Images from Text: We’ve Trained a Neural Network Called DALL·E That Creates Images from Text Captions for a Wide Range of Concepts Expressible in Natural Language”, Ramesh et al 2021
- “VQ-GAN: Taming Transformers for High-Resolution Image Synthesis”, Esser et al 2020
- “Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images”, Child 2020
- “NVAE: A Deep Hierarchical Variational Autoencoder”, Vahdat & Kautz 2020
- “Jukebox: We’re Introducing Jukebox, a Neural Net That Generates Music, including Rudimentary Singing, As Raw Audio in a Variety of Genres and Artist Styles. We’re Releasing the Model Weights and Code, along With a Tool to Explore the Generated Samples.”, Dhariwal et al 2020
- “Jukebox: A Generative Model for Music”, Dhariwal et al 2020
- “RL Agents Implicitly Learning Human Preferences”, Wichers 2020
- “Encoding Musical Style With Transformer Autoencoders”, Choi et al 2019
- “Generating Furry Face Art from Sketches Using a GAN”, Yu 2019
- “BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension”, Lewis et al 2019
- “Bayesian Parameter Estimation Using Conditional Variational Autoencoders for Gravitational-wave Astronomy”, Gabbard et al 2019
- “In-field Whole Plant Maize Architecture Characterized by Latent Space Phenotyping”, Gage et al 2019
- “Generating Diverse High-Fidelity Images With VQ-VAE-2”, Razavi et al 2019
- “Hierarchical Autoregressive Image Models With Auxiliary Decoders”, Fauw et al 2019
- “Anime Neural Net Graveyard”, Gwern 2019
- “How AI Training Scales”, McCandlish et al 2018
- “An Empirical Model of Large-Batch Training”, McCandlish et al 2018
- “Neural Probabilistic Motor Primitives for Humanoid Control”, Merel et al 2018
- “Piano Genie”, Donahue et al 2018
- “IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis”, Huang et al 2018
- “InfoNCE: Representation Learning With Contrastive Predictive Coding (CPC)”, Oord et al 2018
- “The Challenge of Realistic Music Generation: Modelling Raw Audio at Scale”, Dieleman et al 2018
- “Self-Net: Lifelong Learning via Continual Self-Modeling”, Camp et al 2018
- “GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training”, Akcay et al 2018
- “XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings”, Royer et al 2017
- “VQ-VAE: Neural Discrete Representation Learning”, Oord et al 2017
- “Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration”, Rahmatizadeh et al 2017
- “Β-VAE: Learning Basic Visual Concepts With a Constrained Variational Framework”, Higgins et al 2017
- “Neural Audio Synthesis of Musical Notes With WaveNet Autoencoders”, Engel et al 2017
- “Prediction and Control With Temporal Segment Models”, Mishra et al 2017
- “Discovering Objects and Their Relations from Entangled Scene Representations”, Raposo et al 2017
- “Categorical Reparameterization With Gumbel-Softmax”, Jang et al 2016
- “The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables”, Maddison et al 2016
- “Improving Sampling from Generative Autoencoders With Markov Chains”, Creswell et al 2016
- “Neural Photo Editing With Introspective Adversarial Networks”, Brock et al 2016
- “Early Visual Concept Learning With Unsupervised Deep Learning”, Higgins et al 2016
- “Improving Variational Inference With Inverse Autoregressive Flow”, Kingma et al 2016
- “How Far Can We Go without Convolution: Improving Fully-connected Networks”, Lin et al 2015
- “Semi-supervised Sequence Learning”, Dai & Le 2015
- “MADE: Masked Autoencoder for Distribution Estimation”, Germain et al 2015
- “Analyzing Noise in Autoencoders and Deep Networks”, Poole et al 2014
- “Stochastic Backpropagation and Approximate Inference in Deep Generative Models”, Rezende et al 2014
- “Auto-Encoding Variational Bayes”, Kingma & Welling 2013
- “A Connection Between Score Matching and Denoising Autoencoders”, Vincent 2011
- “Transformers As Variational Autoencoders”
- “Randomly Traversing the Manifold of Faces (2): Dataset: Labeled Faces in the Wild (LFW); Model: Variational Auto-Encoder (VAE) / Deep Latent Gaussian Model (DLGM).”
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“Finite Scalar Quantization (FSQ): VQ-VAE Made Simple”, Mentzer et al 2023
“Finding Neurons in a Haystack: Case Studies With Sparse Probing”, Gurnee et al 2023
“Finding Neurons in a Haystack: Case Studies with Sparse Probing”
“TANGO: Text-to-Audio Generation Using Instruction-Tuned LLM and Latent Diffusion Model”, Ghosal et al 2023
“TANGO: Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model”
“ACT: Learning Fine-Grained Bimanual Manipulation With Low-Cost Hardware”, Zhao et al 2023
“ACT: Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware”
“IRIS: Transformers Are Sample-Efficient World Models”, Micheli et al 2022
“Vector Quantized Image-to-Image Translation”, Chen et al 2022
“Draft-and-Revise: Effective Image Generation With Contextual RQ-Transformer”, Lee et al 2022
“Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer”
“UViM: A Unified Modeling Approach for Vision With Learned Guiding Codes”, Kolesnikov et al 2022
“UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes”
“Closing the Gap: Exact Maximum Likelihood Training of Generative Autoencoders Using Invertible Layers (AEF)”, Silvestri et al 2022
“AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars”, Hong et al 2022
“AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars”
“NaturalSpeech: End-to-End Text to Speech Synthesis With Human-Level Quality”, Tan et al 2022
“NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality”
“VQGAN-CLIP: Open Domain Image Generation and Editing With Natural Language Guidance”, Crowson et al 2022
“VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance”
“TATS: Long Video Generation With Time-Agnostic VQGAN and Time-Sensitive Transformer”, Ge et al 2022
“TATS: Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer”
“Diffusion Probabilistic Modeling for Video Generation”, Yang et al 2022
“Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values”, Humayun et al 2022
“Variational Autoencoders Without the Variation”, Daly et al 2022
“Vector-quantized Image Modeling With Improved VQGAN”, Yu et al 2022
“Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders”, Zheng et al 2022
“Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders”
“MLR: A Model of Working Memory for Latent Representations”, Hedayati et al 2022
“CM3: A Causal Masked Multimodal Model of the Internet”, Aghajanyan et al 2022
“Design Guidelines for Prompt Engineering Text-to-Image Generative Models”, Liu & Chilton 2022b
“Design Guidelines for Prompt Engineering Text-to-Image Generative Models”
“DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents”, Pandey et al 2022
“DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents”
“ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation”, Zhang et al 2021
“ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation”
“Discovering State Variables Hidden in Experimental Data”, Chen et al 2021
“High-Resolution Image Synthesis With Latent Diffusion Models”, Rombach et al 2021
“High-Resolution Image Synthesis with Latent Diffusion Models”
“VQ-DDM: Global Context With Discrete Diffusion in Vector Quantized Modelling for Image Generation”, Hu et al 2021
“VQ-DDM: Global Context with Discrete Diffusion in Vector Quantized Modelling for Image Generation”
“Vector Quantized Diffusion Model for Text-to-Image Synthesis”, Gu et al 2021
“Vector Quantized Diffusion Model for Text-to-Image Synthesis”
“L-Verse: Bidirectional Generation Between Image and Text”, Kim et al 2021
“Passive Non-Line-of-Sight Imaging Using Optimal Transport”, Geng et al 2021
“Unsupervised Deep Learning Identifies Semantic Disentanglement in Single Inferotemporal Face Patch Neurons”, Higgins et al 2021
“Telling Creative Stories Using Generative Visual Aids”, Ali & Parikh 2021
“Illiterate DALL·E Learns to Compose”, Singh et al 2021
“Score-based Generative Modeling in Latent Space”, Vahdat et al 2021
“Vector Quantized Models for Planning”, Ozair et al 2021
“NWT: Towards Natural Audio-to-video Generation With Representation Learning”, Mama et al 2021
“NWT: Towards natural audio-to-video generation with representation learning”
“VideoGPT: Video Generation Using VQ-VAE and Transformers”, Yan et al 2021
“Symbolic Music Generation With Diffusion Models”, Mittal et al 2021
“Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models”, Bond-Taylor et al 2021
“Greedy Hierarchical Variational Autoencoders (GHVAEs) for Large-Scale Video Prediction”, Wu et al 2021
“Greedy Hierarchical Variational Autoencoders (GHVAEs) for Large-Scale Video Prediction”
“CW-VAE: Clockwork Variational Autoencoders”, Saxena et al 2021
“Denoising Diffusion Implicit Models”, Song et al 2021
“DALL·E 1: Creating Images from Text: We’ve Trained a Neural Network Called DALL·E That Creates Images from Text Captions for a Wide Range of Concepts Expressible in Natural Language”, Ramesh et al 2021
“VQ-GAN: Taming Transformers for High-Resolution Image Synthesis”, Esser et al 2020
“VQ-GAN: Taming Transformers for High-Resolution Image Synthesis”
“Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images”, Child 2020
“Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images”
“NVAE: A Deep Hierarchical Variational Autoencoder”, Vahdat & Kautz 2020
“Jukebox: We’re Introducing Jukebox, a Neural Net That Generates Music, including Rudimentary Singing, As Raw Audio in a Variety of Genres and Artist Styles. We’re Releasing the Model Weights and Code, along With a Tool to Explore the Generated Samples.”, Dhariwal et al 2020
“Jukebox: A Generative Model for Music”, Dhariwal et al 2020
“RL Agents Implicitly Learning Human Preferences”, Wichers 2020
“Encoding Musical Style With Transformer Autoencoders”, Choi et al 2019
“Generating Furry Face Art from Sketches Using a GAN”, Yu 2019
“BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension”, Lewis et al 2019
“Bayesian Parameter Estimation Using Conditional Variational Autoencoders for Gravitational-wave Astronomy”, Gabbard et al 2019
“In-field Whole Plant Maize Architecture Characterized by Latent Space Phenotyping”, Gage et al 2019
“In-field whole plant maize architecture characterized by Latent Space Phenotyping”
“Generating Diverse High-Fidelity Images With VQ-VAE-2”, Razavi et al 2019
“Hierarchical Autoregressive Image Models With Auxiliary Decoders”, Fauw et al 2019
“Hierarchical Autoregressive Image Models with Auxiliary Decoders”
“Anime Neural Net Graveyard”, Gwern 2019
“How AI Training Scales”, McCandlish et al 2018
“An Empirical Model of Large-Batch Training”, McCandlish et al 2018
“Neural Probabilistic Motor Primitives for Humanoid Control”, Merel et al 2018
“Neural probabilistic motor primitives for humanoid control”
“Piano Genie”, Donahue et al 2018
“IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis”, Huang et al 2018
“IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis”
“InfoNCE: Representation Learning With Contrastive Predictive Coding (CPC)”, Oord et al 2018
“InfoNCE: Representation Learning with Contrastive Predictive Coding (CPC)”
“The Challenge of Realistic Music Generation: Modelling Raw Audio at Scale”, Dieleman et al 2018
“The challenge of realistic music generation: modelling raw audio at scale”
“Self-Net: Lifelong Learning via Continual Self-Modeling”, Camp et al 2018
“GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training”, Akcay et al 2018
“GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training”
“XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings”, Royer et al 2017
“XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings”
“VQ-VAE: Neural Discrete Representation Learning”, Oord et al 2017
“Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration”, Rahmatizadeh et al 2017
“Β-VAE: Learning Basic Visual Concepts With a Constrained Variational Framework”, Higgins et al 2017
“β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework”
“Neural Audio Synthesis of Musical Notes With WaveNet Autoencoders”, Engel et al 2017
“Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders”
“Prediction and Control With Temporal Segment Models”, Mishra et al 2017
“Discovering Objects and Their Relations from Entangled Scene Representations”, Raposo et al 2017
“Discovering objects and their relations from entangled scene representations”
“Categorical Reparameterization With Gumbel-Softmax”, Jang et al 2016
“The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables”, Maddison et al 2016
“The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables”
“Improving Sampling from Generative Autoencoders With Markov Chains”, Creswell et al 2016
“Improving Sampling from Generative Autoencoders with Markov Chains”
“Neural Photo Editing With Introspective Adversarial Networks”, Brock et al 2016
“Neural Photo Editing with Introspective Adversarial Networks”
“Early Visual Concept Learning With Unsupervised Deep Learning”, Higgins et al 2016
“Early Visual Concept Learning with Unsupervised Deep Learning”
“Improving Variational Inference With Inverse Autoregressive Flow”, Kingma et al 2016
“Improving Variational Inference with Inverse Autoregressive Flow”
“How Far Can We Go without Convolution: Improving Fully-connected Networks”, Lin et al 2015
“How far can we go without convolution: Improving fully-connected networks”
“Semi-supervised Sequence Learning”, Dai & Le 2015
“MADE: Masked Autoencoder for Distribution Estimation”, Germain et al 2015
“Analyzing Noise in Autoencoders and Deep Networks”, Poole et al 2014
“Stochastic Backpropagation and Approximate Inference in Deep Generative Models”, Rezende et al 2014
“Stochastic Backpropagation and Approximate Inference in Deep Generative Models”
“Auto-Encoding Variational Bayes”, Kingma & Welling 2013
“A Connection Between Score Matching and Denoising Autoencoders”, Vincent 2011
“A Connection Between Score Matching and Denoising Autoencoders”
“Transformers As Variational Autoencoders”
“Randomly Traversing the Manifold of Faces (2): Dataset: Labeled Faces in the Wild (LFW); Model: Variational Auto-Encoder (VAE) / Deep Latent Gaussian Model (DLGM).”
Wikipedia
Miscellaneous
Link Bibliography
-
https://arxiv.org/abs/2309.15505: “Finite Scalar Quantization (FSQ): VQ-VAE Made Simple”, Fabian Mentzer, David Minnen, Eirikur Agustsson, Michael Tschannen -
https://arxiv.org/abs/2304.13731: “TANGO: Text-to-Audio Generation Using Instruction-Tuned LLM and Latent Diffusion Model”, Deepanway Ghosal, Navonil Majumder, Ambuj Mehrish, Soujanya Poria -
https://arxiv.org/abs/2304.13705: “ACT: Learning Fine-Grained Bimanual Manipulation With Low-Cost Hardware”, Tony Z. Zhao, Vikash Kumar, Sergey Levine, Chelsea Finn -
https://arxiv.org/abs/2209.00588: “IRIS: Transformers Are Sample-Efficient World Models”, Vincent Micheli, Eloi Alonso, François Fleuret -
https://arxiv.org/abs/2205.08535: “AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars”, Fangzhou Hong, Mingyuan Zhang, Liang Pan, Zhongang Cai, Lei Yang, Ziwei Liu -
https://arxiv.org/abs/2205.04421#microsoft: “NaturalSpeech: End-to-End Text to Speech Synthesis With Human-Level Quality”, -
https://arxiv.org/abs/2204.03638#facebook: “TATS: Long Video Generation With Time-Agnostic VQGAN and Time-Sensitive Transformer”, Songwei Ge, Thomas Hayes, Harry Yang, Xi Yin, Guan Pang, David Jacobs, Jia-Bin Huang, Devi Parikh -
https://arxiv.org/abs/2203.01993: “Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values”, Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk -
https://arxiv.org/abs/2110.04627#google: “Vector-quantized Image Modeling With Improved VQGAN”, -
2022-liu-2.pdf: “Design Guidelines for Prompt Engineering Text-to-Image Generative Models”, Vivian Liu, Lydia B. Chilton -
https://arxiv.org/abs/2112.15283#baidu: “ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation”, Han Zhang, Weichong Yin, Yewei Fang, Lanxin Li, Boqiang Duan, Zhihua Wu, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang -
https://arxiv.org/abs/2112.10752: “High-Resolution Image Synthesis With Latent Diffusion Models”, Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer -
https://arxiv.org/abs/2111.11133: “L-Verse: Bidirectional Generation Between Image and Text”, -
https://arxiv.org/abs/2106.04615#deepmind: “Vector Quantized Models for Planning”, Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, Oriol Vinyals -
https://arxiv.org/abs/2104.10157: “VideoGPT: Video Generation Using VQ-VAE and Transformers”, Wilson Yan, Yunzhi Zhang, Pieter Abbeel, Aravind Srinivas -
https://openai.com/research/dall-e: “DALL·E 1: Creating Images from Text: We’ve Trained a Neural Network Called DALL·E That Creates Images from Text Captions for a Wide Range of Concepts Expressible in Natural Language”, -
https://arxiv.org/abs/2011.10650#openai: “Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images”, Rewon Child -
https://arxiv.org/abs/2007.03898#nvidia: “NVAE: A Deep Hierarchical Variational Autoencoder”, Arash Vahdat, Jan Kautz -
https://openai.com/research/jukebox: “Jukebox: We’re Introducing Jukebox, a Neural Net That Generates Music, including Rudimentary Singing, As Raw Audio in a Variety of Genres and Artist Styles. We’re Releasing the Model Weights and Code, along With a Tool to Explore the Generated Samples.”, Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever -
https://cdn.openai.com/papers/jukebox.pdf: “Jukebox: A Generative Model for Music”, Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever -
https://arxiv.org/abs/1910.13461#facebook: “BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension”, -
face-graveyard: “Anime Neural Net Graveyard”, Gwern -
https://openai.com/research/how-ai-training-scales: “How AI Training Scales”, Sam McCandlish, Jared Kaplan, Dario Amodei -
2011-vincent.pdf: “A Connection Between Score Matching and Denoising Autoencoders”, Pascal Vincent