“‘Autoencoder NN’ Tag”,2019-09-26 (CNN, compression, model-based RL)⁠:

Bibliography for tag ai/nn/vae, most recent first: 4 related tags, 108 annotations, & 8 links (parent).

• See Also
• Gwern
  • “Anime Neural Net Graveyard”, Gwern2019
• Links
  • “Scaling the Codebook Size of VQGAN to 100,000 With a Utilization Rate of 99%”, Zhu et al 2024
  • “Visual Autoregressive Modeling (VAR): Scalable Image Generation via Next-Scale Prediction”, Tian et al 2024
  • “Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data”, Gerstgrasser et al 2024
  • “Neural Network Parameter Diffusion”, Wang et al 2024
  • “Attention versus Contrastive Learning of Tabular Data—A Data-Centric Benchmarking”, Rabbani et al 2024
  • “Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet”
  • “GIVT: Generative Infinite-Vocabulary Transformers”, Tschannen et al 2023
  • “Sequential Modeling Enables Scalable Learning for Large Vision Models”, Bai et al 2023
  • “Finite Scalar Quantization (FSQ): VQ-VAE Made Simple”, Mentzer et al 2023
  • “DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation”, Duan 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
  • “Bridging Discrete and Backpropagation: Straight-Through and Beyond”, Liu et al 2023
  • “Low-Bitrate Redundancy Coding of Speech Using a Rate-Distortion-Optimized Variational Autoencoder”, Valin et al 2022
  • “IRIS: Transformers Are Sample-Efficient World Models”, Micheli et al 2022
  • “Understanding Diffusion Models: A Unified Perspective”, Luo2022
  • “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
  • “AdaVAE: Exploring Adaptive GPT-2s in Variational Autoencoders for Language Modeling”, Tu 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
  • “Vector-Quantized Image Modeling With Improved VQGAN”, Yu et al 2022
  • “Variational Autoencoders Without the Variation”, Daly et al 2022
  • “Truncated Diffusion Probabilistic Models and Diffusion-Based Adversarial Autoencoders”, 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 & Chilton2022b
  • “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
  • “High-Resolution Image Synthesis With Latent Diffusion Models”, Rombach et al 2021
  • “Discovering State Variables Hidden in Experimental Data”, Chen et al 2021
  • “VQ-DDM: Global Context With Discrete Diffusion in Vector Quantized Modeling for Image Generation”, Hu et al 2021
  • “Vector Quantized Diffusion Model for Text-To-Image Synthesis”, Gu et al 2021
  • “Passive Non-Line-Of-Sight Imaging Using Optimal Transport”, Geng et al 2021
  • “L-Verse: Bidirectional Generation Between Image and Text”, Kim 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 & Parikh2021
  • “Illiterate DALL·E Learns to Compose”, Singh et al 2021
  • “MeLT: Message-Level Transformer With Masked Document Representations As Pre-Training for Stance Detection”, Matero et al 2021
  • “Score-Based Generative Modeling in Latent Space”, Vahdat et al 2021
  • “NWT: Towards Natural Audio-To-Video Generation With Representation Learning”, Mama et al 2021
  • “Vector Quantized Models for Planning”, Ozair et al 2021
  • “VideoGPT: Video Generation Using VQ-VAE and Transformers”, Yan et al 2021
  • “TSDAE: Using Transformer-Based Sequential Denoising Autoencoder for Unsupervised Sentence Embedding Learning”, Wang et al 2021
  • “Symbolic Music Generation With Diffusion Models”, Mittal et al 2021
  • “Deep Generative Modeling: 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
  • “Multimodal Dynamics Modeling for Off-Road Autonomous Vehicles”, Tremblay et al 2020
  • “Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images”, Child2020
  • “NVAE: A Deep Hierarchical Variational Autoencoder”, Vahdat & Kautz2020
  • “Jukebox: A Generative Model for Music”, Dhariwal et al 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
  • “RL Agents Implicitly Learning Human Preferences”, Wichers2020
  • “Encoding Musical Style With Transformer Autoencoders”, Choi et al 2019
  • “Generating Furry Face Art from Sketches Using a GAN”, Yu2019
  • “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
  • “Bit-Swap: Recursive Bits-Back Coding for Lossless Compression With Hierarchical Latent Variables”, Kingma et al 2019
  • “Hierarchical Autoregressive Image Models With Auxiliary Decoders”, Fauw et al 2019
  • “Practical Lossless Compression With Latent Variables Using Bits Back Coding”, Townsend et al 2019
  • “An Empirical Model of Large-Batch Training”, McCandlish et al 2018
  • “How AI Training Scales”, 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: Modeling 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
  • “Language As a Latent Variable: Discrete Generative Models for Sentence Compression”, Miao & Blunsom2016
  • “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 & Le2015
  • “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 & Welling2013
  • “Building High-Level Features Using Large Scale Unsupervised Learning”, Le et al 2011
  • “A Connection Between Score Matching and Denoising Autoencoders”, Vincent2011
  • “Reducing the Dimensionality of Data With Neural Networks”, Hinton & Salakhutdinov2006
  • “Generating Large Images from Latent Vectors”, Ha2024
  • “Transformers As Variational Autoencoders”
  • “Randomly Traversing the Manifold of Faces (2): Dataset: Labeled Faces in the Wild (LFW); Model: Variational Autoencoder (VAE) / Deep Latent Gaussian Model (DLGM).”
  • Sort By Magic
    • anomaly-detection
    • contrastive-learning
    • compression
    • generative-models
  • Wikipedia
• Miscellaneous
• Bibliography