“‘Compression’ Tag”,2019-09-07 (NN sparsity)⁠:

Bibliography for tag cs/algorithm/information/compression, most recent first: 5 related tags, 78 annotations, & 79 links (parent).

• See Also
• Gwern
  • “Research Ideas”, Gwern2017
  • “Umineko: The Hopium Of The Magics”, Gwern2018
  • “The sort –key Trick”, Gwern2014
  • “Against Copyright”, Gwern2008
• Links
  • “WebP: The WebPage Compression Format”, Sireneva2024
  • “Investigating Learning-Independent Abstract Reasoning in Artificial Neural Networks”, Barak & Loewenstein2024
  • “SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound”, Liu et al 2024
  • “Training LLMs over Neurally Compressed Text”, Lester et al 2024
  • “Infini-Gram: Scaling Unbounded n-Gram Language Models to a Trillion Tokens”, Liu et al 2024
  • “Language Modeling Is Compression”, Delétang et al 2023
  • “Bayesian Flow Networks”, Graves et al 2023
  • “Gzip versus Bag-Of-Words for Text Classification With k-NN”, Opitz2023
  • “High-Fidelity Audio Compression With Improved RVQGAN”, Kumar et al 2023
  • “White-Box Transformers via Sparse Rate Reduction”, Yu et al 2023
  • “How to Enumerate Trees from a Context-Free Grammar”, Piantadosi2023
  • “DIRAC: Neural Image Compression With a Diffusion-Based Decoder”, Goose et al 2023
  • “Less Is More: Parameter-Free Text Classification With Gzip”, Jiang et al 2022
  • “Low-Bitrate Redundancy Coding of Speech Using a Rate-Distortion-Optimized Variational Autoencoder”, Valin et al 2022
  • “RGB No More: Minimally-Decoded JPEG Vision Transformers”, Park & Johnson2022
  • “High Fidelity Neural Audio Compression”, Défossez et al 2022
  • “T2CI-GAN: Text to Compressed Image Generation Using Generative Adversarial Network”, Rajesh et al 2022
  • “DiffC: Lossy Compression With Gaussian Diffusion”, Theis et al 2022
  • “MuZero With Self-Competition for Rate Control in VP9 Video Compression”, Mandhane et al 2022
  • “A Deep Dive into an NSO Zero-Click IMessage Exploit: Remote Code Execution”, Beer & Groß 2021
  • “Palette: Image-To-Image Diffusion Models”, Saharia et al 2021
  • “Autoregressive Diffusion Models”, Hoogeboom et al 2021
  • “Variational Diffusion Models”, Kingma et al 2021
  • “Rip Van Winkle’s Razor, a Simple New Estimate for Adaptive Data Analysis”, Arora & Zhang2021
  • “Why Are Tar.xz Files 15× Smaller When Using Python’s Tar Library Compared to MacOS Tar?”, Lindestøkke2021
  • “Generating Images With Sparse Representations”, Nash et al 2021
  • “Rip Van Winkle’s Razor: A Simple Estimate of Overfit to Test Data”, Arora & Zhang2021
  • “Generative Speech Coding With Predictive Variance Regularization”, Kleijn et al 2021
  • “1-Bit Adam: Communication Efficient Large-Scale Training With Adam’s Convergence Speed”, Tang et al 2021
  • “Scaling Laws for Autoregressive Generative Modeling”, Henighan et al 2020
  • “Not-So-BigGAN: Generating High-Fidelity Images on Small Compute With Wavelet-Based Super-Resolution”, Han et al 2020
  • “Zip Files: History, Explanation and Implementation”, Wennborg2020
  • “The 1-Bit Instrument: The Fundamentals of 1-Bit Synthesis, Their Implementational Implications, and Instrumental Possibilities”, Troise2020
  • “People Prefer Simpler Content When There Are More Choices: A Time Series Analysis of Lyrical Complexity in Six Decades of American Popular Music”, Varnum et al 2019
  • “Bit-Swap: Recursive Bits-Back Coding for Lossless Compression With Hierarchical Latent Variables”, Kingma et al 2019
  • “Unraveling the JPEG: JPEG Images Are Everywhere in Our Digital Lives, but behind the Veil of Familiarity Lie Algorithms That Remove Details That Are Imperceptible to the Human Eye. This Produces the Highest Visual Quality With the Smallest File Size—But What Does That Look Like? Let’s See What Our Eyes Can’t See!”, Shehata2019
  • “Practical Lossless Compression With Latent Variables Using Bits Back Coding”, Townsend et al 2019
  • “SignSGD: Compressed Optimization for Non-Convex Problems”, Bernstein et al 2018
  • “Lempel-Ziv: a ‘1-Bit Catastrophe’ but Not a Tragedy”, Lagarde & Perifel2017
  • “BBhash: Fast and Scalable Minimal Perfect Hashing for Massive Key Sets”, Limasset et al 2017
  • “Full Resolution Image Compression With Recurrent Neural Networks”, Toderici et al 2016
  • “On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models”, Schmidhuber2015
  • “Compress and Control”, Veness et al 2014
  • “A Really Simple Approximation of Smallest Grammar”, Jeż 2014
  • “One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling”, Chelba et al 2013
  • “The Thermodynamics of Prediction”, Still et al 2012
  • “Notes on a New Philosophy of Empirical Science”, Burfoot2011
  • “Universal Entropy of Word Ordering Across Linguistic Families”, Montemurro & Zanette2011
  • “Google-Wide Profiling: A Continuous Profiling Infrastructure for Data Centers”, Ren et al 2010
  • “New Strategy of Lossy Text Compression”, Al-Dubaee & Ahmad2010
  • “A Monte Carlo AIXI Approximation”, Veness et al 2009
  • “A Machine Learning Perspective on Predictive Coding With PAQ8 and New Applications”, Knoll2009
  • “The Bayesian Brain: the Role of Uncertainty in Neural Coding and Computation”, Knill & Pouget2004
  • “Clustering by Compression”, Cilibrasi & Vitanyi2003
  • “Data Compression and Entropy Estimates by Non-Sequential Recursive Pair Substitution”, Grassberger2002
  • “Compression and Information Leakage of Plaintext”, Kelsey2002
  • “Estimating and Comparing Entropy across Written Natural Languages Using PPM Compression”, Behr et al 2002
  • “Language Trees and Zipping”, Benedetto et al 2001
  • “Redundancy Reduction Revisited”, Barlow2001
  • “Fast Text Compression With Neural Networks”, Mahoney2000
  • “Text Compression As a Test for Artificial Intelligence”, Mahoney1999
  • “An Information-Theoretic Model for Steganography”, Cachin1998
  • “The Art of Computer Programming, Volume 3: Sorting & Searching § Chapter 6, Searching: Hashing: History”, Knuth1998
  • “THE ENTROPY OF ENGLISH USING PPM-BASED MODELS—Data Compression Conference, 1996. DCC ’96. Proceedings”
  • “Measuring the Complexity of Writing Systems”, Bosch et al 1994
  • “Entropy of Natural Languages: Theory and Experiment”, Levitin & Reingold1994
  • “Possible Principles Underlying the Transformations of Sensory Messages”, Barlow1961
  • “Prediction and Entropy of Printed English”, Shannon1951
  • “About the Test Data”
  • “Timm S. Mueller”
  • “Codec2: a Whole Podcast on a Floppy Disk”
  • “Finding Near-Duplicates With Jaccard Similarity and MinHash”
  • “How We Shrank Our Trip Planner till It Didn’t Need Data.”
  • “Statistical Inference Through Data Compression”
  • “ChessPositionRanking/img/2389704906374985477664262349386869232706664089.png at Main · Tromp/ChessPositionRanking”
  • “Relation of Word Order and Compression Ratio and Degree of Structure”
  • “King James Programming”
  • “That Alien Message”, Yudkowsky2024
  • Sort By Magic
    • data-compression
    • compression
    • compression-strategies
    • speech-coding
    • diffusion-models
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