“‘Information Theory’ Tag”,2019-11-12 (Internet archiving)⁠:

Bibliography for tag cs/algorithm/information, most recent first: 9 related tags, 60 annotations, & 6 links (parent).

• See Also
• Gwern
  • “How Complex Are Individual Differences?”, Gwern2010
  • “Death Note: L, Anonymity & Eluding Entropy”, Gwern2011
• Links
  • “Channel Capacity of a Telegraph”
  • “The Unbearable Slowness of Being”, Zheng & Meister2024
  • “An Information-Theoretic Analysis of In-Context Learning”, Jeon et al 2024
  • “Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities”, Kirsch & Gal2023
  • “Market Microstructure and Informational Efficiency: The Role of Intermediation”, Guthmann & Albrecht2023
  • “Electrochemical Potential Enables Dormant Spores to Integrate Environmental Signals”, Kikuchi et al 2022
  • “What Do We Maximize in Self-Supervised Learning?”, Shwartz-Ziv et al 2022
  • “Macaques Preferentially Attend to Intermediately Surprising Information”, Wu et al 2022
  • “The Cost of Information Acquisition by Natural Selection”, McGee et al 2022
  • “Efficiently Irrational: Deciphering the Riddle of Human Choice”, Glimcher2022
  • “First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization”, Reddy et al 2022
  • “The InterModel Vigorish (IMV): A Flexible and Portable Approach for Quantifying Predictive Accuracy With Binary Outcomes”, Domingue et al 2022
  • “Intelligence and Unambitiousness Using Algorithmic Information Theory”, Cohen et al 2021
  • “Entropy Trade-Offs in Artistic Design: A Case Study of Tamil kolam”, Tran et al 2021
  • “Computation in the Human Cerebral Cortex Uses Less Than 0.2 Watts yet This Great Expense Is Optimal When considering Communication Costs”, Levy & Calvert2020
  • “On the Measure of Intelligence”, Chollet2019
  • “Neural Networks Are a Priori Biased towards Boolean Functions With Low Entropy”, Mingard et al 2019
  • “A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment”, Leibfried et al 2019
  • “Common Neural Code for Reward and Information Value”, Kobayashi & Hsu2019
  • “Humans Store about 1.5 Megabytes of Information during Language Acquisition”, Mollica & Piantadosi2019
  • “Predictability and Uncertainty in the Pleasure of Music: A Reward for Learning?”, Gold et al 2019
  • “Accounting Theory As a Bayesian Discipline”, Johnstone2018
  • “Measurement Invariance Explains the Universal Law of Generalization for Psychological Perception”, Frank2018
  • “Information Flow Reveals Prediction Limits in Online Social Activity”, Bagrow et al 2017
  • “Predicting Green: Really Radical (plant) Predictive Processing”, Calvo & Friston2017
  • “Exploration and Exploitation of Victorian Science in Darwin’s Reading Notebooks”, Murdock et al 2016
  • “Multiplicative LSTM for Sequence Modeling”, Krause et al 2016
  • “Capacity-Approaching DNA Storage”, Erlich & Zielinski2016
  • “Energy-Efficient Algorithms”, Demaine et al 2016
  • Advances in Physarum Machines: Sensing and Computing With Slime Mould, Adamatzky2016
  • “Ed Fredkin and the Physics of Information: An Inside Story of an Outsider Scientist”, Hagar2016
  • “Nanoconnectomic Upper Bound on the Variability of Synaptic Plasticity”, Bartol et al 2015
  • “Calculating Kolmogorov Complexity from the Output Frequency Distributions of Small Turing Machines”, Soler-Toscano et al 2014
  • “The Physics of Bacterial Decision Making”, Ben-Jacob et al 2014
  • “Understanding Predictive Information Criteria for Bayesian Models”, Gelman2013
  • “A Widely Applicable Bayesian Information Criterion”, Watanabe2012
  • “A Cross-Language Perspective On Speech Information Rate”, Pellegrino et al 2011
  • “Information Geometry and Evolutionary Game Theory”, Harper2009
  • “The Epic Story of Maximum Likelihood”, Stigler2007
  • “A Methodology for Studying Various Interpretations of the N,N-Dimethyltryptamine-Induced Alternate Reality”, Rodriguez2006
  • “What Color Are Your Bits?”, Skala2004
  • “Ultimate Physical Limits to Computation”, Lloyd1999
  • “The Physical Limits of Communication”, Lachmann et al 1999
  • “Analog Versus Digital: Extrapolating from Electronics to Neurobiology”, Sarpeshkar1998
  • “Quantum Effects in Algorithms”, Jozsa1998
  • “Information Theory and an Extension of the Maximum Likelihood Principle”, Akaike1998
  • “Information and the Accuracy Attainable in the Estimation of Statistical Parameters”, Rao1992
  • “The Total Evidence Theorem for Probability Kinematics”, Graves1989
  • “Profile of Claude Shannon”, Liversidge & Shannon1987
  • “How Much Do People Remember? Some Estimates of the Quantity of Learned Information in Long-Term Memory”, Landauer1986
  • “The Fundamental Physical Limits of Computation”, Bennett & Landauer1985
  • “Randomness Conservation Inequalities; Information and Independence in Mathematical Theories”, Levin1984
  • “A Convergent Gambling Estimate Of The Entropy Of English”, Cover & King1978
  • “Quantum Effects in Communications Systems”, Gordon1962
  • “Coding Theorems for a Discrete Source With a Fidelity Criterion”, Shannon1959
  • “Chance Remarks”, Pierce1949
  • “Chance Remarks § Shannon’s n-Gram Generations”, Pierce1949 (page 4)
  • “Gyrophone: Recognizing Speech From Gyroscope Signals”, Michalevsky2024
  • “How Many Persons Can There Be: Brain Reconstruction and Big Numbers”
  • “Harder Drive: Hard Drives We Didn’t Want or Need”, tom7 2024
  • Sort By Magic
    • entropy-bias
    • quantum-communication
    • memory-estimation
    • information-theory
  • Wikipedia
• Miscellaneous
• Bibliography