The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A formal theory of inductive inference. Part II
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“The ability to generalise is closely related to compression [14,15], with more compressed representations yielding better accuracy [13] for the same reason that there is only one straight line interpolating any two points, but infinitely many polynomials of higher degree.”
From Compression, The Fermi Paradox and Artificial Super-Intelligence (2022) · cites this paper as [15]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
On computable numbers, with an application to the entscheidungsproblem
Turing, A. M. (1937) · Proceedings of the London Mathematical Society · doi:10.1112/plms/s2-42.1.230 · Cited by 8,283
Banks, D. L., Ríos, J., & Insua, D. R. (2015) · Chapman & Hall/CRC eBooks · doi:10.1201/b18653-3 · Cited by 3,649
Three models for the description of language
Chomsky, N. (1956) · IEEE Transactions on Information Theory · doi:10.1109/tit.1956.1056813 · Cited by 2,984
Keynes, J. M. (1973) · Palgrave Macmillan UK eBooks · doi:10.1007/978-1-349-00843-8 · Cited by 2,576
Logical Foundations of Probability
Savage, L. J., & Carnap, R. (1952) · Econometrica · doi:10.2307/1907654 · Cited by 2,042
Computer Languages for Symbol Manipulation
Green, B. F. (1961) · IRE Transactions on Electronic Computers · doi:10.1109/tec.1961.5219281 · Cited by 11
Cited by
Sorted by citations
Items 601–700 of 1,001
Scouting context-sensitive components
Pfaffmann, J. O., & Zauner, K. (2002) · NASA/DoD Conference on Evolvable Hardware · doi:10.1109/eh.2001.937942 · Cited by 18
Complexity of Automata, Brains, and Behavior
Bremermann, H. J. (1974) · Lecture notes in biomathematics · doi:10.1007/978-3-642-80885-2_17 · Cited by 18
On the inference of optimal descriptions
Daley, R. (1977) · Theoretical Computer Science · doi:10.1016/0304-3975(77)90015-9 · Cited by 18
AI Evaluation: past, present and futurePreprint
Hernández‐Orallo, J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.6908 · Cited by 18
(Non-)Equivalence of Universal Priors
Wood, I. B., Sunehag, P., & Hütter, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-44958-1_33 · Cited by 17
Recovery From Linear Measurements With Complexity-Matching Universal Signal Estimation
Zhu, J., Baron, D., & Duarte, M. F. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2393845 · Cited by 17
Beyond No Free Lunch: Realistic algorithms for arbitrary problem classes
Marshall, J. A. R., & Hinton, T. G. (2010) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2010.5586389 · Cited by 17
Theories of Artificial Intelligence—Meta-Theoretical considerations
Wang, P. (2012) · Atlantis thinking machines · doi:10.2991/978-94-91216-62-6_16 · Cited by 17
On the computability of Solomonoff induction and AIXI
Leike, J., & Hütter, M. (2017) · Theoretical Computer Science · doi:10.1016/j.tcs.2017.11.020 · Cited by 17
Küppers, B. (2013) · Springer eBooks · doi:10.1007/978-3-642-34997-3_4 · Cited by 17
Is There an Elegant Universal Theory of Prediction?
Legg, S. (2006) · Lecture notes in computer science · doi:10.1007/11894841_23 · Cited by 17
On The Logical Foundations of Probability Theory
Shiryayev, A. N. (1992) · Springer eBooks · doi:10.1007/978-94-011-2260-3_53 · Cited by 17
A Subjective Measure of Complexity
Fioretti, G. (1999) · Advances in Complex Systems · doi:10.1142/s0219525999000187 · Cited by 17
Generalized kolmogorov complexity and other dual complexity measures
Burgin, M. (1991) · Cybernetics and Systems Analysis · doi:10.1007/bf01068189 · Cited by 17
Formal definitions of information and knowledge and their role in growth through structural change
Hilbert, M. (2016) · Structural Change and Economic Dynamics · doi:10.1016/j.strueco.2016.03.004 · Cited by 18
A Cognitive Approach to Organizational Complexity
Fioretti, G., & Visser, B. (2006) · Springer eBooks · doi:10.1007/0-387-28898-8_35 · Cited by 17
Easily adaptable complexity measure for finite time series
Ke, D., & Qin-ye, T. (2008) · Physical Review E · doi:10.1103/physreve.77.066215 · Cited by 17
Sommerer, C., & Mignonneau, L. (2002) · Leonardo · doi:10.1162/00240940252940531 · Cited by 17
The Kolmogorov complexity of infinite words
Ludwig Staiger, B. (2007) · Theoretical Computer Science · doi:10.1016/j.tcs.2007.04.013 · Cited by 17
Minimal representation multisensor fusion using differential evolution
Joshi, R., & Sanderson, A. C. (2002) · IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) · doi:10.1109/cira.1997.613868 · Cited by 17
Downey, R. G., Hirschfeldt, D. R., & Laforte, G. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44683-4_28 · Cited by 17
Algorithmic information and simplicity in statistical physics
Schack, R. (1997) · International Journal of Theoretical Physics · doi:10.1007/bf02435782 · Cited by 17
Sequence Complexity and Composition
Konopka, A. K. (2005) · Wiley eBooks · doi:10.1038/npg.els.0005260 · Cited by 17
Convergence of Discrete MDL for Sequential Prediction
Poland, J., & Hütter, M. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27819-1_21 · Cited by 17
What they do when in doubt: a study of inductive biases in seq2seq learnersPreprint
Kharitonov, E., & Chaabouni, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.14953 · Cited by 19
Visual Representation Learning Does Not Generalize Strongly Within the Same DomainPreprint
Schott, L., von Kügelgen, J., Träuble, F., Gehler, P., Russell, C., Bethge, M., Schölkopf, B., Locatello, F., & Brendel, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.08221 · Cited by 41
On the origin of synthetic life: attribution of output to a particular algorithm
Yampolskiy, R. V. (2016) · Physica Scripta · doi:10.1088/0031-8949/92/1/013002 · Cited by 16
A descriptive grammar of morphosyntactic constructions in Ugandan Sign Language (UgSL)
Lutalo-Kiingi, S. (2016) · Sign Language & Linguistics · doi:10.1075/sll.19.1.05lut · Cited by 16
Information: Problems, Paradoxes, and Solutions
Burgin, M. (1970) · tripleC Communication Capitalism & Critique Open Access Journal for a Global Sustainable Information Society · doi:10.31269/triplec.v1i1.5 · Cited by 16
From ADP to the brain: Foundations, roadmap, challenges and research priorities
Werbos, P. J. (2014) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2014.6889359 · Cited by 16
On Potential Cognitive Abilities in the Machine Kingdom
Hernández‐Orallo, J., & Dowe, D. L. (2013) · Minds and Machines · doi:10.1007/s11023-012-9299-6 · Cited by 16
Algorithmicity and programmability in natural computing with the Game of Life asin silicocase study
Zenil, H. (2014) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2014.940686 · Cited by 16
How crystals that sense and respond to their environments could evolve
Schulman, R., & Winfree, E. (2007) · Natural Computing · doi:10.1007/s11047-007-9046-8 · Cited by 16
On semimeasures predicting Martin-Löf random sequences
Hütter, M., & Muchnik, A. (2007) · Theoretical Computer Science · doi:10.1016/j.tcs.2007.03.040 · Cited by 16
Putnam’s Diagonal Argument and the Impossibility of a Universal Learning Machine
Sterkenburg, T. F. (2018) · Erkenntnis · doi:10.1007/s10670-018-9975-x · Cited by 16
Bayesian validation of grammar productions for the language of thought
Romano, S., Salles, A., Amalric, M., Dehaene, S., Sigman, M., & Figueira, S. D. (2018) · PLoS ONE · doi:10.1371/journal.pone.0200420 · Cited by 16
Azmi, E., Ehret, U., Weijs, S. V., Ruddell, B. L., & Perdigão, R. A. P. (2021) · Hydrology and earth system sciences · doi:10.5194/hess-25-1103-2021 · Cited by 16
Automatic analysis of artistic paintings using information-based measures
Silva, J. M., Pratas, D., Antunes, R., Matos, S., & Pinho, A. J. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107864 · Cited by 16
Is SGD a Bayesian sampler? Well, almostPreprint
Mingard, C., Valle-Pérez, G., Skalse, J., & Louis, A. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15191 · Cited by 23
Naoki, M., McKay, B., Xuan, N., Daryl, E., & Saori, T. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02481-8_24 · Cited by 16
On Sequence Prediction for Arbitrary Measures
Ryabko, D., & Hütter, M. (2007) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit.2007.4557570 · Cited by 16
Software Documents: Comparison and Measurement.
Arbuckle, T., Balaban, A., Peters, D. K., & Lawford, M. S. (2007) · Cited by 16
Computing as compression: An overview of the SP theory and system
Wolff, J. G. (1995) · New Generation Computing · doi:10.1007/bf03038313 · Cited by 16
On the Existence and Convergence of Computable Universal Priors
Hütter, M. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39624-6_24 · Cited by 16
'Computing' as Information Compression by Multiple Alignment, Unification and Search
Wolff, J. G. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.3217/jucs-005-11-0777 · Cited by 16
Lectures on Statistical Modeling Theory
Rissanen, J. (2005) · Cited by 16
Smoothing a probablistic lexicon via syntactic transformations
Eisner, J. M., & Marcus, M. P. (2001) · Scholarly Commons (University of Pennsylvania) · Cited by 16
Measuring complexity with zippersPreprint
Baronchelli, A., Caglioti, E., & Loreto, V. (2006) · Cited by 16
Model inference and pattern discovery by minimal representation method
Segen, J., & Sanderson, A. C. (2018) · KiltHub Repository · doi:10.1184/r1/6555587 · Cited by 16
Emergence and Fundamentality in a Pancomputationalist Universe
Pexton, M. (2015) · Minds and Machines · doi:10.1007/s11023-015-9383-9 · Cited by 15
Intuition, intelligence, data compression
Kipper, J. (2019) · Synthese · doi:10.1007/s11229-019-02118-8 · Cited by 16
Turing Tests with Turing Machines
Hernández‐Orallo, J., Insa-Cabrera, J., Dowe, D. L., & Hibbard, B. (2018) · EPiC series in computing · doi:10.29007/9n7d · Cited by 15
Bennett, M. T., & Maruyama, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-93758-4_6 · Cited by 15
Zenil, H., Marshall, J. A. R., & Tegner, J. N. (2023) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.956074 · Cited by 15
The Information-Theoretic and Algorithmic Approach to Human, Animal, and Artificial Cognition
Gauvrit, N., Zenil, H., & Tegner, J. N. (2017) · Studies in applied philosophy, epistemology and rational ethics · doi:10.1007/978-3-319-43784-2_7 · Cited by 15
Simplicity and likelihood: An axiomatic approach
Gilboa, I., & Schmeidler, D. (2010) · Journal of Economic Theory · doi:10.1016/j.jet.2010.03.010 · Cited by 15
On the role of simplicity in science
Scorzato, L. (2012) · Synthese · doi:10.1007/s11229-012-0101-3 · Cited by 15
Schmidhuber, J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-75225-7_6 · Cited by 15
Predicting non-stationary processes
Ryabko, D., & Hütter, M. (2007) · Applied Mathematics Letters · doi:10.1016/j.aml.2007.04.004 · Cited by 15
Artificial Curiosity for Autonomous Space Exploration
Graziano, V., Glasmachers, T., Schaul, T., Pape, L., Cuccu, G., Leitner, J., & Schmidhuber, J. (2011) · QUT ePrints (Queensland University of Technology) · doi:10.26180/19959179 · Cited by 15
A Physical Approach to the Construction of Cognition and to Cognitive Evolution
Diettrich, O. (2001) · Foundations of Science · doi:10.1023/a:1011610921631 · Cited by 15
On the apparent convergence of relative frequency and its implications
Fine, T. L. (1970) · IEEE Transactions on Information Theory · doi:10.1109/tit.1970.1054453 · Cited by 15
Minimal Representation Multi-Sensor Fusion Using Differential Evolution
Joshi, R., & Sanderson, A. C. (2006) · Springer eBooks · doi:10.1007/3-540-31306-0_10 · Cited by 15
ON SYSTEM PROPERTIES AND SYSTEMHOOD
Shenhar, A. J. (1990) · International Journal of General Systems · doi:10.1080/03081079008935136 · Cited by 15
Probabilistic automata as a model for epigenesis of cellular networks
Milgram, M., & Atlan, H. (1983) · Journal of Theoretical Biology · doi:10.1016/0022-5193(83)90281-3 · Cited by 15
An information-theoretic approach to time bounds for on-line computation (preliminary version)
Paul, W. J., Seiferas, J., & Šimon, J. (1980) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/800141.804685 · Cited by 15
Statistical and Information-Theoretic Methods for Data-Analysis
Roos, T. (2007) · Työväentutkimus Vuosikirja · Cited by 15
Universal Learning of Repeated Matrix GamesPreprint
Poland, J., & Hütter, M. (2005) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0508073 · Cited by 15
General Loss Bounds for Universal Sequence PredictionPreprint
Hütter, M. (2001) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0101019 · Cited by 15
On the irreducibility of consciousness and its relevance to free will
Tononi, G. (2013) · Is Science Compatible with Free Will? · doi:10.1007/978-1-4614-5212-6_11 · Cited by 14
Formalization of Evidence: A Comparative Study
Wang, P. (2009) · Journal of Artificial General Intelligence · doi:10.2478/v10229-011-0003-7 · Cited by 14
Text Classification Using Compression-Based Dissimilarity Measures
Coutinho, D., & Figueiredo, M. A. T. (2015) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001415530043 · Cited by 14
On the Possibility of Basing Cryptography on $$\mathsf{EXP}\ne \mathsf{BPP}$$
Liu, Y., & Pass, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84242-0_2 · Cited by 14
Turing machines and causal mechanisms in cognitive science
Lappi, O., & Rusanen, A. (2011) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199574131.003.0011 · Cited by 14
Structural coding versus free-energy predictive coding
van der Helm, P. A. (2015) · Psychonomic Bulletin & Review · doi:10.3758/s13423-015-0938-9 · Cited by 14
Have I seen you before? Principles of Bayesian predictive classification revisited
Corander, J., Cui, Y., Koski, T., & Sirén, J. (2011) · Statistics and Computing · doi:10.1007/s11222-011-9291-7 · Cited by 14
Edelman, S. (2017) · Language Sciences · doi:10.1016/j.langsci.2017.04.003 · Cited by 14
(2020) · Open Agenda Publishing eBooks · doi:10.2307/j.ctv22jnmh8.8 · Cited by 14
Gödel Machines: Towards a Technical Justification of Consciousness
Schmidhuber, J. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-32274-0_1 · Cited by 14
Sequential Decision Making Based on Direct Search
Schmidhuber, J. (2000) · Lecture notes in computer science · doi:10.1007/3-540-44565-x_10 · Cited by 14
Universal Convergence of Semimeasures on Individual Random Sequences
Hütter, M., & Muchnik, A. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30215-5_19 · Cited by 14
Complexity and Chaos - State-of-the-Art; Formulations and Measures of Complexity
Couture, M. (2007) · Defense Technical Information Center (DTIC) · Cited by 14
Measuring complexity with zippers
Baronchelli, A., Caglioti, E., & Loreto, V. (2005) · European Journal of Physics · doi:10.1088/0143-0807/26/5/s08 · Cited by 14
Osherson, D. N., de Jongh, D. H. J., Martin, É., & Weinstein, S. A. (1997) · Elsevier eBooks · doi:10.1016/b978-044481714-3/50017-5 · Cited by 14
Statistical signal processing using a class of iterative estimation algorithms
Feder, M. (1987) · NASA STI/Recon Technical Report N · Cited by 14
Predictive Minimum Description Length Criterion for Time Series Modeling with Neural Networks
Lehtokangas, M. I., Saarinen, J. P. P., Huuhtanen, P., & Kaski, K. K. (1996) · Neural Computation · doi:10.1162/neco.1996.8.3.583 · Cited by 14
Shape matching from grasp using a minimal representation size criterion
Joshi, R., & Sanderson, A. C. (2002) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.1993.292020 · Cited by 14
Computable Approximations of Reals: An Information-Theoretic Analysis
Calude, C. S., & Hertling, P. H. (1998) · Fundamenta Informaticae · doi:10.3233/fi-1998-33201 · Cited by 14
Gene splicing: Spatial separation of overlapping messages
Trifonov, E. N. (1993) · Computers & Chemistry · doi:10.1016/0097-8485(93)80026-a · Cited by 14
Quantum Kolmogorov Complexity and the Quantum Turing MachinePreprint
Müller, M. P. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.0712.4377 · Cited by 14
Asymptotic non-learnability of universal agents with computable horizon functions
Orseau, L. (2012) · Theoretical Computer Science · doi:10.1016/j.tcs.2012.10.014 · Cited by 13
Comparison of Compression-Based Measures with Application to the Evolution of Primate Genomes
Pratas, D., Silva, R. M., & Pinho, A. J. (2018) · Entropy · doi:10.3390/e20060393 · Cited by 13
The teaching size: computable teachers and learners for universal languages
Telle, J. A., Hernández‐Orallo, J., & Ferri, C. (2019) · Machine Learning · doi:10.1007/s10994-019-05821-2 · Cited by 13
Maximizing Fun by Creating Data with Easily Reducible Subjective Complexity
Schmidhuber, J. (2012) · Springer eBooks · doi:10.1007/978-3-642-32375-1_5 · Cited by 14
Deflating the deflationary view of information
Lombardi, O., Fortín, S., & López, C. (2015) · European Journal for Philosophy of Science · doi:10.1007/s13194-015-0128-7 · Cited by 13
Algorithmic treatment of the spin-echo effect
Lloyd, S., & Zurek, W. H. (1991) · Journal of Statistical Physics · doi:10.1007/bf01017985 · Cited by 13
Program Representation for General Intelligence
Looks, M., & Goertzel, B. (2009) · Artificial General Intelligence · doi:10.2991/agi.2009.32 · Cited by 13
Toscano, F. S., & Zenil, H. (2017) · Complexity · doi:10.1155/2017/7208216 · Cited by 13
Shannon Entropy vs. Kolmogorov Complexity
Muchnik, A. A., & Vereshchagin, N. (2006) · Lecture notes in computer science · doi:10.1007/11753728_29 · Cited by 13
The Simplicity and Power model for inductive inference
Pothos, E. M., & Wolff, J. G. (2006) · Artificial Intelligence Review · doi:10.1007/s10462-007-9058-x · Cited by 13
Metadata
- Authors
- Solomonoff, R. J.
- Corresponding author
- Solomonoff, R.J.
- Venue
- Information and Control
- Publisher
- Elsevier BV
- Published
- 1964-06
- Volume
- 7
- Issue
- 2
- Pages
- 224-254
- ISSN
- 0019-9958 (ISSN-L)
- OpenAlex
- W2135625884
- Cited by
- 1,758
- References
- 6
- Citations by year
- 2026: 43 · 2025: 16 · 2024: 18 · 2023: 22 · 2022: 35 · 2021: 52 · 2020: 60 · 2019: 40 · 2018: 60 · 2017: 67 · 2016: 64 · 2015: 75 · 2014: 59 · 2013: 94 · 2012: 88
- FWCI
- 1.52
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Information and Control (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
- Physical Sciences › Computer Science › Computational Theory and Mathematics › semigroups and automata theory
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- inductive reasoning, universal Turing machine, phrase structure grammar, prediction accuracy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
