Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces [PDF]
Frontiers in Artificial Intelligence, 2021 We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research. We show that this model-driven approach may require less training data and can potentially be more generalizable as
Santiago Hernández-Orozco +11 more
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Low Complexity, Low Probability Patterns and Consequences for Algorithmic Probability Applications
Complexity, 2023 Developing new ways to estimate probabilities can be valuable for science, statistics, engineering, and other fields. By considering the information content of different output patterns, recent work invoking algorithmic information theory inspired ...
Mohammad Alaskandarani, Kamaludin Dingle
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Binarized neural networks converge toward algorithmic simplicity: empirical support for the learning-as-compression hypothesis [PDF]
Frontiers in Computational NeuroscienceUnderstanding and controlling the complexity of neural networks is a central challenge in machine learning, with implications for generalization, optimization, and model capacity. While most approaches rely on entropy-based loss functions and statistical
Eduardo Y. Sakabe +14 more
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Semantic Algorithmic Information Theory: From Kolmogorov Complexity to Semantic Equivalence [PDF]
EntropyClassical Algorithmic Information Theory (AIT) provides a rigorous foundation for information-based similarity measurement, but classical formulations and their compression-based approximations largely operate at the syntactic level, making them ...
Jiatong Wu +4 more
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Simplicity and Complexity in Combinatorial Optimization [PDF]
EntropyMany problems in physics and computer science can be framed in terms of combinatorial optimization. Due to this, it is interesting and important to study theoretical aspects of such optimization.
Kamal Dingle, Marcus Hutter
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Entropy, 2023 This work applies concepts from algorithmic probability to Boolean and quantum combinatorial logic circuits. The relations among the statistical, algorithmic, computational, and circuit complexities of states are reviewed.
Bao Gia Bach +3 more
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Objective and Subjective Solomonoff Probabilities in Quantum Mechanics [PDF]
Electronic Proceedings in Theoretical Computer Science, 2018 Algorithmic probability has shown some promise in dealing with the probability problem in the Everett interpretation, since it provides an objective, single-case probability measure.
Allan F. Randall
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Queuing-Inventory Models with MAP Demands and Random Replenishment Opportunities
Mathematics, 2021 Combining the study of queuing with inventory is very common and such systems are referred to as queuing-inventory systems in the literature. These systems occur naturally in practice and have been studied extensively in the literature.
Srinivas R. Chakravarthy, B. Madhu Rao
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Adherence to the algorithmic approach for diagnosis of pulmonary embolism: A teaching hospital experience, Shiraz, Iran [PDF]
ARYA Atherosclerosis, 2020 BACKGROUND: We evaluated to see if the algorithmic approach of pulmonary embolism (PE) [Wells’ score, followed by D-dimer test and computed tomography pulmonary angiography (CTPA)] is appropriately followed in teaching hospitals of Shiraz, Iran.METHODS ...
Vahid Mohammadkarimi +5 more
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Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity [PDF]
Royal Society Open Science, 2018 Natural selection explains how life has evolved over millions of years from more primitive forms. The speed at which this happens, however, has sometimes defied formal explanations when based on random (uniformly distributed) mutations.
Santiago Hernández-Orozco +2 more
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