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statistical mechanics

Compressive algorithmic randomness: Gibbs-randomness proposition for massively energy efficient deep learning

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Figure: Dual Tomographic CompressionPerformance, Süzen, 2025.PreambleRandomness is elusive and its probably one of the outstanding concepts for human scientific endeavour, along with gravity. Kolmogorov complexity, appears to be so novel in trying to answering "what is randomness?". The idea that...

Resolution of misconception of overfitting: Differentiating learning curves from Occam curves

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Preamble Occam (Wikipedia)A misconception that overfitted model can be identified with the  amount of generalisation gap between model's training and test sets over its learning curves is still out there. Even in some prominent online lectures and blog posts, this misconception is now repeated...

Loschimidt's Paradox and Causality: Can we establish Pearlian expression for Boltzmann's H-theorem?

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Boltzmann (Wikipedia)Post covers the papers: H-theorem do-conjecture, M. Süzen,  arxiv:2310.01458 (2023) PreambleProbably the most important achievement for humans is the ability to produce scientific discoveries, that  helps us objectively understand how nature works and build artificial tools...

Differentiating ensembles and sample spaces: Alignment between statistical mechanics and probability theory

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Preamble Sample space is the primary concept introduced in any probability and statistics books and in papers. However, there needs to be more clarity about what constitutes a sample space in general: there is no explicit distinction between the unique event set and the replica sets. The...

Overfitting is about complexity ranking of inductive biases : Algorithmic recipe

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Preamble    Figure: Moon patterns human brain invents. (Wikipedia)Detecting overfitting is inherently a comparison problem of the complexity of multiple objects, i.e., models or an algorithm capable of making predictions. A model is overfitted (underfitted) if we only compare it to another model....

Heavy-matter-wave and ultra-sensitive interferometry: An opportunity for quantum-gravity becoming an evidence based research

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Solar Eclipse of 1919 (wikipedia)Preamble    Cool ideas in theoretical physics are ofter opaque for general reader whether if they are backed up with any experimental evidence in the real world. The success of LIGO (Laser Interferometer Gravitational-wave Observatory) definitely proven the value...

Bayesian rabbit holes: Decoding conditional probability with non-commutative algebra

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Preamble    The White Rabbit (Wikipedia)A novice analyst or even experienced (data) scientist would have thought that the bar notation $|$ in representing conditional probability carries some different operational mathematics. Primarily when written in explicit distribution functions $p(x|y)$....

A misconception in ergodicity: Identify ergodic regime not ergodic process

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Preamble     Figure 1: Two observable's approach to ergodicity for Bernoulli Trials. Ergodicity appears in many fields, in physics, chemistry and natural sciences but in economics to machine learning as well. Recall that, ergodicity in physics and mathematical definition diverges significantly due...

Deep Learning in Mind a Gentle Introduction to Spectral Ergodicity

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Preamble    Figure: Monalisa on Eigenvector grids (Wikipedia)In the post, A New Matrix Mathematics for Deep Learning : Random Matrix Theory of Deep Learning, we have outlined a new mathematical concepts that are aimed at deep learning but in general belonging to applied mathematics. Here, we dive...