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interpretable machine learning

Why the simplest explanation is always the best

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PreambleThe simplest explanation is always the best among the explanations that are representative.  It is called the principle of parsimony,  Occam's razor. It is the bedrock of scientific enlightenment. There is a recent development that people start to do a category error to discard this...

machine learning data science overfitting complexity complexity theory interpretable machine learning bayesian statistics. statistical models evalutionary computation overgenearlisation

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...

machine learning deep learning occam's razor complexity theory interpretable machine learning statistical mechanics

Misconceptions on non-temporal learning: When do machine learning models qualify as prediction systems?

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Preamble    Babylonian Tablet for square root of 2. (Wikipedia)Prediction implies a mechanics, as in knowing a form of a trajectory over time.  Strictly speaking a predictive system implies knowing a solution to the path, set of variable depending on time, time evolution of the system under...

machine learning causality applied mathematics deep learning supervised learning dynamical systems reinforcement learning interpretable machine learning representation learning

The concept of overgeneralisation and goodness of rank : Overfitting is not about comparing training and test learning curves

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Preamble     Walt Disney Hall, Los Angeles  (Wikipedia)Unfortunately, it is still thought in machine learning classes that overfitting can be detected by comparing training and test learning curves on the single model's performance.  The origins of this misconception is unknown. Looks like an...

machine learning stat interpretable machine learning connectionist learning overgenearlisation bayesian inference

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....

physics machine learning deep learning overfitting statistics interpretable machine learning sparse recovery statistical mechanics statistical models inductive bias

Building robust AI systems: Is an artificial intelligent agent just a probabilistic boolean function?

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Preamble    George Boole (Wikipedia)Agent, AI agent or an intelligent agent is used often to describe algorithms or AI systems that are released by research teams recently. However, the definition of an intelligent agent (IA) is a bit opaque. Naïvely thinking, it is nothing more than a decision...

functional programming causality deep learning reinforcement learning interpretable machine learning connectionist learning robustness bayesian statistics.

Physics origins of the most important statistical ideas of recent times

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Figure: Maxwell's handwritings, state diagram (Wikipedia)PreambleThe modern statistics now move into an emerging field called data science that amalgamate many different fields from high performance computing to control engineering. However, the emergent behaviour from researchers in machine...

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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...

machine learning statistical physics random matrix theory spectral ergodicity interpretable machine learning statistical mechanics ergodic hypothesis