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SaddlePoint

Academic and technical blog — statistics, machine learning, and adjacent topics.

Towards a Testable Theory of Emergence

ai
machine-learning
transformers
emergence
theory

A proposal for testing emergence as heldout but reachable coherence under recursive retrieval.

Jun 30, 2026
33 min

Science Moves. Engineering Compounds.

ai
machine-learning
engineering
moats

Scientific advantage diffuses faster every year; what endures is engineering muscle memory — operational capability earned through execution, not read from papers.

Jun 13, 2026
5 min

PageRank and KNN: an equivalence

graphs
ml
notebook

A tutorial-style notebook showing how PageRank on a weighted similarity graph relates to KNN-style neighborhood scores.

Dec 15, 2020
5 min

ML App Interoperability with Deep Learning, Part 2

machine-learning
ml-apps
interoperability
deep-learning

Deep learning frameworks provide a practical default for model interchange: readable specifications, scalable execution, and broad commercial adoption.

Jan 10, 2018
4 min

ML App Interoperability with PMML, Part 1

machine-learning
ml-apps
interoperability

PMML is a serious model-exchange effort, but weak importer support, inconsistent model interfaces, and XML-heavy design limit its role as a default.

Jan 9, 2018
3 min

A Small Step Toward Data Science Automation

machine-learning
automation
data-science

Model selection can be treated as a decision problem: learn rules from data characteristics, then use those rules to guide downstream modeling.

Nov 8, 2017
3 min

DataFrame as the ML Data Interface

machine-learning
dataframe
data-engineering

Even when data originates in flexible NoSQL systems, ML workflows need structured responses. The DataFrame is a practical default container.

Oct 27, 2017
3 min

A USB Interface for ML Apps

machine-learning
ml-apps
architecture

A USB metaphor for machine learning apps: separate data, code, config, and authentication so components can connect cleanly across environments.

Oct 26, 2017
3 min

Axioms for Reliable ML Apps, Part 2

machine-learning
ml-apps
architecture

A concrete architecture for ML apps: versioned data, code, config, micro apps, DAG orchestration, and DataFrames as the default data interface.

Oct 24, 2017
3 min

Reliable ML Apps: Goals and Guarantees

machine-learning
ml-apps
architecture

Machine learning apps need explicit guarantees: repeatability, auditability, recoverability, scalability, testability, security, responsiveness, and representation.

Oct 22, 2017
3 min
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