2022-03

Symbolic Logic

Protected: Discrepancy between correlation (regression coefficient) and causation (intervention effect)

Differences between regression coefficients and intervention effect values for digital transformation, artificial intelligence , and machine learning tasks.
IOT技術:IOT Technology

Submodular Optimization and Machine Learning

Overview of Machine Learning with Submodular Optimization Submodular functions are a concept corresponding to c...
Symbolic Logic

Machine Learning Professional Series “Submodular Optimization and Machine Learning” reading notes

  Machine Learning Professional Series "Submodular Optimization and Machine Learning" reading notes Submodular f...
ICT技術:ICT Technology

Collecting AI Conference Papers

  Collecting AI Conference Papers The overall picture of the AI conference is as follows. AI-related con...
グラフ理論

Bayesian inference and MCMC open source software

Bayesian inference and MCMC open source software Bayesian statistics means that not only the data, but also th...
グラフ理論

Machine Learning Startup Series – Introduction to Machine Learning with Bayesian Inference Reading Notes

Summary Bayesian estimation can be one of the statistical methods for interpreting data and learning models fro...
Symbolic Logic

From Inductive logic Programming 2011 Proceedings

Machine Learning Technology  Artificial Intelligence Technology  Natural Language Processing Technology  Semantic Web Te...
IOT技術:IOT Technology

ISWC2019 Papers

ISWC2019 Papers From ISWC2019, an international conference on Semantic Web technology, one of the artificial i...
Symbolic Logic

Protected: Causal InferenceIntroduction(2)Stratified Analysis and Regression Modeling

Theory and practice of causal inference through analysis by stratified analysis and regression models for statistical causal estimation used in digital transformation , artificial intelligence , and machine learning tasks
Symbolic Logic

Protected: Introduction to Causal Inference (1) Confounding Factors and Randomized Experiments

Introduction to statistical causal inference (randomized experiments controlling for confounding factors to distinguish between causality and pseudo-correlation)
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