Overview of ChebNet and Examples of Algorithms and Implementations
Overview of ChebNet
ChebNet (Chebyshev network) is a type of graph neural network (GNN) proposed by Deffer...
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Antimatter, Gravity and its Applications
Introduction
The other day, NHK news reported "First Observation of Gravitational Fall of 'Antimatter' Internat...
Bipolar lithium-ion iron phosphate battery
Introduction
From "Really?" Life expectancy is 1,000,000 km? Toyota's new battery will save Japan! Coming in 202...
Overview of state space models and examples of implementing time series data analysis using R and Python
Overview of time series data analysis
Time-series data is called data whose values change over time, suc...
What is a function – its history, programming and machine learning
About Functions
A function will generally be mathematically defined as a rule that assigns to each element in ...
Protected: Explainable Machine Learning (18)Adversarial Examples
Explainable Machine Learning with Adversarial Sample Approach utilized for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Cyber Security, Surrogate Models, Neural Networks, Black Box Attack, Expectation Over Transformation algorithm, EOT, InceptionV3, TensorFlow, Fast gradient method, VGG16 classifier, ImageNet, adversarial patch, 1-pixel attack, L-BFGS method, Fast gradient sign method
Protected: Explainable Artificial Intelligence (15) model-independent interpretation (sharpley value)
Model-independent interpretation with Sharpe Ray values as explainable artificial intelligence used for digital transformation, artificial intelligence, and machine learning tasks breakDown, fastshap, R language, symmetry axioms, LIME, SHAP, sparse explanation, efficiency, symmetry, dummy, additivity principle, Sharpe Ray values, cooperative game theory
Protected: On-line Stochastic Optimization and Stochastic Dual Averaging (SDA) for Machine Learning
On-line stochastic optimization and stochastic dual averaging methods for machine learning (mirror image descent, strongly convex functions, convex functions, convergence rates, polynomial decay averaging, strongly convex regularization) for digital transformation, artificial intelligence and machine learning tasks.
Evaluation of clustering for familiarization with k-means
On the evaluation of clustering around k-means for digital transformation, artificial intelligence, and machine learning tasks curse of dimensionality, Mahalanobis distance, Davies-Bouldin index, Dunn index, squared error, RSME, cluster number estimation, inter-cluster density, intra-cluster density
KI 2017: Advances in Artificial Intelligence Papers
KI2017
In the previous article we discussed KI2016. In this issue, we describe KI2017, which w...