AI — Models & Research

Mocap as a Service: Video Motion Capture Makes Human Motion Analysis for Everyone

Lecture 23 | MIT 6.832 (Underactuated Robotics), Spring 2019

Lecture 22 | MIT 6.832 (Underactuated Robotics), Spring 2019

COMMENT FONCTIONNE LE MACHINE LEARNING ?

HOW DOES MACHINE LEARNING WORK?

Fuzzy & Annotated Semantic Web Languages

Uncertainty-aware Food Analysis by Deep Learning

Neuromorphic computing with emerging memory devices

Human Authentication Using Facial Cues

From Research to Scalable AI Solutions, 2 examples: Trust & Fairness and CNN Automatic Generation

Deep Learning for SPECT, SPECT/CT and PET

Learning Embedding Space for Clustering From Deep Representations

A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment

Lecture 21 | MIT 6.832 (Underactuated Robotics), Spring 2018

Lecture 20 | MIT 6.832 (Underactuated Robotics), Spring 2018

Backpropagation, intuitively | Deep Learning Chapter 3

Backpropagation calculus | Deep Learning Chapter 4

Gradient descent, how neural networks learn | Deep Learning Chapter 2

But what is a neural network? | Deep learning chapter 1

BRAIN 2025, Prof. Terry Sejnowski

Defaul Logic

Autoepistemc Logic

Taxonomies and Inheritance

Natural Language Semantics

Resource Description Framework (RDF)

English to CD Theory

Top Down and Bottom Up Reasoning

Semantic Nets and Frames

Reification and Abstract Entities

Normalisation

Natural Language Semantics

DL: Extensions

DL: ALC

Classification

CD Theory (contd)

Backward Chaining

ALC examples

A-box reasoning

The Rete Algorithm

The Rete Algorithm

The OPS5 Expert System Shell

Syntax