Tutorials

ECAI 2020 hosts two kinds of tutorials: a) “standard” half­-day tutorials; b) 90-minute “spotlight” tutorials. Spotlight tutorials are meant to address emerging areas, techniques, methodologies and perspectives in AI or specialized topics. Standard tutorials are meant to cover rather established areas of AI such as (but not exclusively) Machine Learning, Knowledge Representation and Reasoning, Semantic Web, Natural Language Processing, Multi-Agent Systems, Robotics, as well as topics in the overlap of such research areas.

Tutorials should serve one or more of the following objectives:

  • Introduce novices to major topics within Artificial Intelligence.
  • Introduce expert non-specialists to an AI subarea.
  • Motivate and explain a topic of emerging importance for AI.
  • Survey a mature area of AI research and/or practice.
  • Provide instruction in established but specialized AI methodologies.
  • Present a novel synthesis combining distinct lines of AI work.
  • Introduce AI audiences to an external topic that can motivate or use AI research.
  • Mentor AI researchers (particularly, junior researchers) on a broad AI-relevant non-technical topic (examples could be AI jobs, or ethical issues in AI).

August 29

9:00 am
12:15 pm
Automatic Keyphrase Extraction from Text: A Walk-through
9:00 am
12:15 pm
Probabilistic Circuits: Representations, Inference, Learning and Applications
9:00 am
10:30 am
An Introduction to Vector Symbolic Architectures and Hyperdimensional Computing
10:45 am
12:15 pm
Machine Learning Guided Program Synthesis
1:45 pm
5:00 pm
Deep Learning in the Fields
1:45 pm
5:00 pm
StaRAI – Semantics and Symmetries in Exact Lifted Inference
1:45 pm
5:00 pm
A Submodular Optimization Framework for Data, Feature and Topic Summarization

August 30

9:00 am
12:15 pm
Probabilistic Inductive Logic Programming
9:00 am
10:30 am
Graph Representation Learning in the Presence of Community Outliers
10:45 am
12:15 pm
Data Driven Policy Learning in Real World Multi-Agent Environments
1:45 pm
5:00 pm
Cognitive Vision: On Deep Semantics for Explainable Visuospatial Computing
1:45 pm
5:00 pm
Deep Learning for Graphs – Processing symbolic relationships with neural networks
1:45 pm
3:15 pm
Argumentative Explanations in AI
3:30 pm
5:00 pm
Explanation and Fairness in Unsupervised Learning

September 04

9:00 am
12:15 pm
Computational Argumentation in the context of Human-Agent Interaction
9:00 am
12:15 pm
Scalable Deep Learning: How far is one billion neurons?
9:00 am
12:15 pm
Practical Uses of Existential Rules in Knowledge Representation
9:00 am
12:15 pm
Tutorial on Distributed Constraint Optimization for the Internet-of-Things (DCOP for IoT)
1:45 pm
5:00 pm
Advances in Maximum Satisfiability
1:45 pm
5:00 pm
Measuring Algorithmic Fairness: challenges and solutions for the industry
1:45 pm
5:00 pm
Answer Set Programming: From Theory to Practice
1:45 pm
5:00 pm
Knowledge Graph Embeddings: From Theory to Practice

September 05

9:00 am
12:15 pm
Fundamentals of Accelerated Computing with CUDA Python
9:00 am
12:15 pm
Mathematical Morphology and Artificial Intelligence
10:45 am
12:15 pm
Cognitive Logics: Mechanisms Predicting Human Inference Patterns
1:45 pm
5:00 pm
Referring Expressions in Knowledge Representation System
1:45 pm
5:00 pm
Belief Change: From 1985 to present days
3:30 pm
5:00 pm
Combining IoT and ML for situation classification