IJCNN 2019 - Accepted Tutorials 

The finalized tutorials to be included in the conference program are listed below.

 Number

Title

Organizers

1

Deep Learning for Graphs

Davide Bacciu (Università di Pisa)

2

PHYSICS OF THE MIND

Leonid I. Perlovsky, Harvard University

3

Beyond Deep Learning: How to get Fast, Interpretable and Highly Accurate Classifiers

Plamen Angelov and  Xiaowei Gu, Lancaster University, UK

4

Theory and Methodology of Transfer Learning

Pierre-Alexandre Murena,  AgroParisTech And France and Antoine Cornuejols,  Télécom ParisTech and AgroParisTech

5

Deep Learning: Artificial Neural Networks and Kernel based Models

Siamak Mehrkanoon, DKE, Maastricht University, Johan A. K. Suykens, ESAT-STADIUS, KU Leuven, Belgium

6

Modern Gaussian Processes: Scalable Inference and Novel Applications

Edwin V. Bonilla, Data61, Australia and Maurizio Filippone, EURECOM, France

7

Machine Learning methods in Spiking Neural Networks for classification problems

Abeegithan Jeyasothy (Nanyang Technological University, Singapore), Savitha Ramasamy (Institute for Infocomm Research, A*STAR), Suresh Sundaram (Nanyang Technological University, Singapore)

8

Universal Turing Machines and
How They Emerge from DN Network

Juyang Weng, Michigan State University

9

Tensor Decompositions for Big Data Analytics: Trends and Applications

Danilo P. Mandic, Ilia Kisil and Giuseppe G. Calvi,, Imperial College London

10

Task-Independent and Modality-Independent Developmental Learning Engines: From Theory to Programming (*)

Juyang Weng and Juan L. Castro-Garcia, Michigan State University,

11

Information Geometry: An Introduction

Jun Zhang (Professor of University of Michigan-Ann Arbor, USA)

12

Non-Iterative Learning Methods for Classification and Forecasting

P. N. Suganthan, Technological University, Singapore.