Track Editors

Kristian Kersting, TU Dortmund, Germany

Associate Editors

Artur d'Avila Garcez, City University London, UK
Tarek Besold, Free University of Bozen - Bolzano, Italy
Leon Bottou, Facebook, NY, USA
Ramananthan Guha, Google Inc., Mountain View, USA
Luis Lamb, UFRGS, Brazil
Gary Marcus, New York University, USA
Risto Miikkulainen, University of Texas, Austin, USA

Track Overview

The recent success of deep neural networks at tasks such as language modelling, computer vision, and speech recognition has attracted considerable interest from industry and academia. Achieving a better understanding and widespread use of such models involves the use of Knowledge Representation and Reasoning together with sound Machine Learning methodologies and systems.

The goal of this special track, which closed in 2017, was to serve as a home for the publication of leading research in deep learning towards cognitive tasks, focusing on applications of neural computation to advanced AI tasks requiring knowledge representation and reasoning.


Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures

Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezny, Steven Schockaert and Ondrej Kuzelka

Learning Explanatory Rules from Noisy Data

Richard Evans and Edward Grefenstette

On the Behavior of Convolutional Nets for Feature Extraction

Dario Garcia-Gasulla, Ferran Parés, Armand Vilalta, Jonatan Moreno, Eduard Ayguadé, Jesús Labarta, Ulises Cortés and Toyotaro Suzumura

Symbol Grounding Association in Multimodal Sequences with Missing Elements

Federico Raue, Andreas Dengel, Thomas M. Breuel and Marcus Liwicki