Talks and Presentations

Icons: What do we know? Do we know things? Let’s find out

conference

presentation

Analysis of psychological normative studies and learning with Deep Learning models.

Jay Paul Morgan

Manifold-aware Adversarial Examples

workshop

presentation

Constructing manifold-aware adversarial examples using our adaptive neighbourhoods algorithm.

Jay Paul Morgan

Cloud Removal

workshop

poster

Poster presentation of our work of removing cloud contaminants from ground-based solar imagery.

Jay Paul Morgan

Adaptive Neighbourhoods for the Discovery of Adversarial Examples

workshop

presentation

We demonstrate how, through the development of uniquely-adaptive searchable regions, existing methods can help to further improve the robustness of Deep Learning models, and also make the existing methods applicable to non-image related tasks by providing an upper bound for discovering adversarial examples.

Jay Paul Morgan

Learning how to Learn with Deep Learning

presentation

A small presentation given to a select group of students during the summer months. This presentation gives a very brief introduction to Deep Learning and the basic components that make up the feed-forward network.

Jay Paul Morgan

Trustable Machine Learning Systems

presentation

workshop

I present the foundational knowledge for understanding adversarial examples, how we can use the input space to dictate the search space for the existence of these examples, and demonstrate their presence with the use of SAT-solving. This work, as a free and open-source project, provides a framework for ML practitioners to verify their own architectures.

Jay Paul Morgan

A Chatbot Framework for the Children’s Legal Centre

conference

presentation

We present novel method to address legal rights for children through a chatbot framework by integrating machine learning, a dialogue graph, and information extraction.

Jay Paul Morgan