I missed this when it came out!
Niels Leadholm, a visiting researcher, discusses his (recently de-anonymized) PhD research on hierarchical feature binding and robust machine vision. He first explores the issue of robust machine vision and his motivation in developing a deep-learning neural network architecture using a biologically-inspired approach. Many AI systems nowadays are vulnerable to adversarial examples. Niels explains how the characteristics of “feature binding,” which happens in a primate’s brain, can be implemented in machine learning systems to enhance robustness.
If you want to follow Niels’ work, you can follow him on Twitter (@neuro_AI).
Papers in the presentation:
Solutions to the Binding Problem - Anne Treisman
Similar binding papers:
The Emergence of Polychronization and Feature Binding in a Spiking Neural Network Model of the Primate Ventral Visual System - Akihiro Eguchi, James B. Isbister, Nasir Ahmad, Simon Stringer
Coding of Border Ownership in Monkey Visual Cortex - Hong Zhou, Howard S. Friedman and Rüdiger von der Heydt
Synchrony unbound: a critical evaluation of the temporal binding hypothesis - M N Shadlen , J A Movshon
Intriguing properties of neural networks - Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus
Scalable Object Detection using Deep Neural Networks - Dumitru Erhan, Christian Szegedy, Alexander Toshev, Dragomir Anguelov
Untangling invariant object recognition - James J DiCarlo, David D Cox