Prototype Analysis in Hopfield Networks With Hebbian Learning.

We discuss prototype formation in the Hopfield network. Typically, Hebbian learning with highly correlated states leads to degraded memory performance. We show that this type of learning can lead to prototype formation, where unlearned states emerge as representatives of large correlated subsets of states, alleviating capacity woes.

VASE: Variational Assorted Surprise Exploration for Reinforcement Learning.

Exploration in environments with continuous control and sparse rewards remains a key challenge in reinforcement learning (RL). One of the approaches to encourage more systematic and efficient exploration relies on surprise as an intrinsic reward for the agent. We introduce a new definition of surprise and its RL implementation named variational assorted surprise exploration (VASE).

Hierarchical structure from motion optical flow algorithms to harvest three-dimensional features from two-dimensional neuro-endoscopic images.

Technical advances have led to an increase in the use of the endoscope in neurosurgery in recent years, particularly for intraventricular procedures and pituitary and anterior skull base surgery. Recently stereoscopic three-dimensional (3D) endoscopes have become available and may over time replace traditional two-dimensional (2D) imagery. An alternative strategy would be to use computer software algorithms to give monocular 2D endoscopes 3D capabilities.

Deep networks are effective encoders of periodicity.

We present a comparative theoretical analysis of representation in artificial neural networks with two extreme architectures, a shallow wide network and a deep narrow network, devised to maximally decouple their representative power due to layer width and network depth. We show that, given a specific activation function, models with comparable VC-dimension are required to guarantee zero error modeling of real functions over a binary input. However, functions that exhibit repeating patterns can be encoded much more efficiently in the deep representation, resulting in significant reduction in complexity.