ADR Awareness Centre

Generative Adversarial Networks (GANs) are part of the cutting edge in recent machine learning research.

Deep Convolutional Generative Adversarial Networks (DCGAN) have been used to generate highly compelling pictures or videos, such as manipulated facial animations, interior and outdoor images, videos.

This lecture overviews  object pose estimation   that has many applications in Human-Robotic Interaction (HRI), Robotics and Augmented Reality.

We study the power of cooperation in a network of communicating agents that solve a learning task. Agents use an underlying communication network to get information about what the other agents know.

Medicine stands apart from other areas where machine learning can be applied.

This lecture will overview 2D linear and cyclic convolution. Then it will present their fast execution through FFTs, resulting in algorithms having computational complexity of the order O(N^2log2N).