


Super Resolution
This lecture overviews Super Resolution that has many applications in digital imaging, display and printing.



Artificial Neural Networks. Perceptron
This lecture will cover the basic concepts of Artificial Neural Networks (ANNs): Biological neural models, Perceptron, Activation functions, Loss types, Steepest Gradient Descent, On-line Perceptron training, Batch Perceptron training.



Deep Autoencoders
This lecture overviews Deep Autoencoders that has many applications in image denoising, classification, generation and in object pose estimation.


Experience Driven Content Generation
Procedural content generation (PCG) is an increasingly important area of technology within modern human-computer interaction (HCI) design.


A comprehensive survey of geometric deep learning
The survey provides a comprehensive overview of deep learning methods for geometric data (point clouds, voxels, network graphs etc.). The relevant knowledge and theoretical background of geometric deep learning is presented first.



Real-like MAX-SAT Instances and the Landscape Structure Across the Phase Transition
In contrast with random uniform instances, industrial SAT instances of large size are solvable today by state-of-the-art algorithms.