ADR Awareness Centre

1D convolutions are extensively used in digital signal processing (filtering/denoising) and analysis (also through CNNs).  As their computational complexity is of the order O(N^2), their fast execution is a must.

This lecture overviews Human Centered Computing that has many applications in Human-Computer Interfaces, Data Analytics and Social Media Analytics. It covers the following topics in detail: Semantic Video Content Analysis.

This lecture overviews Graph Convolutional Networks (GCN) that have many applications in Deep Learning, Signal and Video Analysis, Network Theory, Web Science and Social Media Analytics.

This lecture overviews Adversarial Machine Learning that has many applications in DNN robustness and in privacy protection.

In this lecture, the limitations of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) in effectively processing sequences are emphasized.

Convolutional Neural Networks form the backbone of current AI revolution and are used in a multitude of classification and regression problems. This lecture overviews the transition from multilayer perceptrons to deep architectures.