Displaying 133 resources
Discrete Fourier Transform
This lecture overviews Discrete Fourier Transform that has many applications in digital signal processing and analysis and in power spectrum estimation.
The power of cooperation in networks of learning agents
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.
Super Resolution
This lecture overviews Super Resolution that has many applications in digital imaging, display and printing.
Introduction to Statistics
This lecture provides an Introduction to Statistics that has many applications in Data Analytics, Machine Learning and Signal Analysis. It covers the following topics in detail: Random Variables. Data Types. Data Sampling.
Generative Adversarial Networks
This lecture overviews Generative Adversarial Networks that have many applications in Media Production.
Orthogonal Signal Transforms. Fourier Series
This lecture overviews Orthogonal Signal Transforms. Fourier Series that has many applications in signal processing, analysis and compression.
Fast 2D Convolutions Algorithms
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).
Graph Convolutional Networks
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. It covers the following topics in detail: Graph Convolutions.
Robust Statistics
This lecture overviews Robust Statistics that has many applications in Data Analytics and Digital Signal Processing and Analysis. It covers the following topics in detail: Outliers.
AI4Media Workshop on GANs for Media Content Generation
Generative Adversarial Networks (GANs) are part of the cutting edge in recent machine learning research.
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.
Graph Neural Networks
This lecture overviews Graph Neural Networks that has many applications in Deep Learning, Signal and Video Analysis, Network Theory, Web Science and Social Media Analytics. It covers the following topics in detail: Introduction to Graphs.