Current Trends in AI
The world of artificial intelligence evolves at an astonishing speed. For those working with AI on a daily basis, staying up to date with the newest techniques within various domains is a challenge. In this seminar series, academic experts will bring you up to speed with the hottest topics in Computer Vision, Natural Language Processing, Combinatorial Optimization and Time Series Analysis. All seminars will include various use cases and one testimonial from an industrial partner.
Practical information:
Want to register?
- Register until: 10 Nov 2022
- Prerequisites: For those working with AI on a daily basis
- Price: €800 for complete seminar or €220 for one seminar
Georganiseerd door:
Program
Wednesday 16 November 2022
Trends in Computer Vision (9 - 12.15h)
Computer Vision traditionally employs supervised Deep Learning of Convolutional Neural Networks. However, to reach adequate performance in industrial application cases, it typically needs an enormous amount of annotated training examples. We present various techniques to tackle this:
- Few Shot Learning and Semi Supervised Learning
- Training from simulation images, Domain Transfer and Generative Adversarial Networks
Next to that, we focus on techniques to drastically reduce the computational demand of CNN-based neural networks, in order to reach real-time behaviour on resource-constrained embedded platforms.
Trends in Natural Language Processing (13 - 16.15h)
The second seminar will focus on the newest techniques in Natural Language Processing, with a particular focus on transformer architectures such as BERT, GPT-3 and ChatGPT. The following topics will be tackled during the seminar:
- Overview of BERT, GPT-3 and the transformer architecture
- Transfer learning in NLP
- ChatGPT
- Integration of CV and NLP
- Explainability
- Multilinguality
Wednesday 30 November 2022
Combinatorial Optimization (9 - 12.15h)
The third seminar will give you an overview of the huge progress that has been made regarding calculation speed and efficiency in solving combinatorial problems, before addressing these topics:
- Uncertainty within combinatorial optimization, and dynamic problems
- Data science meets optimization
- Parameter tuning and instance space analysis
- GPU and Cloud based optimization
Time Series Analysis (13 - 16.15h)
The domain of Time Series Analysis has mainly focussed on static data sets and historical data. However, when performing analyses in an online setting, the distribution of data changes over time and adaptive learning strategies are vital. This seminar will teach you about techniques to handle this continuously changing data stream, such as Automated Machine Learning, Adaptive Learning, Continual Learning, Transfer Learning and Active Learning.