Seminar: Tensorization and Uncertainty Quantification in Deep Learning

The speaker will give an overview of the following two topics:

1. Modern deep neural networks, which consist of large weight matrices, are often prone to over-parameterization and can be computationally expensive to train and store. Tensorizing and decomposing these weight matrices has emerged as an effective solution to this problem, since it allows for neural networks to represent large matrices with significantly fewer parameters. This technology has been applied in various neural network architectures and use cases, making it an interesting topic of research. This talk will include a brief introduction on the basic idea, some hands-on tutorials on how to implement such models with very few code, and an overview on related publications.
   
   
2. Deep neural networks have achieved impressive results in a wide range of machine learning tasks, but accurately quantifying the uncertainty of these models remains a significant challenge. Gaussian processes, on the other hand, provide a principled approach to modeling uncertainty. However, they often struggle to scale to large amounts of training data. This talk will first introduce the fundamental concept of the latest research on scalable Gaussian Process models. Second, we would discuss two recent publications that demonstrate how to incorporate scalable Gaussian Processes with representation / deep learning. References to programming frameworks will also be included.

Yinchong Yang holds a master in statistics and a PhD in computer science from the Ludwig Maximilian University of Munich. As a senior key expert of robust AI at Siemens, he conducts research in the quantification and certification of robustness and uncertainty for industrial grade AI.

He’s also interested in tensor decomposition methods in machine learning, such as tensorized neural networks and relational learning from tensor data.

 
About the EuroCC-2 project

EuroCC 2 will work to identify and address the skills gaps in the European High Performance Computing (HPC) ecosystem and coordinate cooperation across Europe to ensure a consistent skills base.

The role of EuroCC 2 is to establish and run a network of more than 30 NCCs across the EuroHPC Participating States. The NCCs act as single points of access in each country between stakeholders and national and EuroHPC systems. They operate on a regional and national level to liaise with local communities, in particular SMEs, map HPC competencies and facilitate access to European HPC resources for users from the private and public sector.

EuroCC 2 delivers training, interacts with industry, develops competence mapping and communication materials and activities, and supports the adoption of HPC services in other related fields, such as quantum computing, artificial intelligence (AI), high performance data analytics (HPDA) to expand the HPC user base.

This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101101903. The JU receives support from the European Union’s programme Digital Europe and Germany, Bulgaria, Austria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, France, Netherlands, Belgium, Luxembourg , Slovakia, Norway, Switzerland, Turkey, the Republic of North Macedonia, Iceland, Montenegro and Serbia.