Peter Richtárik
KAUST
VOL_04
22 June 2022 at 5 pm
online & Twin City C, Bratislava
The role of local training in federated learning: We finally know what’s going on!
Ever wanted to start utilizing federated learning? Professor Peter Richtarik from King Abdullah University of Science and Technology will give us a detailed lecture on the role of local training in federated learning and will answer any questions you may have.
Federated Learning refers to machine learning over private data stored across a large number of heterogeneous devices, such as mobile phones or hospitals. In an October 2020 Forbes article, and alongside self-supervised learning and transformers, Federated Learning was described as one of three emerging areas that will shape the next generation of AI technologies. In this talk I will explain how we recently resolved one of the key open algorithmic and mathematical problems in this field.
_Supported by
_Recording
_Speakers
Peter Richtárik is a professor of Computer Science at KAUST, Saudi Arabia, where he leads the Optimization and Machine Learning Lab. Through his work on randomized and distributed algorithms, he has contributed to the foundations of optimization, machine learning, and federated learning. Prof Richtárik’s serves as an Area Chair for leading machine learning conferences, including NeurIPS, ICML and ICLR, and is the recipient of numerous international awards.
_Agenda
Language: English
5:00 pm
Welcome
5:10 pm
Interactive presentation - The role of local training in federated learning: we finally know what's going on! - Peter Richtarik (KAUST)
6:10 pm
Live Q&A: _Slido #better_ai
_Sign up
Past Meetups
Understanding Neural Networks – Speech Recognition & Measuring Quality of ML Models
The explosion of ChatGPT and GPT4 into mainstream applications may seem like we have solved all there is regarding language. However, there are still challenges that AI struggles with.
In this edition of Better AI Meetup we will be talking about speech processing as well as designing ways to evaluate machine learning algorithms for accuracy and speed and choose a proper one for a particular use case.
More Data, Better Algorithms: Harnessing the Possibilities of Synthetic Data
The advent of generative models such as DALL-E, Midjourney or Stable Diffusion have brought a deluge of AI-generated images. The question we want to answer is: How to harness their power for innovative uses that improve quality of life?
We will present two practical examples on how to use generative AI for improving datasets for machine learning.
Lukáš will show us how to use AI for annotation and labeling of large datasets, while Igor has developed novel ways to generate fingerprints for training fast, accurate models for fragmented fingerprints. In both cases, AI is helping improve the accuracy of models that then help automate administrative processes or even solve crimes.
Safer Future of AI: Cybersecurity Equilibrium and Explainability of Deep Learning Models
Machine Learning is used not only to detect new malware, but also to create more efficient, harder to detect threats. Will the pros of machine learning outweigh the cons or will the cybersecurity equilibrium deteriorate? And how to explain what the AI does when it makes decisions? This time, we will look at outsmarting cyber-criminals as well as understanding the AI itself.
The role of local training in federated learning: We finally know what’s going on!
Ever wanted to start utilizing federated learning? Professor Peter Richtarik from King Abdullah University of Science and Technology will give us a detailed lecture on the role of local training in federated learning and will answer any questions you may have.
Federated Learning refers to machine learning over private data stored across a large number of heterogeneous devices, such as mobile phones or hospitals. In an October 2020 Forbes article, and alongside self-supervised learning and transformers, Federated Learning was described as one of three emerging areas that will shape the next generation of AI technologies. In this talk I will explain how we recently resolved one of the key open algorithmic and mathematical problems in this field.
AI Meets Humans: The Challenges of Introducing ML Models into Everyday Life
What happens when research, its practical applications and human factor intersect? How to train the machine learning models to expect the unpredictable events that are inevitable in real life? This time we’ll look at the challenges the AI and ML technologies face when put to practical use.
Do you care for Datasets as much as for code in your ML Project?
This time, we want to give you insight into how AI and machine learning methods are put to practical use at Innovatrics for training new biometric algorithms and in Swiss Re, one of the largest re-insurance companies in the world, to develop new, innovative products.
Machine learning (ML) helped us to solve such complex tasks as face recognition, however when it is done on a large scale it relies on many high-effort data management tasks. We will describe our winding road from images in folders and labels stored in CSV files to structured versioned datasets with reproducible data processing pipelines. Moreover we mention how we deal with data versioning / validation / quality evaluation, reproducible ML trainings and experiment management.
First ever Slovak language neural model and why AI is becoming mainstream
The opening presentation is centered on the most recent success of AI research in Slovakia – the SlovakBERT model. Creating a neural language model for languages with limited available corpus can be challenging. We will explain what are the main obstacles when creating such model and what its practical applications are. The second presentation will focus on why AI is becoming mainstream and what it means for the future of the technology.
