Speaker
Description
Everybody knows that machine learning models are initialized with random numbers. If we want to make our experiments reproducible, we have to use a random seed. But when and where should we use random seeds to freeze the entire environment? Sometimes, randomness occurs even when a seed is used.
I present real examples demonstrating the importance of randomness at different levels in machine learning, from model building and data analysis to training. Most researchers use a fixed random seed during experiments, but if they work with non-deterministic operations, the results may not be reproducible. Some pitfalls can be avoided, while others cannot, depending on the nature of the experiment and the structure of the model itself. However, understanding the real limitations of an experiment is crucial. In laboratory and production environments, this knowledge can save significant amounts of time and money in training models.
In the lecture, I present real code snippets to demonstrate how model training can fail due to a lack of randomness, such as when working with an unshuffled dataset. Researchers often rely on the default initialization methods for layers and nodes, which may yield predictable results but not necessarily the most optimal ones. This area is vast, even in classical machine learning. However, in the lecture, I will also showcase some use cases from the field of quantum machine learning
