IJS main lecture hall, Tuesday, 10.12.2024 at 13:00
Automated machine learning has been successful in supporting data scientists in selecting appropriate machine learning architectures, as well as optimizing hyperparameters. By doing so…
About
The AI@JSI seminars are a series of events focused on Artificial Intelligence, hosted by the Department of Knowledge Technologies at the Jožef Stefan Institute. About once a month, we invite researchers from around the world to present their work, aiming to inform both the professional community and the general public about the latest advancements in this field. Below, you will find information on the upcoming event, as well as details and recordings of past events.
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Upcoming event
Past events
Designing New Metaheuristics: Manual Versus Automatic Approaches
Metaheuristics, such as evolutionary algorithms, ant colony optimization, and particle swarm optimization, are among the most successful techniques for solving complex optimization problems. Historically, the design of metaheuristic algorithms…
Modeling Rac1 Dynamics: Insights into Cellular Movement and Internal Mechanics
Why do patterns emerge in nature, and what do they reveal about underlying processes? From the stripes on a zebra to waves in the ocean, patterns offer insight into complex systems. In our study, we explore…
ML Model Reliability From a Dataset Perspective
Machine learning (ML) models depend on the data on which they are trained. Consequently, flaws in datasets can lead to significant vulnerabilities in ML models …
Modelling the collapse of complex societies
Why do societies collapse? Some famous examples include Easter Island, the Maya, the Roman Empire, and the Chinese dynasties. The speaker has developed mathematical models for these historical cases and the talk will focus on…
Artificial Intelligence and Scientific Discovery: Paradigms, Progress, and Potential
Within the general excitement about artificial intelligence, there has been special interest in the technology’s application to discovery of scientific knowledge. Like AI itself, this subfield has a long history and many…
Transitions in probabilistic inference: From recommender systems to equation discovery
Inferential methods attempt to understand data and make predictions about the word by explicitly formulating generative models and fitting them. By this process, these methods are able to provide insight on…
Your data is not i.i.d.: Learning from Data Streams
I will first give a quick summary of Learning from Data Streams, and of Continual Learning, including some recent work on Online Continual Learning. I will give an overview of the TAIAO project, which stands for…
