Thesis supervisor: Gergő Orbán
Location of studies (in Hungarian): BME KTT Abbreviation of location of studies: BME
Description of the research topic:
The memory systems entertained by biological systems is effective for ensuring survival of individuals by acquiring knowledge about the environment, but memories are far from being verbatim copies of sensory experience. In fact, these memories are prone to errors which distort these memories. More importantly, the errors are predictable systematic deviations which depends on a number of factors. Functional approaches of perception have achieved great success in identifying sensory illusions with the signatures of optimal computations which emerge as a consequence of integration of sensory evidence and learned regularities when data is limited. In this project, we are using advances in machine learning to explain the properties of memory systems and to provide a normative framework of 'memory illusions’. This research links to a number of hot topics in machine learning,/artificial intelligence, including continual learning, latent variable compression. The candidate will have a chance to learn cutting-edge methods in machine learning and will get to know an expanding field. computational cognitive science.
Required language skills: English Number of students who can be accepted: 1
Deadline for application: 2024-05-31
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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