From Biology to Machine Learning and vice versa
Abstract: In this talk, we will revisit the intertwined history of neuroscience and machine learning, highlighting the many connections between the two fields — especially when dealing with natural images and the concept of invariance. Humans are naturally invariant to a wide range of transformations such as translation, scaling, and contrast changes. Understanding how our brains achieve this robustness could lead to the development of much more efficient artificial neural networks. We will explore how biologically inspired models, particularly those mimicking the structure and function of the visual cortex, have influenced modern ML architectures . Finally, we will present a specific use case where following a “human-inspired” approach led to improved performance of neural networks in blurred-image classification. Using a method based on entropy maximization, we show how pre-processing strategies inspired by biological systems can significantly narrow the gap between machine and human perception. This suggests that drawing deeper from biology not only helps us understand our brain better but also paves the way for more robust and interpretable machine learning systems.
Short bio: Prof. Marina Martinez-Garcia received the B.S. degree in mathematics from the Polytechnic University of Catalonia (UPC), Spain, in 2009, and the Ph.D. degree in computer science from Pompeu Fabra University (UPF) in 2014. She is a Professor in the mathematics Department of Universitat Jaume I, Castelló Spain. She is also a member of the Institut de Matemàtiques de Castello (IMAC) and the Instituto de Estudios Feministas Purificación Escribano. Her current research interests include machine learning and deep learning with applications in neuroscience.
Presenter: Prof. Marina Martinez-Garcia (Universitat Jaume I, Castelló Spain)
Date: 2025-05-15 12:15 (CEST)
Location: Oficinas ELLIS Alicante, Muelle Pte., 5 – Edificio A, Alicante 03001, Alicante ES
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This talk is part of the 3rd Distinguished Lectures Mini-Workshop.