134 citations · 178 across the 11 of their papers we have counts for
5 papers · 1 filter
Not Yet AlphaFold for the Mind: Evaluating Centaur as a Synthetic Participant
Sabrina Namazova, Alessandra Brondetta, Younes Strittmatter +2
Simulators have revolutionized scientific practice across the natural sciences. By generating data that reliably approximate real-world phenomena, they enable scientists to acceler…
GFN-SR: Symbolic Regression with Generative Flow Networks
Sida Li, Ioana Marinescu, Sebastian Musslick
Symbolic regression (SR) is an area of interpretable machine learning that aims to identify mathematical expressions, often composed of simple functions, that best fit in a given s…
A Quantitative Approach to Predicting Representational Learning and Performance in Neural Networks
Ryan Pyle, Sebastian Musslick, Jonathan D. Cohen +1
A key property of neural networks (both biological and artificial) is how they learn to represent and manipulate input information in order to solve a task. Different types of repr…
Recovering Quantitative Models of Human Information Processing with Differentiable Architecture Search
Sebastian Musslick
The integration of behavioral phenomena into mechanistic models of cognitive function is a fundamental staple of cognitive science. Yet, researchers are beginning to accumulate inc…
Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks
Sachin Ravi, Sebastian Musslick, Maia Hamin +2
The terms multi-task learning and multitasking are easily confused. Multi-task learning refers to a paradigm in machine learning in which a network is trained on various related ta…