134 citations · 178 across the 11 of their papers we have counts for
12 papers
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…
Automating the Practice of Science -- Opportunities, Challenges, and Implications
Sebastian Musslick, Laura K. Bartlett, Suyog H. Chandramouli +11
Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches e…
Large language models surpass human experts in predicting neuroscience results
Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun +36
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offe…
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…
A Benchmark for Compositional Visual Reasoning
Aimen Zerroug, Mohit Vaishnav, Julien Colin +2
A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning…
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…
Efficiency of learning vs. processing: Towards a normative theory of multitasking
Yotam Sagiv, Sebastian Musslick, Yael Niv +1
A striking limitation of human cognition is our inability to execute some tasks simultaneously. Recent work suggests that such limitations can arise from a fundamental tradeoff in…
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…
Topological limits to parallel processing capability of network architectures
Giovanni Petri, Sebastian Musslick, Biswadip Dey +5
The ability to learn new tasks and generalize performance to others is one of the most remarkable characteristics of the human brain and of recent AI systems. The ability to perfor…
A Formal Approach to Modeling the Cost of Cognitive Control
Kayhan Ozcimder, Biswadip Dey, Sebastian Musslick +4
This paper introduces a formal method to model the level of demand on control when executing cognitive processes. The cost of cognitive control is parsed into an intensity cost whi…
A Graph-Theoretic Approach to Multitasking
Noga Alon, Jonathan D. Cohen, Biswadip Dey +6
A key feature of neural network architectures is their ability to support the simultaneous interaction among large numbers of units in the learning and processing of representation…