501 citations · 1k across the 35 of their papers we have counts for
35 papers
Prior-Amortized In-Context Bayesian Inference for Generalized Linear Mixed-Effects Models
Alex Kipnis, Marcel Binz, Eric Schulz
Hierarchical data is ubiquitous in the empirical sciences and most commonly analyzed with generalized linear mixed-effects models (GLMMs), which have interpretable parameters. Baye…
Meta-learning as a principle for human-like visual representations
Can Demircan, Marcel Binz, Alireza Modirshanechi +1
The structure of human visual representations underpins our capacity for adaptive behaviour. While pretrained neural networks model human visual representations with unprecedented…
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq +76
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, w…
Toward an automated science of the mind
Akshay K. Jagadish, Milena Rmus, Kristin Witte +3
Artificial Intelligence is starting to automate scientific discovery, raising the prospect of an automated science of the mind. We chart this field around four challenges: represen…
metabeta -- A fast neural model for Bayesian mixed-effects regression
Alex Kipnis, Marcel Binz, Eric Schulz
Hierarchical data with multiple observations per group is ubiquitous in empirical sciences and is often analyzed using mixed-effects regression. In such models, Bayesian inference…
Meta-learning ecological priors from large language models explains human learning and decision making
Akshay K. Jagadish, Mirko Thalmann, Julian Coda-Forno +2
Human cognition is profoundly shaped by the environments in which it unfolds. Yet, it remains an open question whether learning and decision making can be explained as a principled…