22 citations · 69 across the 7 of their papers we have counts for
7 papers
The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models
Ariel Goldstein, Eric Ham, Mariano Schain +17
Deep Language Models (DLMs) provide a novel computational paradigm for understanding the mechanisms of natural language processing in the human brain. Unlike traditional psycholing…
Data-Efficient Mutual Information Neural Estimator
Xiao Lin, Indranil Sur, Samuel A. Nastase +3
Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications. Recent work, MINE (B…
Shared Representational Geometry Across Neural Networks
Qihong Lu, Po-Hsuan Chen, Jonathan W. Pillow +3
Different neural networks trained on the same dataset often learn similar input-output mappings with very different weights. Is there some correspondence between these neural netwo…
A combinatorial framework to quantify peak/pit asymmetries in complex dynamics
Uri Hasson, Jacopo Iacovacci, Ben Davis +4
We explore a combinatorial framework which efficiently quantifies the asymmetries between minima and maxima in local fluctuations of time series. We firstly showcase its performanc…
Mapping Between fMRI Responses to Movies and their Natural Language Annotations
Kiran Vodrahalli, Po-Hsuan Chen, Yingyu Liang +8
Several research groups have shown how to correlate fMRI responses to the meanings of presented stimuli. This paper presents new methods for doing so when only a natural language a…
A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis
Hejia Zhang, Po-Hsuan Chen, Janice Chen +5
There is a growing interest in joint multi-subject fMRI analysis. The challenge of such analysis comes from inherent anatomical and functional variability across subjects. One appr…