Search Results for author:
Found 12 papers, 3 papers with code
Identifying and interpreting non-aligned human conceptual representations using language modeling
In applying this method, we show that congenital blindness induces conceptual reorganization in both a-modal and sensory-related verbal domains, and we identify the associated semantic shifts.
Reconciling Shared versus Context-Specific Information in a Neural Network Model of Latent Causes
Through learning, it naturally stores structure that is shared across tasks in the network weights.
Enhancing Interpretability using Human Similarity Judgements to Prune Word Embeddings
Focusing on word embeddings, we present a supervised-learning method that, for a given domain (e. g., sports, professions), identifies a subset of model features that strongly improve prediction of human similarity judgments.
The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models
Our results reveal a connection between human language processing and DLMs, with the DLM's layer-by-layer accumulation of contextual information mirroring the timing of neural activity in high-order language areas.
A Data-Efficient Mutual Information Neural Estimator for Statistical Dependency Testing
Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications.
Data-Efficient Mutual Information Neural Estimator
We demonstrate the effectiveness of our estimators on synthetic benchmarks and a real world fMRI data, with application of inter-subject correlation analysis.
Shared Representational Geometry Across Neural Networks
Different neural networks trained on the same dataset often learn similar input-output mappings with very different weights.
Mapping Between fMRI Responses to Movies and their Natural Language Annotations
Several research groups have shown how to correlate fMRI responses to the meanings of presented stimuli.
A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis
In this work, we examine a searchlight based shared response model to identify shared information in small contiguous regions (searchlights) across the whole brain.
A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation
We examine two ways to combine the ideas of a factor model and a searchlight based analysis to aggregate multi-subject fMRI data while preserving spatial locality.
A Reduced-Dimension fMRI Shared Response Model
Multi-subject fMRI data is critical for evaluating the generality and validity of findings across subjects, and its effective utilization helps improve analysis sensitivity.
Boosting with Spatial Regularization
We prove that the proposed algorithm exhibits a ``grouping effect, which encourages the selection of all spatially local, discriminative base classifiers.
