Search Results for author:
Found 6 papers, 2 papers with code
Revealing Vision-Language Integration in the Brain with Multimodal Networks
We operationalize sites of multimodal integration as regions where a multimodal vision-language model predicts recordings better than unimodal language, unimodal vision, or linearly-integrated language-vision models.
Population Transformer: Learning Population-level Representations of Intracranial Activity
We present a self-supervised framework that learns population-level codes for intracranial neural recordings at scale, unlocking the benefits of representation learning for a key neuroscience recording modality.
Operator learning for hyperbolic partial differential equations
We construct the first rigorously justified probabilistic algorithm for recovering the solution operator of a hyperbolic partial differential equation (PDE) in two variables from input-output training pairs.
BrainBERT: Self-supervised representation learning for intracranial recordings
We create a reusable Transformer, BrainBERT, for intracranial recordings bringing modern representation learning approaches to neuroscience.
Learning a natural-language to LTL executable semantic parser for grounded robotics
We take a step toward robots that can do the same by training a grounded semantic parser, which discovers latent linguistic representations that can be used for the execution of natural-language commands.
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Although we focus on object recognition here, data with controls can be gathered at scale using automated tools throughout machine learning to generate datasets that exercise models in new ways thus providing valuable feedback to researchers.
Ranked #51 on
Image Classification
on ObjectNet
(using extra training data)
