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Christopher Wang

7 accepted papers

2025

Population Transformer: Learning Population-level Representations of Neural Activity

ICLR 2025oral

We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address key challenges in scaling models with neural time-series data, namely, sparse and variable electrode distribution across subjects and datasets. The Population T…

2024

Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli

NeurIPS 2024oral

We present the Brain Treebank, a large-scale dataset of electrophysiological neural responses, recorded from intracranial probes while 10 subjects watched one or more Hollywood movies. Subjects watched on average 2.6 Hollywood movies, for an average viewing time of 4.3 hours, and a total of 43 hours…

Cited by 3SourcePDFScholar
2024

BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?

NeurIPS 2024poster

When evaluating stimuli reconstruction results it is tempting to assume that higher fidelity text and image generation is due to an improved understanding of the brain or more powerful signal extraction from neural recordings. However, in practice, new reconstruction methods could improve performan…

Cited by 0SourcePDFScholar
2024

Revealing Vision-Language Integration in the Brain with Multimodal Networks

ICML 2024poster

We use (multi)modal deep neural networks (DNNs) to probe for sites of multimodal integration in the human brain by predicting stereoencephalography (SEEG) recordings taken while human subjects watched movies. We operationalize sites of multimodal integration as regions where a multimodal vision-lang…

2023

BrainBERT: Self-supervised representation learning for intracranial recordings

ICLR 2023poster

We create a reusable Transformer, BrainBERT, for intracranial recordings bringing modern representation learning approaches to neuroscience. Much like in NLP and speech recognition, this Transformer enables classifying complex concepts, i.e., decoding neural data, with higher accuracy and with much…

2020

Learning a natural-language to LTL executable semantic parser for grounded robotics

CoRL 2020

Children acquire their native language with apparent ease by observing how language is used in context and attempting to use it themselves. They do so without laborious annotations, negative examples, or even direct corrections. We take a step toward robots that can do the same by training a grounde

Cited by 0SourcePDFScholar
2019

ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

NeurIPS 2019poster

We collect a large real-world test set, ObjectNet, for object recognition with controls where object backgrounds, rotations, and imaging viewpoints are random. Most scientific experiments have controls, confounds which are removed from the data, to ensure that subjects cannot perform a task by explo…

Cited by 697SourcePDFScholar