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Fall 2024
This thesis presents a novel data-driven approach for identifying categoryselective regions in the human brain that are consistent across multiple participants. By leveraging a massive fMRI dataset and a multi-modal (language and image) neural network (CLIP), we trained a highly accurate...
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Fall 2022
Convolution Neural Networks (CNNs) have rapidly evolved since their neuroscience beginnings. These models efficiently and accurately classify images by optimizing the model’s hidden representations to these images through training. These representa- tions have been shown to resemble neural data...
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Insights into Early Word Comprehension - Tracking the Neural Representations of Word Semantics in Infants
DownloadSpring 2022
Infants start developing rudimentary language skills and can start understanding simple words well before their first birthday. This development has also been shown primarily using Event Related Potential (ERP) techniques to find evidence of word comprehension in the infant brain. While these...
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The Contrastive Gap: A New Perspective on the ‘Modality Gap’ in Multimodal Contrastive Learning
DownloadFall 2024
Learning jointly from images and texts using contrastive pre-training has emerged as an effective method to train large-scale models with a strong grasp of semantic image concepts. For instance, CLIP, pre-trained on a large corpus of web data, excels in tasks like zero-shot image classification,...