Papers by Jashn Arora
Visio-Linguistic Brain Encoding (2022.coling-1)
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| Challenge: | Existing studies have failed to explore co-attentive multi-modal modeling for visual and text reasoning. |
| Approach: | They propose to use image and multi-modal Transformers to reconstruct fMRI brain activity . they use two popular datasets to study visual and text reasoning . |
| Outcome: | The proposed model outperforms existing models on two popular datasets . the results raise the question whether visual processing is affected implicitly by linguistic processing . |
Multi-view and Cross-view Brain Decoding (2022.coling-1)
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| Challenge: | a recent study has shown that brain decoding models can decode concepts from single view . a multi-view decoder can take brain recordings for any view as input and predict the concept . |
| Approach: | They propose to build a multi-view decoder that can take brain recordings for any view as input and predict the concept. |
| Outcome: | The proposed systems can decode concepts from brain recordings from any view . the proposed systems have 0.68 pairwise accuracy across view pairs and 0.8 average pairwise precision across tasks. |
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity? (2022.naacl-main)
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| Challenge: | Existing literature has focused on pretrainer-based text-driven brain encoding models . however, few studies have explored the efficacy of task-specific learning of Transformers . |
| Approach: | They propose to use ten popular natural language processing tasks to learn Transformer representations for predicting brain responses. |
| Outcome: | The proposed model predicts brain activity across the whole brain. |