Papers by Nikhil Ghosh

2 papers
On the Benefits of Learning to Route in Mixture-of-Experts Models (2023.emnlp-main)

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Challenge: Existing Mixture-of-Expert (MoE) models allow us to scale up model sizes while keeping the amount of compute time fixed.
Approach: They propose to use a router to route inputs to experts in a layer to scale up model sizes while keeping the amount of compute time fixed.
Outcome: The proposed model scales up with the help of a router that routes input tokens to experts in a layer and shows that it is more efficient than a non-trainable router.
Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles (2024.lrec-main)

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Challenge: Existing methods for cross-document coreference resolution do not provide images for all mentions of events.
Approach: They propose a multimodal cross-document event coreference resolution method that integrates visual and textual cues with a simple linear map between vision and language models.
Outcome: The proposed method improves on a popular ECB+ and AIDA datasets.

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