Papers by Geoffrey Hinton

2 papers
Meta-Learning Fast Weight Language Models (2022.emnlp-main)

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Challenge: Dynamic evaluation of language models (LMs) adapts model parameters at test time using gradient information from previous tokens.
Approach: They propose a neural component that uses gradient updates as linear attention to improve model performance.
Outcome: The proposed model can be applied at training time and learn to make good use of gradient updates.
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search (P18-1)

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Challenge: a large-scale lookup operation to ground language via ‘snapshots’ of our physical world accessed through image search is currently used to learn word representations.
Approach: They propose a large-scale lookup operation to ground language via ‘snapshots’ of our physical world accessed through image search.
Outcome: The proposed model is based on a large-scale lookup operation to ground language using image search.

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