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Nicholas Hynes

Researcher at Massachusetts Institute of Technology

Publications -  11
Citations -  1285

Nicholas Hynes is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Game theory & Valuation (finance). The author has an hindex of 9, co-authored 11 publications receiving 822 citations. Previous affiliations of Nicholas Hynes include University of California, Berkeley.

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Proceedings ArticleDOI

Learning Cross-Modal Embeddings for Cooking Recipes and Food Images

TL;DR: This paper introduces Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes and 800k food images, and demonstrates that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic.
Proceedings ArticleDOI

Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution

TL;DR: Ekiden as mentioned in this paper is a system that combines blockchains with Trusted Execution Environments (TEEs), and leverages a novel architecture that separates consensus from execution, enabling efficient TEE-backed confidentiality-preserving smart-contracts and high scalability.
Posted Content

Towards Efficient Data Valuation Based on the Shapley Value.

TL;DR: This paper proposes a repertoire of efficient algorithms for approximating the Shapley value, a popular notion of value which originated in coopoerative game theory and demonstrates the value of each training instance for various benchmark datasets.
Journal ArticleDOI

Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images

TL;DR: The Recipe1M+ dataset as mentioned in this paper is a large-scale, structured corpus of over one million cooking recipes and 13 million food images, which enables the ability to train high-capacity models on aligned, multimodal data.
Proceedings Article

Towards Efficient Data Valuation Based on the Shapley Value

TL;DR: In this article, the authors study the problem of ''how much is my data worth'' by utilizing the Shapley value, a popular notion of value which originated in co-operative game theory.