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Nearest-neighbor matchup effects: accounting for team matchups for predicting March Madness

Published in Journal of Quantitative Analysis in Sports, 2015

We introduce our novel nearest-neighbor matchup effects framework, which presents a flexible way to account for team characteristics above and beyond team strength that may influence game outcomes. The paper was covered by several media outlets, click into the page to see one example.

Recommended citation: Hoegh, A., Carzolio, M., Crandell, I., Hu, X., Roberts, L., Song, Y., & Leman, S. C. (2015). Nearest-neighbor matchup effects: accounting for team matchups for predicting March Madness. Journal of Quantitative Analysis in Sports, 11(1), 29-37.
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Smart Vision-Language Reasoners

Published in ICML 2024, 2024

Images in Math AI Considered harmful? Not quite. This paper demonstrates improved performance on question answering problems in math by customizing the neural network architecture to pool information from both vision and text backbones. The improvements come from a custom QF layer which includes multihead self attention layers as well as a cross attention layer (vision & text). We fine tune the model using the smart-101 dataset presented in CVPR 2023.

Recommended citation: Roberts D, Roberts L. Smart Vision-Language Reasoners. arXiv preprint arXiv:2407.04212. 2024 Jul 5.
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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Talk on algorithmic differentiation of QR factorization

Published: September 17, 2024

This is a talk at the Algorithmic Differentiation 2024 conference.

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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