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- In mathematics, the Wasserstein or Kantorovich–Rubinstein metric or distance is a distance function defined between probability distributions on a given metric space M {\displaystyle M} M. Intuitively, if each distribution is viewed as a unit amount of "dirt" piled on M {\displaystyle M} M, the metric is the minimum "cost" of turning one pile into the other, which is assumed to be the amount of dirt that needs to be moved times the mean distance it has to be moved. Because of this analogy, the metric is known in computer science as the earth mover's distance.
- Scientists used machine learning to reveal new scientific knowledge hidden in old research papers.
- TLDR — Extractive question answering is an important task for providing a good user experience in many applications. The popular Retriever-Reader framework for QA using BERT can be difficult to scale…
- Can't remember what is precision and recall (sensitivity)? Why accuracy is not enough? Read the explanation with an example with a confusion matrix.
publications
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- The Twelfth International Conference on Learning Representations, (2024)
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- Forty-first International Conference on Machine Learning, (2024)
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- (2023)cite arxiv:2303.12712.
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- Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, New York, NY, USA, Association for Computing Machinery, (2023)
- Advances in Neural Information Processing Systems, 36, page 51991--52008. Curran Associates, Inc., (2023)
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- Proceedings of the first Workshop on Information Extraction from Scientific Publications, page 67--77. Online, Association for Computational Linguistics, (November 2022)
- PLOS ONE, 17 (9): 1-34 (September 2022)
- (2021)cite arxiv:2106.09685Comment: Draft V2 includes better baselines, experiments on GLUE, and more on adapter latency.
