1 code implementation • 13 Mar 2023 • Arun Tejasvi Chaganty, Megan Leszczynski, Shu Zhang, Ravi Ganti, Krisztian Balog, Filip Radlinski
Users in consumption domains, like music, are often able to more efficiently provide preferences over a set of items (e. g. a playlist or radio) than over single items (e. g. songs).
no code implementations • 27 Jan 2023 • Megan Leszczynski, Shu Zhang, Ravi Ganti, Krisztian Balog, Filip Radlinski, Fernando Pereira, Arun Tejasvi Chaganty
This has motivated conversational recommender systems (CRSs), with control provided through natural language feedback.
1 code implementation • Findings (ACL) 2022 • Megan Leszczynski, Daniel Y. Fu, Mayee F. Chen, Christopher Ré
Entity retrieval--retrieving information about entity mentions in a query--is a key step in open-domain tasks, such as question answering or fact checking.
1 code implementation • Findings (EMNLP) 2021 • Maya Varma, Laurel Orr, Sen Wu, Megan Leszczynski, Xiao Ling, Christopher Ré
Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities.
no code implementations • 11 Aug 2021 • Laurel Orr, Atindriyo Sanyal, Xiao Ling, Karan Goel, Megan Leszczynski
The industrial machine learning pipeline requires iterating on model features, training and deploying models, and monitoring deployed models at scale.
2 code implementations • ICLR 2020 • Tri Dao, Nimit S. Sohoni, Albert Gu, Matthew Eichhorn, Amit Blonder, Megan Leszczynski, Atri Rudra, Christopher Ré
Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference speed and reduce memory usage compared to general linear maps.
1 code implementation • 20 Oct 2020 • Laurel Orr, Megan Leszczynski, Simran Arora, Sen Wu, Neel Guha, Xiao Ling, Christopher Re
A challenge for named entity disambiguation (NED), the task of mapping textual mentions to entities in a knowledge base, is how to disambiguate entities that appear rarely in the training data, termed tail entities.
Ranked #1 on Entity Disambiguation on AIDA-CoNLL (Micro-F1 metric)
1 code implementation • 29 Feb 2020 • Megan Leszczynski, Avner May, Jian Zhang, Sen Wu, Christopher R. Aberger, Christopher Ré
To theoretically explain this tradeoff, we introduce a new measure of embedding instability---the eigenspace instability measure---which we prove bounds the disagreement in downstream predictions introduced by the change in word embeddings.
no code implementations • 24 Apr 2019 • Nimit S. Sohoni, Christopher R. Aberger, Megan Leszczynski, Jian Zhang, Christopher Ré
In this paper we study a fundamental question: How much memory is actually needed to train a neural network?
1 code implementation • 9 Mar 2018 • Christopher De Sa, Megan Leszczynski, Jian Zhang, Alana Marzoev, Christopher R. Aberger, Kunle Olukotun, Christopher Ré
Low-precision computation is often used to lower the time and energy cost of machine learning, and recently hardware accelerators have been developed to support it.