Search Results for author: Megan Leszczynski

Found 10 papers, 7 papers with code

Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset

1 code implementation13 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).

Music Recommendation Recommendation Systems +1

TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval

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.

Entity Retrieval Fact Checking +3

Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text

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.

Data Integration Entity Disambiguation

Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems

no code implementations11 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.

Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps

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.

Image Classification speech-recognition +1

Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation

1 code implementation20 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)

Entity Disambiguation Relation Extraction

Understanding the Downstream Instability of Word Embeddings

1 code implementation29 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.

Word Embeddings

High-Accuracy Low-Precision Training

1 code implementation9 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.

Quantization Vocal Bursts Intensity Prediction

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