68 citations · 175 across the 10 of their papers we have counts for
10 papers
Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset
Arun Tejasvi Chaganty, Megan Leszczynski, Shu Zhang +3
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).…
Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation
Megan Leszczynski, Shu Zhang, Ravi Ganti +4
Recommender systems are ubiquitous yet often difficult for users to control, and adjust if recommendation quality is poor. This has motivated conversational recommender systems (CR…
TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval
Megan Leszczynski, Daniel Y. Fu, Mayee F. Chen +1
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. However, state-of-the-ar…
Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text
Maya Varma, Laurel Orr, Sen Wu +3
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 ent…
Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems
Laurel Orr, Atindriyo Sanyal, Xiao Ling +2
The industrial machine learning pipeline requires iterating on model features, training and deploying models, and monitoring deployed models at scale. Feature stores were developed…
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps
Tri Dao, Nimit S. Sohoni, Albert Gu +5
Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference…
Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation
Laurel Orr, Megan Leszczynski, Simran Arora +4
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 t…
Understanding the Downstream Instability of Word Embeddings
Megan Leszczynski, Avner May, Jian Zhang +3
Many industrial machine learning (ML) systems require frequent retraining to keep up-to-date with constantly changing data. This retraining exacerbates a large challenge facing ML…
Low-Memory Neural Network Training: A Technical Report
Nimit S. Sohoni, Christopher R. Aberger, Megan Leszczynski +2
Memory is increasingly often the bottleneck when training neural network models. Despite this, techniques to lower the overall memory requirements of training have been less widely…
High-Accuracy Low-Precision Training
Christopher De Sa, Megan Leszczynski, Jian Zhang +4
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. Still, it has b…