82 citations · 320 across the 14 of their papers we have counts for
7 papers · 1 filter
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers
Albert Gu, Isys Johnson, Karan Goel +4
Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with uniqu…
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…
Declarative Machine Learning Systems
Piero Molino, Christopher Ré
In the last years machine learning (ML) has moved from a academic endeavor to a pervasive technology adopted in almost every aspect of computing. ML-powered products are now embedd…
Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins
Sahaana Suri, Ihab F. Ilyas, Christopher Ré +1
Structured data, or data that adheres to a pre-defined schema, can suffer from fragmented context: information describing a single entity can be scattered across multiple datasets…
Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation
Mayee F. Chen, Benjamin Cohen-Wang, Stephen Mussmann +2
Labeling data for modern machine learning is expensive and time-consuming. Latent variable models can be used to infer labels from weaker, easier-to-acquire sources operating on un…
Rethinking Neural Operations for Diverse Tasks
Nicholas Roberts, Mikhail Khodak, Tri Dao +3
An important goal of AutoML is to automate-away the design of neural networks on new tasks in under-explored domains. Motivated by this goal, we study the problem of enabling users…