82 citations · 320 across the 14 of their papers we have counts for
6 papers · 1 filter
Domino: Discovering Systematic Errors with Cross-Modal Embeddings
Sabri Eyuboglu, Maya Varma, Khaled Saab +5
Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly…
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…
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…
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…
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
Karan Goel, Albert Gu, Yixuan Li +1
Classifiers in machine learning are often brittle when deployed. Particularly concerning are models with inconsistent performance on specific subgroups of a class, e.g., exhibiting…