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06/02/2022
Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees
Communication compression is a crucial technique for modern distributed ...
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04/04/2022
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning
Transfer learning can be seen as a data- and compute-efficient alternati...
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08/30/2021
Evaluating Bayes Error Estimators on Read-World Datasets with FeeBee
The Bayes error rate (BER) is a fundamental concept in machine learning ...
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06/11/2021
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks
Despite the great successes achieved by deep neural networks (DNNs), rec...
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03/20/2021
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
Recent success of deep neural networks (DNNs) hinges on the availability...
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02/15/2021
A Data Quality-Driven View of MLOps
Developing machine learning models can be seen as a process similar to t...
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10/16/2020
On Automatic Feasibility Study for Machine Learning Application Development with ease.ml/snoopy
In our experience working with domain experts who are using today's Auto...
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10/15/2020
On Convergence of Nearest Neighbor Classifiers over Feature Transformations
The k-Nearest Neighbors (kNN) classifier is a fundamental non-parametric...
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10/13/2020
Which Model to Transfer? Finding the Needle in the Growing Haystack
Transfer learning has been recently popularized as a data-efficient alte...
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02/27/2020
