Luka Rimanic | DeepAI

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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 ...

1 Jue Wang, et al. ∙

∙ 04/04/2022

SHiFT: An Efficient, Flexible Search Engine for Transfer Learning

Transfer learning can be seen as a data- and compute-efficient alternati...

21 Cedric Renggli, et al. ∙

∙ 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 ...

0 Cedric Renggli, et al. ∙

∙ 06/11/2021

Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks

Despite the great successes achieved by deep neural networks (DNNs), rec...

0 Nezihe Merve Gürel, et al. ∙

∙ 03/20/2021

DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation

Recent success of deep neural networks (DNNs) hinges on the availability...

0 Boxin Wang, et al. ∙

∙ 02/15/2021

A Data Quality-Driven View of MLOps

Developing machine learning models can be seen as a process similar to t...

0 Cedric Renggli, et al. ∙

∙ 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...

0 Cedric Renggli, et al. ∙

∙ 10/15/2020

On Convergence of Nearest Neighbor Classifiers over Feature Transformations

The k-Nearest Neighbors (kNN) classifier is a fundamental non-parametric...

13 Luka Rimanic, et al. ∙

∙ 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...

19 Cedric Renggli, et al. ∙

∙ 02/27/2020

Provable Robust Learning Based on Transformation-Specific Smoothing

As machine learning systems become pervasive, safeguarding their securit...

6 Linyi Li, et al. ∙

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