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Decoding EEG Brain Activity for Multi-Modal Natural Language Processing
Until recently, human behavioral data from reading has mainly been of in...
02/17/2021 ∙ by Nora Hollenstein, et al. ∙
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A Data Quality-Driven View of MLOps
Developing machine learning models can be seen as a process similar to t...
02/15/2021 ∙ by Cèdric Renggli, et al. ∙
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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...
10/16/2020 ∙ by Cèdric Renggli, et al. ∙
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On Convergence of Nearest Neighbor Classifiers over Feature Transformations
The k-Nearest Neighbors (kNN) classifier is a fundamental non-parametric...
10/15/2020 ∙ by Luka Rimanic, et al. ∙
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Which Model to Transfer? Finding the Needle in the Growing Haystack
Transfer learning has been recently popularized as a data-efficient alte...
10/13/2020 ∙ by Cèdric Renggli, et al. ∙
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Scalable Transfer Learning with Expert Models
Transfer of pre-trained representations can improve sample efficiency an...
09/28/2020 ∙ by Joan Puigcerver, et al. ∙
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Lossy Image Compression with Recurrent Neural Networks: from Human Perceived Visual Quality to Classification Accuracy
Deep neural networks have recently advanced the state-of-the-art in imag...
10/08/2019 ∙ by Maurice Weber, et al. ∙
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Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment
Continuous integration is an indispensable step of modern software engin...
03/01/2019 ∙ by Cèdric Renggli, et al. ∙
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Distributed Learning over Unreliable Networks
Most of today's distributed machine learning systems assume reliable ne...
10/17/2018 ∙ by Hanlin Tang, et al. ∙
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The Convergence of Sparsified Gradient Methods
Distributed training of massive machine learning models, in particular d...
09/27/2018 ∙ by Dan Alistarh, et al. ∙
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SparCML: High-Performance Sparse Communication for Machine Learning
One of the main drivers behind the rapid recent advances in machine lear...
02/22/2018 ∙ by Cèdric Renggli, et al. ∙
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