Lightning

By Marilynn Shirley Stratton. 2018

It seems, to Rose - that when it comes to lightning - grown-ups expect children to swallow some pretty farfetched stories. How lightning peeled the varnish right off the cedar fence, right beside a hydro pole, practically on top of where Dad was standing at the time.

Subfields: ML Systems & Hardware

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In ML Systems & Hardware

1. Adrian Daniel Popescu
2. Eden Belouadah
3. Joelle Pineau
4. Fabio Cuzzolin
5. Ryan J. Lowe
6. Jasmine Wang
7. Davide Bacciu
8. Prasanna Parthasarathi

Citations in the corpus

• Resolution-robust Large Mask Inpainting with Fourier Convolutions (2021, methodology): “The experimentation pipeline is implemented using PyTorch [33], PyTorch-Lightning [9], and Hydra [49].”
• NeMo: a toolkit for building AI applications using Neural Modules (2019, background): “…loosely classified into two main groups: (1) higher-level neural network APIs such as Keras[11], Sonnet [18], PyTorch Ignite [3], PyTorch Lightning [4] and (2) configuration-driven tookits such as Tensor2Tensor [20], Ludwig [5], OpenSeq2Seq [13], FairSeq [16], OpenNMT [12], Seq2Seq [9] and many…”
• Avalanche: an End-to-End Library for Continual Learning (2021, background): “Another prominent example of a collection of base-line training strategies and pretrained models is the natu-ral language processing transformers library by Hugging Face [55].”
• Learning an Unreferenced Metric for Online Dialogue Evaluation (2020, methodology): “We would also like to thank William Falcon and the entire Pytorch Lightning community for making research code awesome.”