Accounting for Variance in Machine Learning Benchmarks

no code implementations • 1 Mar 2021 • Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent

Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices.

Benchmarking BIG-bench Machine Learning +1

Towards the Latent Transcriptome

1 code implementation • 8 Oct 2018 • Assya Trofimov, Francis Dutil, Claude Perreault, Sebastien Lemieux, Yoshua Bengio, Joseph Paul Cohen

In this work we propose a method to compute continuous embeddings for kmers from raw RNA-seq data, without the need for alignment to a reference genome.