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paper "Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data" with BertrandThirion and Gael Varoquaux got accepted to #ICML2019 ! Arxiv: https://arxiv.org/abs/1807.11718# code: https://github.com/sergulaydore/Feature-Grouping-Regularizer

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Erin LeDell: Happy to share our #ICML2019 #AutoML Workshop paper, "An Open Source AutoML Benchmark". We present a new #opensource AutoML benchmarking system and include results on: H2O AutoML, auto-sklearn, TPOT, Auto-WEKA
📰 Paper: https://www.automl.org/wp-content/uploads/2019/06/automlws2019_Paper45.pdf
👩‍💻 Code: https://github.com/openml/automlbenchmark/

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Vithursan Thangarasa
Excited to be presenting my work on "Differentiable Hebbian Plasticity for Continual Learning"
(https://openreview.net/forum?id=r1x-E5Ss34 )
at the #ICML2019 Adaptive and Multi-task Learning workshop. Blog post to my
paper: https://vithursant.com/dhp-softmax/ .

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#ICML2019 live from Long Beach, CA, via icmlconf Learn more
https://mld.ai/icml2019-live #machinelearning #ML #mldcmu #ICML

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deep learning for breast cancer screening at the AI for Social Good Workshop at #ICML2019

Paper: https://arxiv.org/abs/1903.08297
Code: https://github.com/nyukat/breast_cancer_classifier

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Notes from Thirty-sixth International Conference on Machine Learning here:
https://david-abel.github.io/notes/icml_2019.pdf
#ICML2019

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Best paper award at #ICML2019 main idea: unsupervised learning of disentangled representations is fundamentally impossible without inductive biases. Verified theoretically & experimentally.
https://arxiv.org/pdf/1811.12359.pdf

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I'll be sharing 5 Lessons Learned Helping 200,000 non-ML experts* use ML as an #ICML2019 AutoML workshop keynote
https://sites.google.com/view/automl2019icml/schedule?authuser=0

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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations"
http://goo.gle/2IyFqTO
recipient of an #ICML2019 Best Paper Award! Learn more in the blog post at
http://goo.gle/2KaMs48 .

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Best Papers Awards #ICML2019
The Thirty-sixth International Conference on Machine Learning, Long Beach, USA

(1)Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations https://arxiv.org/pdf/1811.12359.pdf
Congratulations to the GoogleAI team of Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly,Bernhard Schölkopf, Olivier Bachem

(2)Rates of Convergence for Sparse Variational Gaussian Process Regression
https://arxiv.org/pdf/1903.03571.pdf
Kudos to David R. Burt, Carl E. Rasmussen, Mark van der Wilk of University of Cambridge

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