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Dustin Tran

6 Dec, 4 tweets, 2 min read

@latentjasper

Tomorrow @latentjasper @balajiln and I present a #NeurIPS2020 tutorial on "Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning". Whether you're new to the area or an expert, there is critically useful info! 8-10:30a PT nips.cc/virtual/2020/t…

The talk is split into three sections: 1. Why Uncertainty & Robustness; 2. Foundations; and 3. Recent Advances.

Tutorials do _not_ need registration to attend!

See everyone at the conference!

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Dustin Tran

@dustinvtran

7 Dec

@balajiln

Snippet 1 from the #NeurIPS2020 tutorial: @balajiln What do we mean by uncertainty and out-of-distribution robustness? nips.cc/virtual/2020/t…

Snippet 2: On even simple benchmarks, neural networks are not only poor at generalizing to OOD but also degrade in their uncertainty estimates.

Snippet 3: There are a number of applications where uncertain & robust methods are already being used. It's at the heart of many AI & ML areas.

Read 27 tweets

Dustin Tran

@dustinvtran

7 Sep

How I spent this weekend: upgrading my battlestation.

Cable management is a huge quality of life improvement.

KVM switch also helps for managing personal and work computers.

Read 4 tweets

Dustin Tran

@dustinvtran

13 Dec 19

Check out BatchEnsemble: Efficient Ensembling with Rank 1 Perturbations at the #NeurIPS2019 Bayesian DL workshop. Better accuracies and uncertainty than dropout and competitive with ensembles across a wide range of tasks. 1/-

It’s a drop in replacement for individual layers, like dropout, batchnorm, and variational layers and is available with baselines and Bayesian layers at github.com/google/edward2. 2/-

Unlike ensembles, it’s trained end to end under a single loss function (NLL) and computation can be parallelized across ensemble members in GPU/TPUs. BatchEnsemble is like a new parameterization for neural nets. 3/-

Read 6 tweets

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