The future is hard to anticipate! In our latest #CVPR2021 paper, we introduce a framework for learning *what* is predictable in the future.
Rather than committing up front to categories to predict, our approach learns how to hedge the bet.
hyperfuture.cs.columbia.edu
Learning unsupervised machine translation is easier if you open your eyes!
Image distributions create transitive relations between languages. This creates incidental supervision for learning multilingual representations on 50 unpaired languages
arxiv.org/pdf/2012.04631…@Surisdi
What causes adversarial examples? Latest #ECCV2020 paper from @ChengzhiM and Amogh shows that deep networks are vulnerable partly because they are trained on too few tasks. Just by increasing tasks, we strengthen robustness for each task individually. arxiv.org/pdf/2007.07236…
Oops! Dave+Bo introduce a dataset of unconstrained videos showing unintentional action. We study self-supervised approaches for learning video representations of intentionality.
#CVPR2020 Poster 93, Tue 10am PST
Website: oops.cs.columbia.edu
Paper: arxiv.org/abs/1911.11206