Energy functions and shortcut learning

This week we are joined by Kyunghyun Cho. He is an associate professor of computer science and data science at New York University, a research scientist at Facebook AI Research and a CIFAR Associate Fellow. On top of this he also co-chaired the recent ICLR 2020 virtual conference.

We talk about a variety of topics in this weeks episode including the recent ICLR conference, energy functions, shortcut learning and the roles popularized Deep Learning research areas play in answering the question “What is Intelligence?”.

Underrated ML Twitter: https://twitter.com/underrated_ml

Kyunghyun Cho Twitter: https://twitter.com/kchonyc?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor

Please let us know who you thought presented the most underrated paper in the form below:

https://forms.gle/97MgHvTkXgdB41TC8

Links to the papers:

“Shortcut Learning in Deep Neural Networks” - https://arxiv.org/pdf/2004.07780.pdf

"Bayesian Deep Learning and a Probabilistic Perspective of Generalization” - https://arxiv.org/abs/2002.08791

"Classifier-agnostic saliency map extraction" - https://arxiv.org/abs/1805.08249

“Deep Energy Estimator Networks” - https://arxiv.org/abs/1805.08306

“End-to-End Learning for Structured Prediction Energy Networks” - https://arxiv.org/abs/1703.05667

“On approximating nabla f with neural networks” - https://arxiv.org/abs/1910.12744

“Adversarial NLI: A New Benchmark for Natural Language Understanding“ - https://arxiv.org/abs/1910.14599

“Learning the Difference that Makes a Difference with Counterfactually-Augmented Data” - https://arxiv.org/abs/1909.12434

“Learning Concepts with Energy Functions” - https://openai.com/blog/learning-concepts-with-energy-functions/