Portrait of David Scott Krueger

David Scott Krueger

Core Academic Member
kruegerd@mila.quebec
Canada CIFAR AI Chair
Assistant professor, Université de Montréal, Department of Computer Science and Operations Research (DIRO)
Alumni
Research Topics
Deep Learning
Representation Learning
Website
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Biography

David Krueger is an Assistant Professor in Robust, Reasoning and Responsible AI in the Department of Computer Science and Operations Research (DIRO) at University of Montreal, and a Core Academic Member at Mila - Quebec Artificial Intelligence Institute, UC Berkeley's Center for Human-Compatible AI (CHAI), and the Center for the Study of Existential Risk (CSER). His work focuses on reducing the risk of human extinction from artificial intelligence (AI x-risk) through technical research as well as education, outreach, governance and advocacy.

His research spans many areas of Deep Learning, AI Alignment, AI Safety and AI Ethics, including alignment failure modes, algorithmic manipulation, interpretability, robustness, and understanding how AI systems learn and generalize. He has been featured in media outlets including ITV's Good Morning Britain, Al Jazeera's Inside Story, France 24, New Scientist and the Associated Press.

David completed his graduate studies at the University of Montreal and Mila - Quebec Artificial Intelligence Institute, working with Yoshua Bengio, Roland Memisevic, and Aaron Courville.

Publications

Facilitating Multimodality in Normalizing Flows
Chin-wei Huang
David Scott Krueger
Aaron Courville
The true Bayesian posterior of a model such as a neural network may be highly multimodal. In principle, normalizing flows can represent such… (see more) a distribution via compositions of invertible transformations of random noise. In practice, however, existing normalizing flows may fail to capture most of the modes of a distribution. We argue that the conditionally affine structure of the transformations used in [Dinh et al., 2014, 2016, Kingma et al., 2016] is inefficient, and show that flows which instead use (conditional) invertible non-linear transformations naturally enable multimodality in their output distributions. With just two layers of our proposed deep sigmoidal flow, we are able to model complicated 2d energy functions with much higher fidelity than six layers of deep affine flows.
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
David Scott Krueger
Tegan Maharaj
J'anos Kram'ar
Mohammad Pezeshki
Nicolas Ballas
Nan Rosemary Ke
Anirudh Goyal
Yoshua Bengio
Hugo Larochelle
Aaron Courville
Chris Pal
We propose zoneout, a novel method for regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain thei… (see more)r previous values. Like dropout, zoneout uses random noise to train a pseudo-ensemble, improving generalization. But by preserving instead of dropping hidden units, gradient information and state information are more readily propagated through time, as in feedforward stochastic depth networks. We perform an empirical investigation of various RNN regularizers, and find that zoneout gives significant performance improvements across tasks. We achieve competitive results with relatively simple models in character- and word-level language modelling on the Penn Treebank and Text8 datasets, and combining with recurrent batch normalization yields state-of-the-art results on permuted sequential MNIST.
2016-06-03
ArXiv (preprint)
arxiv.org
arxiv.org