Deep learning can still be a complex mix of art and engineering despite its tremendous success in recent years, and there is still progress to be made before it has fully evolved into a mature scientific discipline. The interdependence of architecture, data, and optimization gives rise to an enormous landscape of design and performance intricacies that are not well-understood. The evolution from engineering towards science in deep learning can be achieved by pushing the disciplinary boundaries. Unlike in the natural and physical sciences -- where experimental capabilities can hamper progress, i.e. limitations in what quantities can be probed and measured in physical systems, how much and how often -- in deep learning the vast majority of relevant quantities that we wish to measure can be tracked in some way. As such, a greater limiting factor towards scientific understanding and principled design in deep learning is how to insightfully harness the tremendous collective experimental capability of the field. As a community, some primary aims would be to (i) identify obstacles to better models and algorithms, (ii) identify the general trends that are potentially important which we wish to understand scientifically and potentially theoretically and; (iii) careful design of scientific experiments whose purpose is to clearly resolve and pinpoint the origin of mysteries (so-called 'smoking-gun' experiments).
Author Information
Levent Sagun (EPFL)
CAGLAR Gulcehre (Deepmind)
Adriana Romero (FAIR)
Negar Rostamzadeh (Element AI)
Bio: Negar Rostamzadeh has received her bachelor degree in Computer Science from the University of Tehran in 2010. She started her PhD in 2011 at the Mhug (Multimedia and Human understanding) group, University of Trento, Italy. There she did research under the direction of Prof. Nicu Sebe. She worked as a research intern at the MMV (Multimedia and Vision) lab at the Queen Mary University of London (May-September 2013), where she was supervised by Prof. Yiannis Patras. From Feb. 2015 to May 2016 Negar was working on Video Captioning and Video Classification through deep learning approaches in MILA (Montreal Institude of Learning Algorithem) lab, which is founded by Prof. Yoshua Bengio. There she was supervised by Prof. Aaron Courville. From early Jun, she had an internship in the Machine Intelligence team at Google in Seattle during Summer 2017. Her areas of interests are Machine Learning (particularity deep learning approaches ) applied to Multimedia problems (mainly video classification/detection and captioning).
Nando de Freitas (DeepMind)
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2019 Poster: Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias »
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2018 Spotlight: Playing hard exploration games by watching YouTube »
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2017 Poster: Robust Imitation of Diverse Behaviors »
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2017 Tutorial: Deep Learning: Practice and Trends »
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2016 Workshop: Neural Abstract Machines & Program Induction »
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2016 Poster: Learning to learn by gradient descent by gradient descent »
Marcin Andrychowicz · Misha Denil · Sergio Gómez · Matthew Hoffman · David Pfau · Tom Schaul · Nando de Freitas -
2015 Workshop: Bayesian Optimization: Scalability and Flexibility »
Bobak Shahriari · Ryan Adams · Nando de Freitas · Amar Shah · Roberto Calandra
