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Upcoming Seminars
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May 7: Beyond Data and Model Parallelism for Deep Neural Networks
Date: May 7 at 3pm
Speaker: Zhihao Jia
Abstract: Existing deep learning systems parallelize the training process of deep neural networks (DNNs) by using simple strategies, such as data and model parallelism, which usually results in suboptimal parallelization performance for large scale training. In this talk, I will first formalize the space of all possible parallelization strategies for training DNNs. After that, I will present FlexFlow, a deep learning framework that automatically finds efficient parallelization strategies by using a guided random search algorithm to explore the space of all possible parallelization strategies. Finally, I will show that FlexFlow significantly outperforms state-of-the-art parallelization approaches by increasing training throughput, reducing communication costs, and achieving improved scalability.
May 21: Machine learning applications for hospitals
Date: May 21 at 3pm
Speaker: David Scheinker
Abstract: Academic hospitals and particle accelerators have a lot in common. Both are complex organizations; employ numerous staff and scientists; deliver a variety of services; research how to improve the delivery of those services; and do it all with a variety of large expensive machines. My group focuses on helping the Stanford hospitals, mostly the Children's Hospital, seek to improve: throughput, decision-support, resource management, innovation, and education. I'll present brief overviews of a variety of ML-based approaches to projects in each of these areas. For example, integer programming to optimize surgical scheduling and Neural Networks to interpret continuous-time waveform monitor data. I will conclude with a broader vision for how modern analytics methodology could potentially transform healthcare delivery. More information on the projects to be discussed is available at surf.stanford.edu/projects
June 4: Machine learning at LLNL (tentative)
Date: June 4, 3pm
Speaker: Brian Spears (LLNL)
Abstract: TBD
TBD: On analyzing urban form at global scale with remote sensing data and generative adversarial networks
Date: TBD
Speaker: Adrian Albert
Abstract: Current analyses of urban development use either simple, bottom-up models, that have limited predictive performance, or highly engineered, complex models relying on many sources of survey data that are typically scarce and difficult and expensive to collect. This talk presents work-in-progress developing a data-driven, flexible, non-parametric framework to simulate realistic urban forms using generative adversarial networks and planetary-level remote-sensing data. To train our urban simulator, we curate and put forth a new dataset on urban form, integrating spatial distribution maps of population, nighttime luminosity, and built land densities, as well as best-available information on city administrative boundaries for 30,000 of the world's largest cities. This is the first analysis to date of urban form using modern generative models and remote-sensing data.
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