When: 9am PT, noon ET, 6pm CET
How: http://MLclub.net
Who:
Title: “Will Machine Learning Solve the Analysis of Stellar Spectroscopy?”
Abstract: An online debate on the benefits and downsides that Machine Learning can play in stellar spectroscopy.
Abstract: The esteemed organizers of ML Club for Astronomy will discuss their perspectives on the positive and negative impacts of deep learning algorithms in Astronomy. Questions from the audience will be entertained.
Presenter: J. Xavier Prochaska (UC Santa Cruz)
Title: A Deep-ish Dive into CapsNets
See mlclub.net for further details.
Bio: Ting Chen is a research scientist in the Google Brain Team. His main research interest is on representation learning, discrete structures, and generative modeling. He joined Google in 2019 after finishing his PhD at University of California, Los Angeles.
Held Jointly with the Astrophysics Machine Learning Club — mlclub.net
When/where: May 6, 2020 at 9am PT; Zoom only — https://ucsc.zoom.us/j/272379932
Talk 1:
Title: Quasar continua predictions with neural spline flows
Speaker: David Reiman (UCSC)
Talk 2:
Title: QuasarNET
Speaker: James Farr (UCL)
Held Jointly with the Astrophysics Machine Learning Club — mlclub.net
When/where: April 8, 2020 at 12pm PT; Zoom only — https://ucsc.zoom.us/j/611217103
Title: Neural Networks with Euclidean Symmetry for Physical Sciences
Speaker: Tess E. Schmidt (LBNL)
Abstract: Neural networks are built for specific data types and assumptions about those data types are built into the operations of the neural network. For example, full-connected layers assume vector inputs are made of independent components and 2D convolutional layers assume that the same features can occur anywhere in an image. In this talk, I show how to build neural networks for the data types of physical systems, geometry and geometric tensors, which transform predictably under rotation, translation, and inversion — the symmetries of 3D Euclidean space. This is traditionally a challenging representation to use with neural networks because coordinates are sensitive to 3D rotations and translations and there is no canonical orientation for physical systems. I present a general neural network architecture that naturally handles 3D geometry and operates on the scalar, vector, and tensor fields that characterize physical systems. Our networks are locally equivariant to 3D rotations and translations at every layer. In this talk, I describe how the network achieves these equivariances and demonstrate the capabilities of our network using simple tasks. I also present applications of Euclidean neural networks to quantum chemistry and geometry generation using our Euclidean equivariant learning framework, e3nn.
When/where: March 18, 2020 at 12pm PT; Zoom only — https://ucsc.zoom.us/j/562952785
Title: Deep Learning for Predicting Domain Prices
Speaker: Jason Ansel, Distinguished Engineer at GoDaddy
Learn how GoDaddy uses neural networks to predict the price of a
domain name in the aftermarket. GoDaddy Domain Appraisals (GoValue) is
available to millions of GoDaddy customers and provides estimated
values to help both buyers and sellers more effectively price domain
names. GoValue is 1.25x better at predicting past domain name sale
prices than human experts.
This talk will explain the hybrid recurrent neural networks behind
GoValue. It will discuss some of the practical aspects of scaling and
deploying a sophisticated machine learning system. Finally, we will
dive into recent research at GoDaddy that created a new neural network
structure for outputting tighter prediction intervals than preexisting
techniques.
Try GoDaddy Domain Appraisals for yourself:
https://www.godaddy.com/domain-value-appraisal