Optimization Inspired Deep Architectures for Multiview 3D

Graphics Processing Units (GPUs) offer high performance and massive parallelization, but learning how to program GPUs for scientific applications can be daunting.
The Princeton Institute for Computational Science & Engineering (PICSciE) and OIT Research Computing , along with the Center for Statistics and Machine Learning(link is external), are announcing a two-week Research Computing Bootcamp held virtually during Winter Break, from January 19-29, 2021.
A proliferation of emerging data science applications require efficient extraction of information from complex data. The unprecedented scale of relevant features, however, often overwhelms the volume of available samples, which dramatically complicates statistical inference and decision making. In this talk, we present two vignettes on how to improve sample efficiency in high-dimensional statistical problems.
Machine Learning methods are extremely powerful but often function as black-box problem solvers, providing improved performance at the expense of clarity. Our work describes a new machine learning approach which translates the strategy of a deep neural network into simple functions that are meaningful and intelligible to the physicist, without sacrificing performance improvements. We apply this approach to benchmark high-energy problems of fat-jet classification and electron identification.
The annual CSML Poster Session event will be held in person or virtually. Watch this space for further details.
Due date for independent work posters and papers TBA. Please check your email for details.
Check out this article on 2020's poster session here.
Computational analyses are playing an increasingly central role in research. However, many researchers have not received training in best practices and tools for reproducibly managing and sharing their code and data. This is a step-by-step, practical workshop on managing your research code and data for computationally reproducible collaboration. The workshop starts with some brief introductory information about computational reproducibility, but the bulk of the workshop is guided work with code and data.