Upcoming Events

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Events Archive

Princeton Research Day

To keep everyone safe and support social distancing, Princeton Research Day will not be held in person this year. We are exploring other possibilities with input from the campus community, and expect to announce those decisions by early April.

Dinner with a Professor

More information forthcoming on this event.

For an article on the previous year's dinner, read here.

Location: Prospect House

FPGA Training with Intel

Research Computing recently installed four Intel FPGAs on the Della cluster. After attending this workshop you should have the skills needed to start using these devices.
Location: 120 Lewis Science Library
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NVIDIA Workshop: Fundamentals of Deep Learning for Computer Vision

Learn deep learning techniques for a range of computer vision tasks, including training and deploying neural networks.
Location: 120 Lewis Science Library

Running and Analyzing Large-scale Psychology Experiments

Psychology has traditionally been a laboratory discipline, focused on small-scale experiments conducted in person. However, recent technological innovations have made it possible to collect far more data from far more people than ever before.

Location: 399 Julis Romo Rabinowitz
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Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

We introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system, given by an ordinary differential equation (ODE), from observed state trajectories.
Location: CSML Classroom 103
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Foundations of Deep Learning with PyTorch

Of the many deep learning frameworks, PyTorch has largely emerged as the first choice for researchers. This workshop will show participants how to implement and train common network architectures in PyTorch. Special topics will be included as time permits. Participants should have some knowledge of Python, NumPy and deep learning theory.

Location: 138 Lewis Science Library
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Preference Modeling with Context-Dependent Salient Features

This talk considers the preference modeling problem and addresses the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is that two items compared in isolation from other items may be compared based on only a salient subset of features.

Location: 214 Fine Hall
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Massively Parallel Evolutionary Computation for Empowering Electoral Reform

Important insights into redistricting can be gained by formulating and analyzing the problem with a Markov Chain Monte Carlo framework that utilizes optimization heuristics to inform transition proposals.
Location: Corwin Hall 127
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Deep convolutional neural networks for multi-scale time-series classification in fusion devices

The multi-scale, mutli-physics nature of fusion plasmas makes predicting plasma events challenging. Recent advances in deep convolutional neural network architectures (CNN) utilizing dilated convolutions enable accurate predictions on sequences which have long-range, multi-scale characteristics, such as the time-series generated by diagnostic...

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