Upcoming Events

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Events Archive

CITP Reading Group: Recommender Systems (RS)

The goal of the recommender systems (RS) reading group is to gain deeper understanding both of seminal work as well as emerging ideas in the field. Papers will include research on RS algorithm development and evaluation; user-centered design and user studies for RS; fairness, accountability, and explainability in recommendations; and societal...

Leveraging Dataset Symmetries in Neural Network Prediction

Scientists and engineers are increasingly applying deep neural networks (DNNs) to modelling and design of complex systems. While the flexibility of DNNs makes them an attractive tool, it also makes their solutions difficult to interpret and their predictive capability difficult to quantify.

Location: https://princeton.zoom.us/j/94658114530
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Function Approximation via Sparse Random Fourier Features

Random feature methods have been successful in various machine learning tasks, are easy to compute, and come with theoretical accuracy bounds. They serve as an alternative approach to standard neural networks since they can represent similar function spaces without a costly training phase. However, for accuracy, random feature methods require more...

AI Meets Large-scale Sensing: preserving and exploiting structure of the real world to enhance machine perception

Machine capability has reached an inflection point, achieving human-level performance in tasks traditionally associated with cognition (vision, speech, strategic gameplay).  However, efforts to move such capability pervasively into the real world, have in many cases fallen far short of the relatively constrained and isolated demonstrations of...
Location: Virtual Seminar
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Finite Width, Large Depth Neural Networks as Perturbatively Solvable Models

Abstract: Deep neural networks are often considered to be complicated "black boxes," for which a systematic analysis is not only out of reach but potentially impossible. In this talk, which is based on ongoing joint work with Dan Roberts and Sho Yaida, I will make the opposite claim. Namely, that deep neural networks at initialization are...

Location: https://princeton.zoom.us/j/94090634488
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Computational Optics for Control and Readout of Neural Activity

Nearly all aspects of cognition and behavior require the coordinated action of multiple brain regions that are spread out over a large 3D volume. To understand the long-distance communication between these brain regions, we need optical techniques that can simultaneously monitor and control tens of thousands of individual neurons at cellular...
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Optimization Inspired Deep Architectures for Multiview 3D

Multiview 3D has traditionally been approached as continuous optimization: the solution is produced by an algorithm that solves an optimization problem over continuous variables (camera pose, 3D points, motion) to maximize the satisfaction of known constraints from multiview geometry. In contrast, deep learning offers an alternative strategy where...
Location: Virtual Seminar
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Fundamentals of Deep Learning (NVIDIA), Research Computing Bootcamp

Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written...
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GPU HPC Bootcamp (NVIDIA), Research Computing Bootcamp

Full event details and registration link here.

This day-long workshop will teach participants the basics of GPU programming through extensive hands-on collaboration based on real-life codes using the OpenACC programming model.

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Data Visualization in R, using ggplot2 with Daisy Huang, Research Computing Bootcamp

This workshop provides an introduction to effective data visualization in R, primarily using the graphics package ggplot2. We will discuss main concepts of the grammar that defines the graphical building blocks of that package, and we will use hands-on examples to explore ggplot2’s layered approach to creating basic and more complex graphs....
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