Upcoming Events

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Events Archive

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

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...

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.


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....

Introduction to NumPy with Vineet Bansal, Research Computing Bootcamp

This session covers the basics of NumPy, the package that underlies most scientific computing done in Python. It will explain the NumPy array, the principal data type in the NumPy package, and how it differs from similar Python structures like lists. There will be particular emphasis on understanding the two core features of NumPy arrays –...

Reproducible Research Reports with R Markdown with Daisy Huang, Research Computing Bootcamp

Do you use LaTeX or Microsoft Word to write your analysis report? Have you ever wished that all your research results (e.g., data analysis, graphs, result discussions) can be included in one place and can be updated effortlessly? Are you tired of all the copying and pasting that you have to do between R and LaTeX/Microsoft Word?

Intro to Data Analysis Using R w/ Brian Arnold & Andrzej Zuranski (Schmidt DataX), Research Computing Bootcamp

This session in an introduction to data analysis using the R programming language, aimed at people who have ever used R or RStudio before. It will briefly cover different facets of data analysis and their execution using basic R. The style is fairly hands-on, with participants executing the examples on their own laptops alongside the instructors....

Deep Networks from First Principles

In this talk, we offer an entirely “white box’’ interpretation of deep (convolutional) networks from the perspective of data compression. In particular, we show how modern deep architectures, linear (convolution) operators and nonlinear activations, and parameters of each layer can be derived from the principle of rate reduction (and invariance).

Breaking the Sample Size Barrier in Statistical Inference and Reinforcement Learning

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.