Welcome to the DC/NoVA Papers We Love meetup!
Papers We Love is an international organization centered around the appreciation of computer science research papers. There's so much we can learn from the landmark research that shaped the field and the current studies that are shaping our future. Our goal is to create a community of tech professionals passionate about learning and sharing knowledge. Come join us!
New to research papers? Watch The Refreshingly Rewarding Realm of Research Papers (https://www.youtube.com/watch?v=8eRx5Wo3xYA) by Sean Cribbs.
Ideas and suggestions are welcome–fill our our interest survey here (https://docs.google.com/forms/d/e/1FAIpQLSeJwLQhnmzWcuyodPrSmqHgqrvNxRbnNSbiWAuwzHwshhy_Sg/viewform) and let us know what motivates you!
// Tentative Schedule
• 7:00-7:30–Informal paper discussion
• 7:30-7:35–Introduction and announcements
• 7:35-8:40–Papers and Discussion: Automatic Differentiation in Julia, presented by Brian Cohen
Links to relevant papers: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.89.7749&rep=rep1&type=pdf and https://arxiv.org/pdf/1607.07892.pdf
• 8:40-9:00–Informal paper discussion
CustomInk Cafe (3rd Floor)
Mosaic District, 2910 District Ave #300
Fairfax, VA 22031
When you get here you can come in via the patio. Don't be scared by the metal gate and sign. It's accessible via the outside stairs near True Food. There is a parking garage next door for those coming by vehicle. And, there is a walkway to the patio on the 3rd floor of the garage nearest moms organic market.
Metro: The Dunn Loring metro station is about 0.7 miles from our meetup location. It’s very walkable, but if you’d prefer a bus, the 402 Southbound and 1A/1B/1C Westbound leave from Dunn Loring Station about every 5-10 minutes (see a schedule for more detailed timetable).
If you're late, we totally understand–please still come! (via the patio is best) Just be sure to slip in quietly if a speaker is presenting.
This talk will feature a discussion on Automatic Differentiation via Dual Numbers and their applications. I will introduce some aspects of the Julia language that allow you to easily implement dual numbers, I will show how to implement dual numbers, and how to get real numerical derivatives from dual numbers. I will compare it to the finite difference approach (numeric differentiation) and symbolic differentiation. We will also explore and discuss how AutoDiff is used in differential equation solvers, machine learning, and photogrammetry.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.89.7749&rep=rep1&type=pdf and https://arxiv.org/pdf/1607.07892.pdf