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: Chris Sterritt will present "Deep Representation Learning with Genetic Programming"
• 8:40-9:00–Informal paper discussion
CustomInk Cafe (3rd Floor)
Mosaic District, 2910 District Ave #300
Fairfax, VA 22031
We'll be right inside from Custom Ink's 3rd floor patio. If you enter the parking garage at the corner of Strawberry Lane and Glass Alley, you can take the elevator or stairs to the 3rd floor and then the walkway from the garage to the entrance of the patio. You can also take the stairs going up that are around the corner from True Food, the stairs with giraffe art on the wall.
If you're late, we totally understand–please still come! Just be sure to slip in quietly if a speaker is presenting.
Chris Sterritt is excited to present this paper — available at https://ccc.inaoep.mx/archivos/CCC-17-009.pdf — which tries to combine what works in machine learning with neural networks and what works when designing genetic programming algorithms.
From the abstract: "In this technical report, we describe a research proposal to develop a new type of deep architecture for representation learning, based on Genetic Programming (GP). GP is a machine learning framework that belongs to evolutionary computation. GP has already been used in the past for representation learning; however, many of those approaches required of human experts knowledge from the representations’ domain. In this proposal, we explore the pitfalls of developing representation learning systems with GP that do not require experts’ knowledge, and propose a solution based on layered GP structures, similar to those found in Deep Neural Networks."