AIJC @ NU
The Graduate Student Led Journal Club for Artificial Intelligence Research at Northwestern University
A Casual Place To Present, Ponder, and Discuss AI at Northwestern
Artificial Intelligence is historically and inherently an interdisciplinary field of research. Many of the biggest advancements in the field have come from incorporating insights from neuroscience, psychology, statistics, economics, human factors, linguistics, physics, topology, biology, and many other academic disciplines. AI Journal Club is meant to be a place where we can present, critique, and discuss research (both seminal and also cutting-edge) in a welcoming environment with a diversity of viewpoints. Presentations range from casual paper discussions, to in-depth tutorials of cutting-edge techniques in AI to spirited debates over the ethical implications of AI. We welcome graduate students from any department to attend and participate in discussion and encourage regular attendees to present research related to their own work, research that they would like to solicit help in understanding, research that can foster conversations about the future of our field, or just research that is interesting and sparks novel and exciting ideas.
Spring 2018 Meetings
- Time : Wednesdays at 11AM
- Location : Frances Searle Building 2-378 (2nd floor)
- Food? : Yes! Every other week(-ish)
Graduate Students with a Northwestern University Email address can sign up for our Slack Channel here
Current Board Members
- Scott Cambo
- Irina Rabkina
- Prem Seetharaman
Previous Board Members
- Joe Blass (Founder)
Previous Presentations
Spring 2018
6/6/18 Deep Learning with Differential Privacy
- Authors are Martin Abadi, H. Brendan McMahan, Andy Chu, Ilya Mironov, Li Zhang, Ian Goodfellow, Kunal Talwar
- Presented by Isaac Johnson
5/30/18 Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
- Authors are Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti, Joel Lehman, Kenneth O. Stanley, Jeff Clune
- presented by Bryan Head
5/23/18 The Building Blocks of Interpretability
- Authors are Chris Olah, Arvind Styanarayan, Ian Johnson, Shan Carter, Ludwig Schubert, Katherine Ye, Alexander Mordvintsev
- presented by Ryan Louie
5/16/18 Accountability of AI Under the Law: The Role of Explanation
- Authors are Finale Doshi-Velez and Mason Kortz
- Presented by Joe Blass
5/8/18 Crowdsourced Pairwise-Comparison For Source Separation Evaluation
- Authors are Mark Cartwright, Bryan Pardo, Gautham J. Mysore
- presented by Ethan Manilow
4/25/18 “Why Should I Trust You?” Explaining the Predictions of Any Classifier
- Authors are Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
- Presented by Scott Cambo
4/18/18 The Mathematics of Statistical Machine Translation: Parameter Estimation
- Authors are Peter F. Brown, Vincent J. Della Pietra, Stephen A. Della Pietra, Robert L. Mercer
- Presented by Constantine Nakos
4/11/18 Efficient Video Object Segmentation via Network Modulation
- Authors are Linjie Yang, Yanran Wang, Xuehan Xiong, Jianchao Yang, Aggelos Katsaggelos
- Presented by Yanran Wang
4/4/18 Interpretable Decision Sets: A Joint Framework for Description and Prediction
- Authors are Himabindu Lakkaraju, Stephen Bach, Jure Leskovec
- Presented by Scott Cambo
Winter 2018
3/15/18 Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms
- Authors are Kate Crawford, Jason Schultz
- Presented by guest speaker, Rebecca Wexler
3/8/18 Recurrent Recommender Networks
- Authors are Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, and How Jing
- Presented by Nicholas Vincent
3/1/18 Poincaré Embeddings for Learning Hierarchical Representations
- Authors are Maximillian Nickel, Douwe Kiela
- Presented by Prem Seetharaman
2/8/18 Computer Assisted Authoring for Natural Language Story Scripts
- Authors are Rushit Sanghrajka, Markus Gross
- Presented by Irina Rabkina
2/1/18 VizWiz: Nearly real-time answers to visual questions
- Authored by Jeffrey Bigham et al.
- Presented by Emily Wang
1/18/18 Game Design for Classical AI
- Authored by Ian Horswill
- Presented by Ethan Robison
1/11/18 A Few Useful Things To Know About Machine Learning
- Authored by Pedro Domingos
- Presented by Scott Cambo
List of Past Presentations
Coming soon