Snapshots of ICML 2019

The 36th International Conference on Machine Learning (ICML) is by all accounts a premier conference in the machine learning world. Thousands of papers are submitted and thousands of people from around the world travel to attend the weeklong conference. This year was no different with over 6,000 attendees and 2,473 submitted papers. Only 621 papers … Continue reading Snapshots of ICML 2019

Playing Text-adventure Games with an AI

By Prithviraj Ammanabrolu People affect change in the world all the time using natural language communication. Grounding such communication in real world actions is a well-studied and notoriously complex task, even the data gathering step is difficult. So does there exist a platform on which we could more easily simulate such communication? And the answer … Continue reading Playing Text-adventure Games with an AI

ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging

By Samarth Brahmbhatt and Charlie Kemp Paper (CVPR 2019 oral) | bib | Explore ContactdB Paper by Samarth Brahmbhatt, Cusuh Ham, Charlie Kemp, and James Hays Georgia Institute of Technology Many times a day, people effortlessly grasp objects, yet human grasping is a complex phenomenon that has proven challenging to emulate and analyze. If robots … Continue reading ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging

Mixing Frank-Wolfe and Gradient Descent

By Sebastian Pokutta, associate director of ML@GT TL;DR: This is an informal summary of our recent paper Blended Conditional Gradients with Gábor Braun, Dan Tu, and Stephen Wright, showing how mixing Frank-Wolfe and Gradient Descent gives a new, very fast, projection-free algorithm for constrained smooth convex minimization. What is the paper about and why you might care Frank-Wolfe methods [FW] … Continue reading Mixing Frank-Wolfe and Gradient Descent

Georgia Tech’s Newest AI System Can Mimic Thinking Out Loud and Explain Its Decision to Non-Experts in Real-time

By Upol Ehsan, Ph.D. Student, School of Interactive Computing, Georgia Tech (contributing writer; Joshua Preston, GVU Center) If the power of AI is to be democratized, it needs to be accessible to anyone regardless of their technical abilities. As AI pervades all aspects of our lives, there is a distinct need for human-centered AI design … Continue reading Georgia Tech’s Newest AI System Can Mimic Thinking Out Loud and Explain Its Decision to Non-Experts in Real-time

Learning to Cluster

“Can machines categorize new things by learning how to group similar things together?” The following describes work by Yen-Chang Hsu, Zhaoyang Lv, and Zsolt Kira, which will be presented at the 2018 International Conference on Learning Representations (ICLR) in Vancouver. Read the paper here. Clustering is the task of partitioning data into groups, so that … Continue reading Learning to Cluster

Convergence of Value Aggregation for Imitation Learning

The following describes work by Ching-An Cheng and Byron Boots, which was awarded Best Paper at the Further details and proofs are available at The 21st International Conference on Artificial Intelligence and Statistics (AISTATS). The paper can be found here: https://arxiv.org/abs/1801.07292. Learning to make sequential decisions is a fundamental topic in designing automatic agents with artificial intelligence. … Continue reading Convergence of Value Aggregation for Imitation Learning