By Yuval Pinter Imagine you’re building a boat, starting from a heap of parts. With each new board or screw, you make sure that it fits the adjacent parts, and that the material type is suitable for the section of the boat it’s in. But there are also bigger concerns to consider - is the… Continue reading How Not to Rock the Semantic Boat
By Ian Stewart The language that people use to communicate online is in constant flux. People may have once written "haha" to indicate laughter but over time have adopted "lol" instead. Entire dictionaries and websites such as UrbanDictionary.com are dedicated to tracking the ebb and flow of the latest slang (i.e. nonstandard) words that propagate… Continue reading What Makes a New Word Stick?
By Zhaoyang Lv We live in a three-dimensional (3D), dynamic world every day. Being able to perceive 3D high-resolution motion is a fundamental ability of our perception system, which enables us to perform versatile jobs. At the age when we are building intelligent robots, autonomous vehicles, and augmented reality toolkits, how can we also enable… Continue reading Learning Rigidity and Scene Flow Estimation
By: Prithvijit Chattopadhyay and Ramprasaath R. Selvaraju (Paper authors include Ramprasaath R. Selvaraju, Prithvijit Chattopadhyay, Mohamed Elhoseiny, Tilak Sharma, Dhruv Batra, Devi Parikh, and Stefan Lee) Deep Neural Networks have pushed the boundaries of standard image-classification tasks in the past few years, with performance on many challenging benchmarks reaching near human-level accuracies. One of the… Continue reading Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance
By Jianwei Yang and Jiasen Lu The following post breaks down Graph R-CNN for Scene Graph Generation, which will be presented at the European Conference on Computer Vision 2018 (ECCV). The conference takes place September 8th through 14th in Munich, Germany. Visual scene understanding has traditionally focused on identifying objects in images -- learning to predict their… Continue reading What is Graph R-CNN?
by Nilaksh Das, PhD student at Georgia Institute of Technology in the School of Computational Science and Engineering. Das is advised by Polo Chau. “SHIELD is a fast and practical approach to defend deep neural networks from adversarial attacks. This work proposes a multifaceted framework which incorporates compression, randomization, model-retraining, and ensembling to make computer vision models robust to adversarial… Continue reading SHIELD: Defending Deep Neural Networks from Adversarial Attacks
Eight Georgia Tech schools partner to offer advanced degree in emerging field of machine learning. The Georgia Institute of Technology has been approved to offer a new advanced degree program for the emerging field of machine learning.In a unanimous vote, the Board of Regents of the University System of Georgia approved Georgia Tech’s request to… Continue reading New Ph.D. Program Highlights Growing Importance of Machine Learning