The Machine Learning Center at Georgia Tech is responsible for training the next generation of machine learning and artificial intelligence pioneers. Jason Lin, a recent master’s in computer science graduate who specialized in machine learning and robotics, is one of many students the center is thrilled to see thriving in today’s workforce. Currently a research… Continue reading ML@GT Alumni Corner: Jason Lin, Seeking Balance between Research, Application, and Problem Solving on the Go
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
Earning a Ph.D. is no easy task. It involves a lot of time, reading, writing, revising, research, and money. When an advisor and student feel that a student’s thesis has finally reached strong conclusions, it is time to defend it. A successful defense is the key to earning the sacred diploma and is likely something… Continue reading What is a Ph.D. Defense and How To Nail Yours
Every semester the Machine Learning Center at Georgia Tech is thrilled to bring renowned researchers and industry experts to campus as a part of our seminar series. The series allows our students to hear talks from some of the leading minds in machine learning and artificial intelligence. Seminars are typically held every other Wednesday at… Continue reading ML@GT Spring Seminar Series
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
