Unsupervised Representation Learning
Abstract:
upervised machine learning using deep neural networks have shown tremendous success on a variety of tasks in machine learning. However, supervised learning on each individual task is neither scalable nor the only way to create world models of visual (and other) phenomena and to do inference on them. The recent research thrusts are in mitigating and surmounting such problems. The learning of static world models is also called representation learning. Unsupervised representation learning seeks to create structured latent representations to avoid the onerous need to generate supervisory labels and to enable learning of task-independent (universal) representations. In this talk, I will provide an overview of recent efforts in my group at Verisk, carried out in collaboration with various academic partners, on these topics. Most of this work is available in recent publications and accompanying code online.
Bio:
Dr. Singh is Head, Verisk | AI and the Director of Human and Computation Intelligence Lab at Verisk. He leads the R&D efforts for the development of AI and machine learning technologies in a variety of areas including computer vision, natural language processing and speech understanding. Verisk Analytics builds tools for risk assessment, risk forecasting and decision analytics in a variety of sectors including insurance, financial services, energy, government and human resources.
From 2013-2015, Dr. Singh was a Technology Leader in the Center for Vision Technologies at SRI International, Princeton, NJ. At SRI, he was the technical lead for the DARPA Visual Media Reasoning (VMR) project for Automatic Performance Characterization and led the development and implementation of efficient Pareto optimal performance curves and a multithreaded APC system for benchmarking more than 40 CV and ML algorithms. Dr. Singh was the Algorithms Lead for the DARPA CwC CHAPLIN project for designing a human-computer collaboration (HCC) system to enable composition of visual narratives (cartoon strips, movies) with effective collaboration between a human actor and the computer. He was also a key performer on the DARPA DTM (Deep Temporal Models) seedling project for designing deep learning algorithms on video data. Previously, Dr. Singh was a Staff Scientist at Siemens Corporate Technology, Princeton, NJ till 2013. At Siemens, he led and contributed to a large number of projects for successful development and deployed of computer vision and machine learning technologies in multi-camera security and surveillance, aerial surveillance, advanced driver assistance and intelligent traffic control; industrial inspection; and, medical image processing and patient diagnostics. Dr. Singh received his Ph.D. in Electrical and Computer Engineering from the University of Illinois at in 2003. He has authored over 35 publications and 15 U.S. and International patents.