Seminar on "DMTK: Making Very Large-Scale Machine Learning Possible" & "Deep Residual Learning in Image Classification and Transfer Learning"
Title: DMTK: Making Very Large-Scale Machine Learning Possible
Lead Researcher, Microsoft Research Asia
Abstract:
Distributed machine learning has become more important than ever in this big data era. Especially in recent years, practices have demonstrated the trend that bigger models tend to generate better accuracies in various applications. However, it remains a challenge for common machine learning researchers and practitioners to learn big models, because the task usually requires a large number of computation resources. In order to enable the training of big models using just a modest cluster and in an efficient manner, we released the Microsoft Distributed Machine Learning Toolkit (DMTK), which contains both algorithmic and system innovations. These innovations make machine learning tasks on big data highly scalable, efficient and flexible.
Title: Deep Residual Learning in Image Classification and Transfer Learning
Speaker: Shaoqing REN
University of Science and Technology of China
Date: 26 April 2016 (Tuesday)
Time: 10:30 a.m. – 12:00 nn
Venue: Room 513, William M. W. Mong Engineering Building
Abstract:
Deeper neural networks are difficult to train. We will present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. In this framework, we explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We can provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and therefore gain more accuracy from considerably increased depth. With this deep network, our team won the 1st place in the ILSVRC 2015 (aka, ImageNet Competition) classification task and detection task, COCO 2015 detection task and segmentation task. In this talk, I will introduce our method and findings of deep residual learning in image classification and transfer learning.
