匯集人工智慧新創公司

We'll present ChainerMN, a multi-node distributed deep learning framework, together with the basics of distributed deep learning. Even though GPUs are continuously gaining more computation throughput, it is still very time-consuming to train state-of-the-art deep neural network models. For better scalability and productivity, it is paramount to accelerate the training process by using multiple GPUs. To enable high-performance and flexible distributed training, we developed ChainerMN, built on top of Chainer. We'll first introduce the basic approaches to distributed deep learning. Then, we'll explain the design choice, basic usage, and implementation details of Chainer and ChainerMN. We'll report benchmark results and discuss the future directions of distributed deep learning.

We'll show how state-of-the-art deep learning techniques can be applied to retail analytics. Namely, we'll show how one can retrieve various information about the product, including its category and ingredients, using a mixture of visual and textual information. We'll start with depicting the business scenario and operational needs of such a system, and then move into a technical and in-depth discussion of the underlying deep learning pipeline. The solution is based on an interplay of region-based convolutional neural networks and NLP techniques. This is a joint effort of Nielsen and deepsense.io.

Sad but true: most of radiology is mind-numbing tedium. Radiologists spend countless hours on tasks that are onerous and error-prone, resulting in high costs and frequent misdiagnoses. Our first product designed to address these deficiencies is Arterys Cardio DL, a web-based, zero-footprint cardiac MRI postprocessing suite. Arterys Cardio DL includes a deep learning-based contouring algorithm that vastly reduces the time required to diagnose heart disease in cardiac MRI. Arterys Cardio DL is the first technology ever to be cleared by the FDA that leverages cloud computing and deep learning in a clinical setting. We'll discuss the technology behind the software and how we proved its safety and efficacy to secure FDA clearance in the United States and the CE Mark in Europe.