Neural Architects Workshop

Deep Neural Networks (DNNs) now represent a fundamental building block of many machine perception methods. The reason is simple—these models achieve exceptional performance. DNNs represent the state-of-the-art for core competencies such as image classification, object detection and semantic segmentation as well as for integrated approaches to higher level tasks including environment mapping and video understanding. While their usefulness is clear, our understanding of how best to design these models remains far from complete.

The goal of this workshop was to bring together researchers to discuss questions and ideas relating to various aspects of the structure and design of DNNs. We considered in particular the following the two questions: (1) What we have learned as a community from our experience of designing these models? (2) Which research directions are most promising for improving existing architectures?

As part of the workshop, we are collecting suggestions (ongoing) for DNN-design related idea that "everyone should know about", which will be edited into a summary document that we hope will prove useful for members of the field. Add your suggestion for DNN-design related idea that "everyone should know about" here.

A short report of the day itself can be found here. A YouTube playlist of most of the invited talks can be found here.