acm-header
Sign In

Communications of the ACM

News

Geometric Deep Learning Advances Data Science


binary data stream, illustration

Credit: Getty Images

Deep learning has transformed numerous fields. In tackling complex tasks such as speech recognition, computer vision, predictive analytics, and even medical diagnostics, these systems consistently achieve—and even exceed—human-level performance. Yet deep learning, an umbrella term for machine learning systems based primarily on artificial neural networks, is not without its limitations. As data becomes non-planar and more complex, the ability of the machine to identify patterns declines markedly.

At the heart of the issue are the basic mechanics of deep learning frameworks. "With just two layers, a simple perceptron-type network can approximate any smooth function to any desired accuracy, a property called 'universal approximation'," points out Michael Bronstein, a professor in the Department of Computing at Imperial College London in the U.K. "Yet, multilayer perceptrons display very weak inductive bias, in the sense that they assume very little about the structure of the problem at hand and fail miserably if applied to high-dimensional data."


 

No entries found

Log in to Read the Full Article

Sign In

Sign in using your ACM Web Account username and password to access premium content if you are an ACM member, Communications subscriber or Digital Library subscriber.

Need Access?

Please select one of the options below for access to premium content and features.

Create a Web Account

If you are already an ACM member, Communications subscriber, or Digital Library subscriber, please set up a web account to access premium content on this site.

Join the ACM

Become a member to take full advantage of ACM's outstanding computing information resources, networking opportunities, and other benefits.
  

Subscribe to Communications of the ACM Magazine

Get full access to 50+ years of CACM content and receive the print version of the magazine monthly.

Purchase the Article

Non-members can purchase this article or a copy of the magazine in which it appears.
Sign In for Full Access
» Forgot Password? » Create an ACM Web Account