Tag: Deep Neural Networks

AI Machine Learning & Data Science Research

Purdue U Proposes ANE: A Self-Adaptive Network Enhancement Method for Optimizing DNN Design

A research team from Purdue University presents a self-adaptive algorithm for optimal deep neural network design. The adaptive network enhancement (ANE) method learns not only from given information (data, function, PDEs) but also from the current computer simulation.

AI Machine Learning & Data Science Research

Microsoft & OneFlow Leverage the Efficient Coding Principle to Design Unsupervised DNN Structure-Learning That Outperforms Human-Designed Structures

A research team from OneFlow and Microsoft takes a step toward automatic deep neural network structure design, exploring unsupervised structure-learning and leveraging the efficient coding principle, information theory and computational neuroscience to design structure learning without label information.

AI Research

BatchNorm + Dropout = DNN Success!

A group of researchers from Tencent Technology, the Chinese University of Hong Kong, and Nankai University recently combined two commonly used techniques — Batch Normalization (BatchNorm) and Dropout — into an Independent Component (IC) layer inserted before each weight layer to make inputs more independent*.

AI Research

Global Minima Solution for Neural Networks?

New research from Carnegie Mellon University, Peking University and the Massachusetts Institute of Technology shows that global minima of deep neural networks can been achieved via gradient descent under certain conditions. The paper Gradient Descent Finds Global Minima of Deep Neural Networks was published November 12 on arXiv.