Graph Structure of Neural Networks
Jul 12, 2020
Speakers
Jiaxuan You
Speaker · 2 followers
Kaiming He
Speaker · 1 follower
Jure Leskovec
Speaker · 17 followers
Saining Xie
Speaker · 0 followers
About
Neural networks are often represented as graphs of connections between the neurons. However, despite their wide use there is currently no understanding of the relationship between the graph structure of a neural network and its predictive performance. Here we systematically investigate this relationship, via developing a novel graph-based representation of neural networks called relational graph, where computation is specified by rounds of message exchange along the graph structure. Using our novel framework we show that (1) there is a “sweet spot”, where relational graphs within certain range of average path length and clustering coefficient lead to neural networks with significant improvements in predictive performance; (2) perhaps even more surprisingly, we find that these sweet spots tend to highly correlate across different architectures and datasets; and, (3) we show that discovering top-performing relational graphs only requires a few epochs of training. Overall, our results suggest promising avenues for designing and understanding neural networks with graph representations.Neural networks are often represented as graphs of connections between the neurons. However, despite their wide use there is currently no understanding of the relationship between the graph structure of a neural network and its predictive performance. Here we systematically investigate this relationship, via developing a novel graph-based representation of neural networks called relational graph, where computation is specified by rounds of message exchange along the graph structure. Using our no…
Categories
AI & Data Science
Category · 10.8k presentations
Mathematics
Category · 2.4k presentations
About ICML 2020
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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