Jul 12, 2020
Recent work suggests that overparameterized autoencoders can be trained to implement associative memory via iterative maps. This phenomenon happens when converged input-output Jacobian of the network has all eigenvalue norms strictly below one. In this work, we theoretically analyze this behavior for sigmoid networks by leveraging recent developments in deep learning theories, especially the Neural Tangent Kernel (NTK) theory. We find that overparameterized sigmoid autoencoders can have attractors in the NTK limit for both training with a single example and multiple examples under certain conditions. In particular, for multiple training examples, we find that the norm of the largest Jacobian eigenvalue drops below one with increasing input norm, leading to associative memory.
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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