Loading…
This event has ended. View the official site or create your own event → Check it out
This event has ended. Create your own
View analytic
Monday, December 7 • 19:00 - 23:59
Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks

Sign up or log in to save this to your schedule and see who's attending!

Multidimensional recurrent neural networks (MDRNNs) have shown a remarkable performance in the area of speech and handwriting recognition. The performance of an MDRNN is improved by further increasing its depth, and the difficulty of learning the deeper network is overcome by using Hessian-free (HF) optimization. Given that connectionist temporal classification (CTC) is utilized as an objective of learning an MDRNN for sequence labeling, the non-convexity of CTC poses a problem when applying HF to the network. As a solution, a convex approximation of CTC is formulated and its relationship with the EM algorithm and the Fisher information matrix is discussed. An MDRNN up to a depth of 15 layers is successfully trained using HF, resulting in an improved performance for sequence labeling.


Monday December 7, 2015 19:00 - 23:59
210 C #32

Attendees (5)