2 citations · 2 across the 4 of their papers we have counts for
4 papers · 1 filter
A Treatise On FST Lattice Based MMI Training
Adnan Haider, Tim Ng, Zhen Huang +2
Maximum mutual information (MMI) has become one of the two de facto methods for sequence-level training of speech recognition acoustic models. This paper aims to isolate, identify…
A Distributed Optimisation Framework Combining Natural Gradient with Hessian-Free for Discriminative Sequence Training
Adnan Haider, Chao Zhang, Florian L. Kreyssig +1
This paper presents a novel natural gradient and Hessian-free (NGHF) optimisation framework for neural network training that can operate efficiently in a distributed manner. It rel…
Combining Natural Gradient with Hessian Free Methods for Sequence Training
Adnan Haider, P. C. Woodland
This paper presents a new optimisation approach to train Deep Neural Networks (DNNs) with discriminative sequence criteria. At each iteration, the method combines information from…
A Common Framework for Natural Gradient and Taylor based Optimisation using Manifold Theory
Adnan Haider
This technical report constructs a theoretical framework to relate standard Taylor approximation based optimisation methods with Natural Gradient (NG), a method which is Fisher eff…