This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.
I have the 63 pages paper named "Training Deep and Recurrent Networks with Hessian-Free Optimization", the hessian-free mainly the reproduced trait in hessian-updated training and reduce most hessian memory with hessian-free training mode to accelerate much steps than SGD relevant methods.
According to the description of uis-rnn, that is a reproduced algorithm of RNN struct, so I recommend to add a feature of hessian-free mode to train compatible scale task in case of the prior of uis-rnn.
Randomly choose a method named Preconditioned Conjugate Gradient algorithm (PCG), this method located at page 9 of the requester's research paper "Training Deep and Recurrent Networks with Hessian-Free Optimization", which consists of The generalized Gaussian-Newton matrix, Damping, Preconditioning contents. Compare Gaussian-Newton and RNN, both are the algorithms with residual sequences and their evaluation.
There is a feature request of hessian-free support, due to the existed testing platform of uis-rnn. This produce report have no correlation of binded download version, only a helpful request of add hessian-free optimization when encounter large-scale residual datasets.
I have the 63 pages paper named "Training Deep and Recurrent Networks with Hessian-Free Optimization", the hessian-free mainly the reproduced trait in hessian-updated training and reduce most hessian memory with hessian-free training mode to accelerate much steps than SGD relevant methods.
According to the description of uis-rnn, that is a reproduced algorithm of RNN struct, so I recommend to add a feature of hessian-free mode to train compatible scale task in case of the prior of uis-rnn.
Randomly choose a method named Preconditioned Conjugate Gradient algorithm (PCG), this method located at page 9 of the requester's research paper "Training Deep and Recurrent Networks with Hessian-Free Optimization", which consists of The generalized Gaussian-Newton matrix, Damping, Preconditioning contents. Compare Gaussian-Newton and RNN, both are the algorithms with residual sequences and their evaluation.
There is a feature request of hessian-free support, due to the existed testing platform of uis-rnn. This produce report have no correlation of binded download version, only a helpful request of add hessian-free optimization when encounter large-scale residual datasets.