26,208 research outputs found
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
A recent strategy to circumvent the exploding and vanishing gradient problem
in RNNs, and to allow the stable propagation of signals over long time scales,
is to constrain recurrent connectivity matrices to be orthogonal or unitary.
This ensures eigenvalues with unit norm and thus stable dynamics and training.
However this comes at the cost of reduced expressivity due to the limited
variety of orthogonal transformations. We propose a novel connectivity
structure based on the Schur decomposition and a splitting of the Schur form
into normal and non-normal parts. This allows to parametrize matrices with
unit-norm eigenspectra without orthogonality constraints on eigenbases. The
resulting architecture ensures access to a larger space of spectrally
constrained matrices, of which orthogonal matrices are a subset. This crucial
difference retains the stability advantages and training speed of orthogonal
RNNs while enhancing expressivity, especially on tasks that require
computations over ongoing input sequences
Unsupervised Neural Hidden Markov Models
In this work, we present the first results for neuralizing an Unsupervised
Hidden Markov Model. We evaluate our approach on tag in- duction. Our approach
outperforms existing generative models and is competitive with the
state-of-the-art though with a simpler model easily extended to include
additional context.Comment: accepted at EMNLP 2016, Workshop on Structured Prediction for NLP.
Oral presentatio
- …