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In this paper, we use Diagonal Recurrent Neural Networks on a sequence prediction task. The modification from standard RNN is simple: Diagonal recurrent matrices are used instead of full. This results in better test likelihood and faster convergence compared to regular full RNNs in most of our experiments. We show the benefits of using diagonal recurrent matrices with popularly used LSTM and GRU architectures...
We propose a method-of-moments algorithm for parameter learning in Left-to-Right Hidden Markov Models. Compared to the conventional Expectation Maximization approach, the proposed algorithm is computationally more efficient, and hence more appropriate for large datasets. It is also asymptotically guaranteed to estimate the correct parameters. We show the validity of our approach with a synthetic data...
In this paper, we derive two novel learning algorithms for time series clustering; namely for learning mixtures of Markov Models and mixtures of Hidden Markov Models. Mixture models are special latent variable models that require the usage of local search heuristics such as Expectation Maximization (EM) algorithm, that can only provide locally optimal solutions. In contrast, we make use of the spectral...
In this work, the curve compression problem is approached with a model-based probabilistic framework. We propose three different models. The proposed models can be used for purposes such as feature extraction or compression. The first model we propose is basically a Bayesian regression model for fitting piece-wise defined segments. The second model unifies clustering with regression. The third model...
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