Koninklijke Philips Corporation N.V.

Kathy Lee

Oladimeji Feyisetan Farri

Sheikh Sadid Al Hasan

Junyi Liu

Vivek Varma Datla

Ashequl Qadir

Aaditya Prakash

Reza Ghaeini

Techniques described herein relate to semi-supervised training and application of stacked autoencoders and other classifiers for predictive and other purposes. In various embodiments, a semi-supervised model () may be trained for sentence classification, and may combine what is referred to herein as a 'residual stacked de-noising autoencoder' ('RSDA') (), which may be unsupervised, with a supervised classifier () such as a classification neural network (e.g., a multilayer perceptron, or &#x201c;MLP&#x201d;). In various embodiments, the RSDA may be a stacked denoising autoencoder that may or may not include one or more residual connections. If present, the residual connections may help the RSDA &#x201c;remember&#x201d; forgotten information across multiple layers. In various embodiments, the semi-supervised model may be trained with unlabeled data (for the RSDA) and labeled data (for the classifier) simultaneously.

Semi-supervised classification with stacked autoencoder

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Westford, MA, United States of America

Kathy Lee