{"id":164325,"date":"2013-08-01T00:00:00","date_gmt":"2013-08-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/tensor-deep-stacking-networks\/"},"modified":"2018-10-16T20:08:51","modified_gmt":"2018-10-17T03:08:51","slug":"tensor-deep-stacking-networks","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/tensor-deep-stacking-networks\/","title":{"rendered":"Tensor Deep Stacking Networks"},"content":{"rendered":"<div class=\"asset-content\">\n<p>A novel deep architecture, the Tensor Deep Stacking Network (T-DSN), is presented. The T-DSN consists of multiple, stacked blocks, where each block contains a bilinear mapping from two hidden layers to the output layer, using a weight tensor to incorporate higher-order statistics of the hidden binary features. A learning algorithm for the T-DSN&#8217;s weight matrices and tensors is developed and described, in which the main parameter estimation burden is shifted to a convex sub-problem with a closed-form solution. Using an efficient and scalable parallel implementation for CPU clusters, we train sets of T-DSNs in three popular tasks in an increasing order of the data size: handwritten digit recognition using MNIST (60k), isolated state\/phone classification and continuous phone recognition using TIMIT (1.1m), and isolated phone classification using WSJ0 (5.2m). Experimental results in all three tasks demonstrate the effectiveness of the T-DSN and the associated learning methods in a consistent manner. In particular, a sufficient depth of the T-DSN, a symmetry in the two hidden layers structure in each T-DSN block, our model parameter learning algorithm, and a softmax layer on top of T-DSN are shown to have all contributed to the low error rates observed in the experiments for all three tasks.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A novel deep architecture, the Tensor Deep Stacking Network (T-DSN), is presented. The T-DSN consists of multiple, stacked blocks, where each block contains a bilinear mapping from two hidden layers to the output layer, using a weight tensor to incorporate higher-order statistics of the hidden binary features. A learning algorithm for the T-DSN&#8217;s weight matrices [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"IEEE","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"8","msr_journal":"IEEE Transactions on Pattern Analysis and Machine Intelligence","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"35","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Brian 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