Improving the Performance of Online Neural Transducer Models

Having a sequence-to-sequence model which can operate in an online fashion is important for streaming applications such as Voice Search. Neural transducer is a streaming sequence-to-sequence model, but has shown a significant degradation in performance compared to non-streaming models such as Listen, Attend and Spell (LAS). In this paper, we present various improvements to NT. Specifically, we look at increasing the window over which NT computes attention, mainly by looking backwards in time so the model still remains online. In addition, we explore initializing a NT model from a LAS-trained model so that it is guided with a better alignment. Finally, we explore including stronger language models such as using wordpiece models, and applying an external LM during the beam search. On a Voice Search task, we find with these improvements we can get NT to match the performance of LAS

Searching the World-Wide-Web using nucleotide and peptide sequences

*Background:* No approaches have yet been developed to allow instant searching of the World-Wide-Web by just entering a string of sequence data. Though general search engines can be tuned to accept ‘processed’ queries, the burden of preparing such ‘search strings’ simply defeats the purpose of quickly locating highly relevant information. Unlike ‘sequence similarity’ searches that employ dedicated algorithms (like BLAST) to compare an input sequence from defined databases, a direct ‘sequence based’ search simply locates quick and relevant information about a blunt piece of nucleotide or peptide sequence. This approach is particularly invaluable to all biomedical researchers who would often like to enter a sequence and quickly locate any pertinent information before proceeding to carry out detailed sequence alignment. 

*Results:* Here, we describe the theory and implementation of a web-based front-end for a search engine, like Google, which accepts sequence fragments and interactively retrieves a collection of highly relevant links and documents, in real-time. e.g. flat files like patent records, privately hosted sequence documents and regular databases. 

*Conclusions:* The importance of this simple yet highly relevant tool will be evident when with a little bit of tweaking, the tool can be engineered to carry out searches on all kinds of hosted documents in the World-Wide-Web.

*Availability:* Instaseq is free web based service that can be accessed by visiting the following hyperlink on the WWW
http://instaseq.georgetown.edu 
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LiveSketch: Query Perturbations for Guided Sketch-based Visual Search

LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch search by creating visual suggestions that augment the query as it is drawn, making query specification an iterative rather than one-shot process that helps disambiguate users' search intent. Our technical contributions are: a triplet convnet architecture that incorporates an RNN based variational autoencoder to search for images using vector (stroke-based) queries; real-time clustering to identify likely search intents (and so, targets within the search embedding); and the use of backpropagation from those targets to perturb the input stroke sequence, so suggesting alterations to the query in order to guide the search. We show improvements in accuracy and time-to-task over contemporary baselines using a 67M image corpus.Comment: Accepted to CVPR 201