413 citations · 3.1k across the 143 of their papers we have counts for
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Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks
Salman Mohammed, Peng Shi, Jimmy Lin
We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of a single fact. Adopting a straightforward dec…
Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting
Raphael Tang, Jimmy Lin
We describe Honk, an open-source PyTorch reimplementation of convolutional neural networks for keyword spotting that are included as examples in TensorFlow. These models are useful…
An Experimental Analysis of the Power Consumption of Convolutional Neural Networks for Keyword Spotting
Raphael Tang, Weijie Wang, Zhucheng Tu +1
Nearly all previous work on small-footprint keyword spotting with neural networks quantify model footprint in terms of the number of parameters and multiply operations for a feedfo…
Deep Residual Learning for Small-Footprint Keyword Spotting
Raphael Tang, Jimmy Lin
We explore the application of deep residual learning and dilated convolutions to the keyword spotting task, using the recently-released Google Speech Commands Dataset as our benchm…
An Exploration of Approaches to Integrating Neural Reranking Models in Multi-Stage Ranking Architectures
Zhucheng Tu, Matt Crane, Royal Sequiera +2
We explore different approaches to integrating a simple convolutional neural network (CNN) with the Lucene search engine in a multi-stage ranking architecture. Our models are train…
Exploring the Effectiveness of Convolutional Neural Networks for Answer Selection in End-to-End Question Answering
Royal Sequiera, Gaurav Baruah, Zhucheng Tu +4
Most work on natural language question answering today focuses on answer selection: given a candidate list of sentences, determine which contains the answer. Although important, an…