Add to ORKGWe analyze an approach to a similarity preserving coding of symbol sequences based on neural distributed representations and show that it can be viewed as a metric embedding process
International audienceComputing pairwise word semantic similarity is widely used and serves as a bui...
Abstract. The Universal Similarity Metric (USM) has been demon-strated to give practically useful me...
Edit distance is a fundamental measure of distance between strings, the extensive study of which has...
We discuss several approaches to similarity preserving coding of symbol sequences and possible conne...
We propose the neural string edit distance model for string-pair matching and string transduction ba...
irements. A simple and computationally very effective "distance" measure for sequences is ...
String kernel-based machine learning methods have yielded great success in practical tasks of struct...
Recent trends suggest that neural-network-inspired word embedding models outperform traditional coun...
Deep neural networks implement a sequence of layer-by-layer operations that are each relatively easy...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
The availability of high throughput sequencing tools coupled with the declining costs in the product...
Abstract Background Sequence similarity networks are useful for classifying and characterizing biolo...
Pattern Recognition tasks in the structural domain generally exhibit high accuracy results, but thei...
Abstract. We survey the emerging area of compression-based, parameter-free, similarity distance meas...
We introduce an oblivious embedding that maps strings of length n under edit distance to strings of ...
International audienceComputing pairwise word semantic similarity is widely used and serves as a bui...
Abstract. The Universal Similarity Metric (USM) has been demon-strated to give practically useful me...
Edit distance is a fundamental measure of distance between strings, the extensive study of which has...
We discuss several approaches to similarity preserving coding of symbol sequences and possible conne...
We propose the neural string edit distance model for string-pair matching and string transduction ba...
irements. A simple and computationally very effective "distance" measure for sequences is ...
String kernel-based machine learning methods have yielded great success in practical tasks of struct...
Recent trends suggest that neural-network-inspired word embedding models outperform traditional coun...
Deep neural networks implement a sequence of layer-by-layer operations that are each relatively easy...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
The availability of high throughput sequencing tools coupled with the declining costs in the product...
Abstract Background Sequence similarity networks are useful for classifying and characterizing biolo...
Pattern Recognition tasks in the structural domain generally exhibit high accuracy results, but thei...
Abstract. We survey the emerging area of compression-based, parameter-free, similarity distance meas...
We introduce an oblivious embedding that maps strings of length n under edit distance to strings of ...
International audienceComputing pairwise word semantic similarity is widely used and serves as a bui...
Abstract. The Universal Similarity Metric (USM) has been demon-strated to give practically useful me...
Edit distance is a fundamental measure of distance between strings, the extensive study of which has...