4 citations · 8 across the 3 of their papers we have counts for
4 papers · 1 filter
Learning Compressed Embeddings for On-Device Inference
Niketan Pansare, Jay Katukuri, Aditya Arora +4
In deep learning, embeddings are widely used to represent categorical entities such as words, apps, and movies. An embedding layer maps each entity to a unique vector, causing the…
One-Shot Learning on Attributed Sequences
Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner +2
One-shot learning has become an important research topic in the last decade with many real-world applications. The goal of one-shot learning is to classify unlabeled instances when…
MLAS: Metric Learning on Attributed Sequences
Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner +2
Distance metric learning has attracted much attention in recent years, where the goal is to learn a distance metric based on user feedback. Conventional approaches to metric learni…
Attributed Sequence Embedding
Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner +2
Mining tasks over sequential data, such as clickstreams and gene sequences, require a careful design of embeddings usable by learning algorithms. Recent research in feature learnin…