4 citations · 4 across the 1 of their papers we have counts for
3 papers
cs.CL2019
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation
Jie Zhu, Junhui Li, Muhua Zhu +3
Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph…
cs.IR2018
Recurrent Binary Embedding for GPU-Enabled Exhaustive Retrieval from Billion-Scale Semantic Vectors
Ying Shan, Jian Jiao, Jie Zhu +1
Rapid advances in GPU hardware and multiple areas of Deep Learning open up a new opportunity for billion-scale information retrieval with exhaustive search. Building on top of the…
cs.LG2017★ 4 cited
Deep Embedding Forest: Forest-based Serving with Deep Embedding Features
Jie Zhu, Ying Shan, JC Mao +3
Deep Neural Networks (DNN) have demonstrated superior ability to extract high level embedding vectors from low level features. Despite the success, the serving time is still the bo…