most citedWhy Do Neural Response Generation Models Prefer Universal Replies?

5 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2019★ 2 cited

Fashion Editing with Adversarial Parsing Learning

Haoye Dong, Xiaodan Liang, Yixuan Zhang +6

Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value. Existin…

cs.CL2018

Learning to Generate Structured Queries from Natural Language with Indirect Supervision

Ziwei Bai, Bo Yu, Bowen Wu +2

Generating structured query language (SQL) from natural language is an emerging research topic. This paper presents a new learning paradigm from indirect supervision of the answers…

cs.CL2018★ 5 cited

Why Do Neural Response Generation Models Prefer Universal Replies?

Bowen Wu, Nan Jiang, Zhifeng Gao +6

Recent advances in sequence-to-sequence learning reveal a purely data-driven approach to the response generation task. Despite its diverse applications, existing neural models are…

cs.CV2018★ 3 cited

Scalable Angular Discriminative Deep Metric Learning for Face Recognition

Bowen Wu, Huaming Wu, Monica M. Y. Zhang

With the development of deep learning, Deep Metric Learning (DML) has achieved great improvements in face recognition. Specifically, the widely used softmax loss in the training pr…

cs.CV2018

Exponential Discriminative Metric Embedding in Deep Learning

Bowen Wu, Zhangling Chen, Jun Wang +1

With the remarkable success achieved by the Convolutional Neural Networks (CNNs) in object recognition recently, deep learning is being widely used in the computer vision community…