activity
20192022
most citedA Survey of Deep Learning Techniques for Neural Machine Translation

99 citations · 115 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV20222 cited

NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension

Xin He, Jiangchao Yao, Yuxin Wang +5

One-shot neural architecture search (NAS) substantially improves the search efficiency by training one supernet to estimate the performance of every possible child architecture (i.…

cs.LG2021

A Survey of Transformers

Tianyang Lin, Yuxin Wang, Xiangyang Liu +1

Transformers have achieved great success in many artificial intelligence fields, such as natural language processing, computer vision, and audio processing. Therefore, it is natura…

cs.CL202099 cited

A Survey of Deep Learning Techniques for Neural Machine Translation

Shuoheng Yang, Yuxin Wang, Xiaowen Chu

In recent years, natural language processing (NLP) has got great development with deep learning techniques. In the sub-field of machine translation, a new approach named Neural Mac…

eess.IV2019

Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models

Xin He, Shihao Wang, Shaohuai Shi +10

Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis hi…

cs.DC2019

Benchmarking the Performance and Energy Efficiency of AI Accelerators for AI Training

Yuxin Wang, Qiang Wang, Shaohuai Shi +4

Deep learning has become widely used in complex AI applications. Yet, training a deep neural network (DNNs) model requires a considerable amount of calculations, long running time,…

cs.PF20195 cited

The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study

Zhenheng Tang, Yuxin Wang, Qiang Wang +1

Over the past years, great progress has been made in improving the computing power of general-purpose graphics processing units (GPGPUs), which facilitates the prosperity of deep n…