activity
20172019
most citedImproving Spark Application Throughput Via Memory Aware Task Co-location: A Mixture of Experts Approach

14 citations · 19 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL20195 cited

Lattice CNNs for Matching Based Chinese Question Answering

Yuxuan Lai, Yansong Feng, Xiaohan Yu +3

Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages lik…

cs.RO2019

Interaction-aware Kalman Neural Networks for Trajectory Prediction

Ce Ju, Zheng Wang, Cheng Long +2

Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.) benefits the on-road motion planning for intelligent and autonomous vehicles. Complex sce…

cs.CV2018

Global and Local Sensitivity Guided Key Salient Object Re-augmentation for Video Saliency Detection

Ziqi Zhou, Zheng Wang, Huchuan Lu +2

The existing still-static deep learning based saliency researches do not consider the weighting and highlighting of extracted features from different layers, all features contribut…

cs.LG2018

Composite Binary Decomposition Networks

You Qiaoben, Zheng Wang, Jianguo Li +3

Binary neural networks have great resource and computing efficiency, while suffer from long training procedure and non-negligible accuracy drops, when comparing to the full-precisi…

cs.DC201714 cited

Improving Spark Application Throughput Via Memory Aware Task Co-location: A Mixture of Experts Approach

Vicent Sanz Marco, Ben Taylor, Barry Porter +1

Data analytic applications built upon big data processing frameworks such as Apache Spark are an important class of applications. Many of these applications are not latency-sensiti…