activity
20202022
most citedDidn't see that coming: a survey on non-verbal social human behavior forecasting

2 citations · 2 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2022

Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020

Zhen Xu, Lanning Wei, Huan Zhao +4

Graph structured data is ubiquitous in daily life and scientific areas and has attracted increasing attention. Graph Neural Networks (GNNs) have been proved to be effective in mode…

cs.CV2022

Comparison of Spatio-Temporal Models for Human Motion and Pose Forecasting in Face-to-Face Interaction Scenarios

German Barquero, Johnny Núñez, Zhen Xu +4

Human behavior forecasting during human-human interactions is of utmost importance to provide robotic or virtual agents with social intelligence. This problem is especially challen…

cs.CV20222 cited

Didn't see that coming: a survey on non-verbal social human behavior forecasting

German Barquero, Johnny Núñez, Sergio Escalera +4

Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years. Its direct applications to human-robot interaction a…

cs.LG2022

Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019

Zhengying Liu, Adrien Pavao, Zhen Xu +22

This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) th…

cs.LG2021

Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020

Ryan Turner, David Eriksson, Michael McCourt +4

This paper presents the results and insights from the black-box optimization (BBO) challenge at NeurIPS 2020 which ran from July-October, 2020. The challenge emphasized the importa…

cs.AI2020

AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification

Jingsong Wang, Tom Ko, Zhen Xu +4

The AutoSpeech challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to speech processing tasks. These tasks, which…