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Our aim is to automatically predict their activity level at a later point of the meeting. The predictive models use verbal and nonverbal features.
We analyze which interaction features are most predictive of later meeting activity levels, and investigate the efficacy of the verbal vs. nonverbal feature ...
Graph-Based Prediction of Meeting Participation. Authors. Murray, Gabriel. Abstract. Given a meeting participant's turn-taking dynamics during one segment of a ...
A spatio-temporal graph models the interactions between traffic participants for predicting the future trajectories of those participants. The predicted ...
Nov 27, 2023 · In this paper, we propose to utilize social media retweeting activity to enhance the learning of event participant prediction models.
Missing: Meeting | Show results with:Meeting
We present a three-pronged approach to the link prediction task, along with several novel variations on established similarity metrics.
Missing: Meeting | Show results with:Meeting
Jul 14, 2023 · This task helps people to understand how events evolve in our real world and make rapid, accurate, and efficient reactions in emergencies.
Missing: Meeting | Show results with:Meeting
Aug 27, 2020 · In this paper, we study a temporal graph learning method with heterogeneous data fusion for predicting concurrent events of multiple types and.
Missing: Meeting | Show results with:Meeting
Mar 24, 2024 · A deep Graph-based Prediction and Planning Policy Network (GP3Net) framework is proposed for non-stationary environments that encodes the interactions between ...
Missing: Meeting | Show results with:Meeting
We propose GraphNLI, a novel graph-based deep learning architecture that uses graph walk techniques to capture the wider context of a discussion thread in a ...