9 citations · 17 across the 6 of their papers we have counts for
6 papers
Deep Crowd-Flow Prediction in Built Environments
Samuel S. Sohn, Seonghyeon Moon, Honglu Zhou +3
Predicting the behavior of crowds in complex environments is a key requirement in a multitude of application areas, including crowd and disaster management, architectural design, a…
Cognitive Agent Based Simulation Model For Improving Disaster Response Procedures
Rohit K. Dubey, Samuel S. Sohn, Christoph Hoelscher +1
In the event of a disaster, saving human lives is of utmost importance. For developing proper evacuation procedures and guidance systems, behavioural data on how people respond dur…
Scenario Generalization of Data-driven Imitation Models in Crowd Simulation
Gang Qiao, Honglu Zhou, Mubbasir Kapadia +2
Crowd simulation, the study of the movement of multiple agents in complex environments, presents a unique application domain for machine learning. One challenge in crowd simulation…
Domain Authoring Assistant for Intelligent Virtual Agents
Sepehr Janghorbani, Ashutosh Modi, Jakob Buhmann +1
Developing intelligent virtual characters has attracted a lot of attention in the recent years. The process of creating such characters often involves a team of creative authors wh…
Topic Spotting using Hierarchical Networks with Self Attention
Pooja Chitkara, Ashutosh Modi, Pravalika Avvaru +2
Success of deep learning techniques have renewed the interest in development of dialogue systems. However, current systems struggle to have consistent long term conversations with…
Affect-Driven Dialog Generation
Pierre Colombo, Wojciech Witon, Ashutosh Modi +2
The majority of current systems for end-to-end dialog generation focus on response quality without an explicit control over the affective content of the responses. In this paper, w…