no code implementations • 21 Mar 2024 • Alice Baird, Rachel Manzelli, Panagiotis Tzirakis, Chris Gagne, Haoqi Li, Sadie Allen, Sander Dieleman, Brian Kulis, Shrikanth S. Narayanan, Alan Cowen
In this short white paper, to encourage researchers with limited access to large-datasets, the organizers first outline several open-source datasets that are available to the community, and for the duration of the workshop are making several propriety datasets available.
no code implementations • 29 Feb 2024 • Dimitrios Kollias, Panagiotis Tzirakis, Alan Cowen, Stefanos Zafeiriou, Irene Kotsia, Alice Baird, Chris Gagne, Chunchang Shao, Guanyu Hu
This paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with IEEE CVPR 2024.
no code implementations • 4 Jul 2023 • Chris Gagne, Peter Dayan
Transformer-based large-scale language models (LLMs) are able to generate highly realistic text.
1 code implementation • 5 May 2023 • Lukas Christ, Shahin Amiriparian, Alice Baird, Alexander Kathan, Niklas Müller, Steffen Klug, Chris Gagne, Panagiotis Tzirakis, Eva-Maria Meßner, Andreas König, Alan Cowen, Erik Cambria, Björn W. Schuller
Participants predict the presence of spontaneous humour in a cross-cultural setting.
no code implementations • 28 Apr 2023 • Björn W. Schuller, Anton Batliner, Shahin Amiriparian, Alexander Barnhill, Maurice Gerczuk, Andreas Triantafyllopoulos, Alice Baird, Panagiotis Tzirakis, Chris Gagne, Alan S. Cowen, Nikola Lackovic, Marie-José Caraty, Claude Montacié
The ACM Multimedia 2023 Computational Paralinguistics Challenge addresses two different problems for the first time in a research competition under well-defined conditions: In the Emotion Share Sub-Challenge, a regression on speech has to be made; and in the Requests Sub-Challenges, requests and complaints need to be detected.
no code implementations • 12 Nov 2021 • Chris Gagne, Peter Dayan
Conditional value-at-risk (CVaR) precisely characterizes the influence that rare, catastrophic events can exert over decisions.
1 code implementation • NeurIPS 2021 • Chris Gagne, Peter Dayan
Distributional reinforcement learning (RL) -- in which agents learn about all the possible long-term consequences of their actions, and not just the expected value -- is of great recent interest.