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Found 5 papers, 1 papers with code
From the Lab to the Wild: Affect Modeling via Privileged Information
Privileged information enables affect models to be trained across multiple modalities available in a lab, and ignore, without significant performance drops, those modalities that are not available when they operate in the wild.
The Invariant Ground Truth of Affect
In particular, we assume that the ground truth of affect can be found in the causal relationships between elicitation, manifestation and annotation that remain \emph{invariant} across tasks and participants.
Supervised Contrastive Learning for Affect Modelling
Affect modeling is viewed, traditionally, as the process of mapping measurable affect manifestations from multiple modalities of user input to affect labels.
RankNEAT: Outperforming Stochastic Gradient Search in Preference Learning Tasks
Stochastic gradient descent (SGD) is a premium optimization method for training neural networks, especially for learning objectively defined labels such as image objects and events.
Dendritic Self-Organizing Maps for Continual Learning
Current deep learning architectures show remarkable performance when trained in large-scale, controlled datasets.
