3 accepted papers
Individual user profiles and interaction histories play a significant role in providing customized experiences in real-world applications such as chatbots, social media, retail, and education. Adaptive user representation learning by utilizing user personalized information has be-come increasingly c…
Regularization is crucial to the success of many practical deep learning models, in particular in frequent scenarios where there are only a few to a moderate number of accessible training samples. In addition to weight decay, noise injection and dropout, regularization based on multi-branch architec…
In this work, we investigate a recently proposed regularization technique based on multi-branch architectures, called Shake-Shake regularization, for the task of speech emotion recognition. In addition, we also propose variants to incorporate domain knowledge into model configurations. The experimen…