Yazhou Zhang

TL;DR: A novel and comprehensive framework for multimodal sentiment analysis in conversations is proposed, called a quantum-like multi-modal network (QMN), which leverages the mathematical formalism of quantum theory (QT) and a long short-term memory (LSTM) network.

TL;DR: A complex-valued fuzzy network is proposed by leveraging the mathematical formalisms of quantum theory and fuzzy logic to address the intrinsic vagueness and uncertainty of human language in emotional expression and understanding in sarcasm detection.

TL;DR: A new conversational dataset is presented, named ScenarioSA, and an interactive long short-term memory network is proposed for conversational sentiment analysis to model interactions between speakers in a conversation, which outperforms a wide range of strong baselines and achieves competitive results with the state-of-art approaches.

TL;DR: In this paper, the authors proposed a vision transformer-based deep learning pipeline for COVID-19 detection from chest X-ray-based imaging, which can be used to monitor the progression of the disease in the affected lungs, assisting healthcare.

TL;DR: The approach proposed in this work is not only feasible in emotion recognition but also promising in diagnosing depression, Alzheimer's disease, mild cognitive impairment, etc., whose specific latent processes may be altered or aberrant compared with the normal healthy control.