A systematic literature review on multimodal machine learning: Applications, challenges, gaps and future directions
Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …
heterogeneous data from multiple modalities and machine learning (ML) are combined to …
Parse: Pairwise alignment of representations in semi-supervised eeg learning for emotion recognition
G Zhang, V Davoodnia… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
We propose pairwise alignment of representations for semi-supervised
Electroencephalogram (EEG) learning (PARSE), a novel semi-supervised architecture for …
Electroencephalogram (EEG) learning (PARSE), a novel semi-supervised architecture for …
Self-supervised EEG emotion recognition models based on CNN
Emotion plays crucial roles in human life. Recently, emotion classification from
electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid …
electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid …
SIM-CNN: self-supervised individualized multimodal learning for stress prediction on nurses using biosignals
S Eom, S Eom, P Washington - Workshop on Machine Learning for …, 2023 - Springer
Precise stress recognition from biosignals is inherently challenging due to the
heterogeneous nature of stress, individual physiological differences, and scarcity of labeled …
heterogeneous nature of stress, individual physiological differences, and scarcity of labeled …
Attx: Attentive cross-connections for fusion of wearable signals in emotion recognition
We propose cross-modal attentive connections, a new dynamic and effective technique for
multimodal representation learning from wearable data. Our solution can be integrated into …
multimodal representation learning from wearable data. Our solution can be integrated into …
Unlocking the potential of two-point cells for energy-efficient and resilient training of deep nets
Context-sensitive two-point layer 5 pyramidal cells (L5PCs) were discovered as long ago as
1999. However, the potential of this discovery to provide useful neural computation has yet …
1999. However, the potential of this discovery to provide useful neural computation has yet …
Hybrid densenet with long short-term memory model for multi-modal emotion recognition from physiological signals
A Pradhan, S Srivastava - Multimedia Tools and Applications, 2024 - Springer
Recognition of emotions from multi-modal physiological signals is one among the toughest
tasks prevailing amid the research communities. Most existing works have focused on …
tasks prevailing amid the research communities. Most existing works have focused on …
Can gaze inform egocentric action recognition?
We investigate the hypothesis that gaze-signal can improve egocentric action recognition on
the standard benchmark, EGTEA Gaze++ dataset. In contrast to prior work where gaze …
the standard benchmark, EGTEA Gaze++ dataset. In contrast to prior work where gaze …
Context-sensitive neocortical neurons transform the effectiveness and efficiency of neural information processing
Deep learning (DL) has big-data processing capabilities that are as good, or even better,
than those of humans in many real-world domains, but at the cost of high energy …
than those of humans in many real-world domains, but at the cost of high energy …
Vital sign forecasting for sepsis patients in ICUs
Sepsis and septic shock are a critical medical condition affecting millions globally, with a
substantial mortality rate. This paper uses state-of-the-art deep learning (DL) architectures to …
substantial mortality rate. This paper uses state-of-the-art deep learning (DL) architectures to …