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Found 5 papers, 0 papers with code
Pre-trained Model Representations and their Robustness against Noise for Speech Emotion Analysis
We investigated the use of pre-trained model representations for estimating dimensional emotions, such as activation, valence, and dominance, from speech.
MAEEG: Masked Auto-encoder for EEG Representation Learning
We propose a reconstruction-based self-supervised learning model, the masked auto-encoder for EEG (MAEEG), for learning EEG representations by learning to reconstruct the masked EEG features using a transformer architecture.
Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation
We investigate the use of pre-trained model representations to improve valence estimation from acoustic speech signal.
Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay
Altogether, we found that LSTM with the proposed forget gate can learn long-term dependencies, outperforming other recurrent networks in multiple domains; such gating mechanism can be integrated into other architectures for improving the learning of long timescale information in recurrent neural networks.
Mapping the Timescale Organization of Neural Language Models
In summary, we demonstrated a model-free technique for mapping the timescale organization in recurrent neural networks, and we applied this method to reveal the timescale and functional organization of neural language models.
