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Found 5 papers, 1 papers with code
Emotion recognition based on multi-modal electrophysiology multi-head attention Contrastive Learning
Emotion recognition is an important research direction in artificial intelligence, helping machines understand and adapt to human emotional states.
SENet: A Spectral Filtering Approach to Represent Exemplars for Few-shot Learning
Furthermore, a shrinkage exemplar loss is proposed to replace the widely used cross entropy loss for capturing the information of individual shrinkage samples.
Kernel Relative-prototype Spectral Filtering for Few-shot Learning
In this paper, we propose a framework of spectral filtering (shrinkage) for measuring the difference between query samples and prototypes, or namely the relative prototypes, in a reproducing kernel Hilbert space (RKHS).
Deep Reinforcement Learning With Adaptive Combined Critics
Then the updated policy is involved in the update of the weight factor, in which we propose a novel method to provide theoretical and experimental guarantee for future policy improvement.
Unbiased Deep Reinforcement Learning: A General Training Framework for Existing and Future Algorithms
These algorithms prove to be far more efficient than their original versions under the framework of deep reinforcement learning, and provide examples for existing and future algorithms to generalize to our new framework.
