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Found 6 papers, 4 papers with code
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis
Much research focuses on modeling the complex intra- and inter-modal interactions between different communication channels.
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition
Deep neural networks have been shown as a class of useful tools for addressing signal recognition issues in recent years, especially for identifying the nonlinear feature structures of signals.
Self-supervised Representation Learning with Relative Predictive Coding
This paper introduces Relative Predictive Coding (RPC), a new contrastive representation learning objective that maintains a good balance among training stability, minibatch size sensitivity, and downstream task performance.
Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults.
Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis
The human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language.
Complex Transformer: A Framework for Modeling Complex-Valued Sequence
While deep learning has received a surge of interest in a variety of fields in recent years, major deep learning models barely use complex numbers.
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