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Junchen Shen

2 accepted papers

2024

High-Order Contrastive Learning with Fine-grained Comparative Levels for Sparse Ordinal Tensor Completion

ICML 2024poster

Contrastive learning is a powerful paradigm for representation learning with prominent success in computer vision and NLP, but how to extend its success to high-dimensional tensors remains a challenge. This is because tensor data often exhibit high-order mode-interactions that are hard to profile an…

Cited by 0SourcePDFScholar
2024

Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification

EMNLP 2024main

Emotion classification has wide applications in education, robotics, virtual reality, etc. However, identifying subtle differences between fine-grained emotion categories remains challenging. Current methods typically aggregate numerous token embeddings of a sentence into a single vector, which, whi…

Cited by 1SourcePDFScholar