VQ-Atom discretizes local atomic environments into semantic tokens via vector quantization, reaching AUROC 0.79 on KIBA drug-target interaction prediction while enabling 3x faster downstream training than continuous representations.
Bert: Pre-training of deep bidirectional transformers for language understanding
2 Pith papers cite this work. Polarity classification is still indexing.
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An LLM multi-modal system integrates topic modeling, transformer sentiment, and behavioral features to predict MOOC learner satisfaction more accurately than single-modality baselines.
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VQ-Atom: Semantic Discretization of Local Atomic Environments for Molecular Representation Learning
VQ-Atom discretizes local atomic environments into semantic tokens via vector quantization, reaching AUROC 0.79 on KIBA drug-target interaction prediction while enabling 3x faster downstream training than continuous representations.
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Predicting User Satisfaction in Online Education Platforms: A Large Language Model Based Multi-Modal Review Mining Framework
An LLM multi-modal system integrates topic modeling, transformer sentiment, and behavioral features to predict MOOC learner satisfaction more accurately than single-modality baselines.