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LLM-Pruner: On the Structural Pruning of Large Language Models Xinyin Ma Gongfan Fang Xinchao Wang National University of Singapore
Large language models (LLMs) have shown remarkable capabilities in language understanding and generation.
Optimal Efficiency-Envy Trade-Offvia Optimal Transport
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Particle Cloud Generationwith Message Passing Generative Adversarial Networks
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RegBN: Batch Normalization of Multimodal Data with Regularization
However, the integration of heterogeneous multimodal data poses a significant challenge, as confounding effects and dependencies among such heterogeneous data sources introduce unwanted variability and bias, leading to suboptimal performance of multimodal models. Therefore, it becomes crucial to normalize the low-or high-level features extracted from data modalities before their fusion takes place.