Tag: adversarial machine learning
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Boost LLMs with Context Training & Active Info Seeking
Enhance large language models using context training with active information seeking for better adaptability and performance in specialized tasks.
Bridging Human and VLM Scene Perception Gaps with CSS
Discover how Counterfactual Semantic Saliency reveals key gaps between human and vision-language model scene perception for better AI alignment.
FeatCal: Efficient Feature Calibration for Merged AI Models
Discover FeatCal, a fast and efficient feature calibration method that improves performance in merged AI models by minimizing feature drift.
Accelerating Masked Diffusion Language Model Training
Discover strategies to speed up masked diffusion language model training while boosting performance and efficiency in NLP tasks.
Efficient Graph Coarsening with Non-Selfishness Principle
Discover NOPE, a novel graph coarsening method that boosts efficiency with linear memory and near-linear computation for large-scale graphs.