《人工智能与数据科学前沿》(FAIDS) 发表机器学习、深度学习、自然语言处理、计算机视觉和数据驱动科学发现的前沿研究。
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研究论文
Vision-Language Foundation Models for Zero-Shot Autonomous Driving Scene Understanding and Risk Assessment
Autonomous driving perception systems trained on fixed taxonomies fail when encountering novel objects or unusual scenarios not represented in their training data — the "long tail" problem. We present DriveLM, a... -
研究论文
Graph Neural Networks with Heterogeneous Message Passing for Multi-Scale Drug-Drug Interaction Prediction
Adverse drug-drug interactions (DDIs) cause approximately 195,000 hospitalizations annually in the US alone. Existing computational DDI prediction methods operate at a single biological scale — either molecular... -
研究论文
Causal Transformer Networks for Counterfactual Reasoning in Large-Scale Recommendation Systems
Modern recommendation systems suffer from popularity bias, filter bubbles, and spurious correlations that degrade long-term user satisfaction. We introduce CausalRec, a Transformer-based architecture that integrates... -
研究论文
Quantum-Classical Hybrid Variational Algorithms for Large-Scale Combinatorial Optimization on NISQ Devices
Combinatorial optimization problems — vehicle routing, portfolio optimization, network design — are ubiquitous in industry yet NP-hard in general. Quantum approximate optimization algorithms (QAOA) promise quantum... -
综述文章
Efficient Sparse Mixture-of-Experts Models for Multilingual Low-Resource Machine Translation
Low-resource machine translation (MT) for the world's 7,000+ languages remains a critical NLP challenge. Dense multilingual models sacrifice per-language quality for breadth, while dedicated bilingual models are...
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