ICML 2026重磅论文：进化策略替代强化学习，开创大模型微调新范式。
arXiv:2509.24372v3 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) for downstream tasks is an essential stage of modern AI d…
用AutoML多目标优化深度移位神经网络，实现可持续深度学习，降低能耗同时保持模型性能。
arXiv:2606.23208v1 Announce Type: new Abstract: Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. How…
探索AutoML在多目标超参数优化中的应用，为深度移位神经网络带来可持续性提升。
arXiv:2404.01965v3 Announce Type: replace-cross Abstract: Deep Learning (DL) has advanced various fields by extracting complex patterns from large dat…
用大模型引导索引优化，突破人-物交互检索的多参数耦合瓶颈，高效又精准。
arXiv:2606.05489v1 Announce Type: new Abstract: Retrieval systems underpin modern AI applications -- spanning visual search, recommendation engines, a…
ACL 2026接收的最新研究，提出一种高效超参数优化方法，助力LLM强化学习落地。
arXiv:2606.03073v1 Announce Type: cross Abstract: Reinforcement learning (RL) for large language models (LLMs) is highly sensitive to hyperparameter c…
大模型驱动自动特征工程，协作贝叶斯超参数优化提效，KDD 2026前沿方法。
arXiv:2602.09851v2 Announce Type: replace Abstract: Feature Engineering (FE) is pivotal in automated machine learning (AutoML) but remains a bottlenec…