基于深度卷积神经网络的 ImageNet 分类
ImageNet Classification with Deep Convolutional Neural Networks
深度卷积网络将数据、GPU 计算与表示学习结合，为现代视觉研究提供了重要起点。
THE RESEARCH ATLAS
Qwen3 技术报告
稠密与混合专家模型结合思考、非思考模式，探索可调节的推理计算。
Qwen3 Technical Report
为什么值得读
多语言模型与推理预算权衡的一份业界参考。
引用
@misc{yang2025qwen3,
title = {Qwen3 Technical Report},
author = {An Yang and Anfeng Li and Baosong Yang and Beichen Zhang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Gao and Chengen Huang and Chenxu Lv and Chujie Zheng and Dayiheng Liu and Fan Zhou and Fei Huang and Feng Hu and Hao Ge and Haoran Wei and Huan Lin and Jialong Tang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Yang and Jiaxi Yang and Jing Zhou and Jingren Zhou and Junyang Lin and Kai Dang and Keqin Bao and Kexin Yang and Le Yu and Lianghao Deng and Mei Li and Mingfeng Xue and Mingze Li and Pei Zhang and Peng Wang and Qin Zhu and Rui Men and Ruize Gao and Shixuan Liu and Shuang Luo and Tianhao Li and Tianyi Tang and Wenbiao Yin and Xingzhang Ren and Xinyu Wang and Xinyu Zhang and Xuancheng Ren and Yang Fan and Yang Su and Yichang Zhang and Yinger Zhang and Yu Wan and Yuqiong Liu and Zekun Wang and Zeyu Cui and Zhenru Zhang and Zhipeng Zhou and Zihan Qiu},
year = {2025},
eprint = {2505.09388},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2505.09388}
}论文与代码版权归原作者；引用及许可请以原始来源为准。
相关研究
注意力即全部
Attention Is All You Need
Transformer 用注意力机制构建序列模型，成为后续语言与多模态模型的重要基础。
BERT：面向语言理解的深度双向 Transformer 预训练
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
通过掩码语言预训练学习双向表示，再迁移到语言理解任务。