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。
面向编程智能体的开放权重模型,探索较少激活参数与强化训练配方的结合。
Qwen3-Coder-Next Technical Report
不要只看参数量,也要阅读智能体评测设置与训练配方。
@misc{cao2026qwen3,
title = {Qwen3-Coder-Next Technical Report},
author = {Ruisheng Cao and Mouxiang Chen and Jiawei Chen and Zeyu Cui and Yunlong Feng and Binyuan Hui and Yuheng Jing and Kaixin Li and Mingze Li and Junyang Lin and Zeyao Ma and Kashun Shum and Xuwu Wang and Jinxi Wei and Jiaxi Yang and Jiajun Zhang and Lei Zhang and Zongmeng Zhang and Wenting Zhao and Fan Zhou},
year = {2026},
eprint = {2603.00729},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2603.00729}
}论文与代码版权归原作者;引用及许可请以原始来源为准。
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。
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: 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。