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。
以 Cosmos-Reason 与扩散轨迹解码器连接驾驶推理和动作预测,并研究推理与行动的一致性。
Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
把推理、轨迹生成与车辆评估放在同一条阅读路径中,区分可解释的说明与可靠的执行。
原始 R1 论文明确使用 Cosmos-Reason 作为视觉—语言基础,并用扩散模型生成轨迹。
本文对应固定的 R1 初版,不混同后续 Alpamayo 1 页面。仿真与道路试验不等于普遍安全证明或监管认证。
沿着这些连接继续阅读 ↗@misc{nvidia2025alpamayo,
title = {Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail},
author = {NVIDIA and : and Yan Wang and Wenjie Luo and Junjie Bai and Yulong Cao and Tong Che and Ke Chen and Yuxiao Chen and Jenna Diamond and Yifan Ding and Wenhao Ding and Liang Feng and Greg Heinrich and Jack Huang and Peter Karkus and Boyi Li and Pinyi Li and Tsung-Yi Lin and Dongran Liu and Ming-Yu Liu and Langechuan Liu and Zhijian Liu and Jason Lu and Yunxiang Mao and Pavlo Molchanov and Lindsey Pavao and Zhenghao Peng and Mike Ranzinger and Ed Schmerling and Shida Shen and Yunfei Shi and Sarah Tariq and Ran Tian and Tilman Wekel and Xinshuo Weng and Tianjun Xiao and Eric Yang and Xiaodong Yang and Yurong You and Xiaohui Zeng and Wenyuan Zhang and Boris Ivanovic and Marco Pavone},
year = {2025},
eprint = {2511.00088v1},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2511.00088v1}
}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。
Denoising Diffusion Probabilistic Models
模型学习逐步反转加噪过程,为图像生成建立了重要的扩散建模路线。
Learning Transferable Visual Models From Natural Language Supervision
通过对比学习对齐图像与自然语言描述,使视觉表示能够迁移到新类别。