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谷歌Pregel: A System for Large-Scale Graph Processing
互联网巨头谷歌之前的关于图数据库系统的文章，之前也是苦于图数据库相关的知识太少，希望通过不断收集和整理，让更多有志于在图数据库领域的同学少走弯路，尽可能的吸取各家所长，将图数据库和自己业务结合，发挥出更大价值。
68360发布于 2018-07-03 - 来自专栏机器学习、深度学习
VGG - Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition ICLR 2015 (oral) http://www.robots.ox.ac.uk
1.1K20发布于 2019-05-26 - 来自专栏计算机视觉理论及其实现
VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION(VGG)
在这项工作中，我们研究了卷积网络深度对其在大规模图像识别设置中的准确性的影响。我们的主要贡献是使用一个非常小的(3×3)卷积滤波器的架构对增加深度的网络进行了全面的评估，这表明通过将深度提升到16-19个权重层，可以显著改善先前的配置。这些发现是我们提交的ImageNet挑战赛的基础，我们的团队在定位和分类方面分别获得了第一名和第二名。我们还表明，我们的表现可以很好地推广到其他数据集，在这些数据集上，他们可以获得最先进的结果。我们已经公开了两个性能最好的ConvNet模型，以便进一步研究如何在计算机视觉中使用深度视觉表示。
2.1K00编辑于 2022-09-03 - 来自专栏AIUAI
论文阅读学习 - ModaNet: A Large-scale Street Fashion Dataset with Polygon Annotations
原文：论文阅读学习 - ModaNet: A Large-scale Street Fashion Dataset with Polygon Annotations - AIUAI 题目：ModaNet : A Large-Scale Street Fashion Dataset with Polygon Annotations - 2018 作者：Shuai Zheng，Fan Yang，M.
1.4K10发布于 2019-02-27 - 来自专栏开心的学习之路
VGGNET分类任务——VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
VGGNet于2014年提出，在文献VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION 中有详细介绍。
1.1K91发布于 2019-02-14 - 来自专栏AIUAI
论文阅读学习 - CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
原文：论文阅读学习 - CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images - AIUAI 论文：CurriculumNet : Weakly Supervised Learning from Large-Scale Web Images - ECCV2018 作者：Sheng Guo, Weilin Huang, Haozhi
2K30发布于 2019-02-27 - 来自专栏机器学习、深度学习
车辆密度估计--Understanding Traffic Density from Large-Scale Web Camera Data
Understanding Traffic Density from Large-Scale Web Camera Data CVPR2017 https://arxiv.org/abs/1703.05868
1K30发布于 2019-05-26 - 来自专栏SnailTyan
Very Deep Convolutional Networks for Large-Scale Image Recognition—VGG论文翻译—中文版
Very Deep Convolutional Networks for Large-Scale Image Recognition 摘要 在这项工作中，我们研究了卷积网络深度在大规模的图像识别环境下对准确性的影响 Imagenet: A large-scale hierarchical image database. In Proc. CVPR, 2009. Improving the Fisher kernel for large-scale image classification. In Proc. ECCV, 2010.
1.5K00发布于 2017-12-28 - 来自专栏SnailTyan
Very Deep Convolutional Networks for Large-Scale Image Recognition—VGG论文翻译—中英文对照
Very Deep Convolutional Networks for Large-Scale Image Recognition ABSTRACT In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting 1 INTRODUCTION Convolutional networks (ConvNets) have recently enjoyed a great success in large-scale Imagenet: A large-scale hierarchical image database. In Proc. CVPR, 2009. Improving the Fisher kernel for large-scale image classification. In Proc. ECCV, 2010.
1.1K00发布于 2017-12-28 - 来自专栏罗西的思考
TensorFlow 分布式之论文篇 TensorFlow : Large-Scale Machine Learning on Heterogeneous Distributed Syst
[翻译] TensorFlow 分布式之论文篇 "TensorFlow : Large-Scale Machine Learning on Heterogeneous Distributed Systems " 目录 [翻译] TensorFlow 分布式之论文篇 "TensorFlow : Large-Scale Machine Learning on Heterogeneous Distributed 按照这个原则，本文主要介绍一篇 TensorFlow 经典论文 TensorFlow : Large-Scale Machine Learning on Heterogeneous Distributed 0xFF 参考 TensorFlow 架构 TensorFlow : Large-Scale Machine Learning on Heterogeneous Distributed Systems TensorFlow : a system for large-scale machine learning TensorFlow 分布式采坑记
3.9K20编辑于 2022-05-09 - 来自专栏Deep learning进阶路
深度学习论文随记（二）---VGGNet模型解读-2014年（Very Deep Convolutional Networks for Large-Scale Image Recognition）
本文介绍了深度学习中VGG模型的基本原理、结构、特点以及应用。VGG由K. Simonyan和A. Zisserman于2014年提出，是一种非常经典的卷积神经网络模型。VGG由多个卷积层和全连接层组成，采用3x3的卷积核，并使用ReLU激活函数。VGG在多个图像分类和物体检测任务中取得了良好的效果。同时，VGG也提出了一种多尺度训练的方法，以提取更多的特征信息。
1.4K00发布于 2017-12-28 - 来自专栏distributed cloud
Edge cloud service solution based on mobile micro data center and SDWAN
Background During large-scale events and conferences, the location has a high demand for edge computing During various large-scale events and conferences, the Mini T-Block can be moved to nearby locations, availability zone can be provided in this mobile edge cloud to meet the service needs of different types of large-scale High cost-effectiveness: No need for various participants of large-scale events to independently support After the large-scale event, mobile edge cloud related devices can be moved back to Tencent Central Cloud
21210编辑于 2025-06-16 - 来自专栏JNing的专栏
论文阅读: R-FCN-3000
Introduction R-FCN-3000的定位是 large-scale detector 。 large-scale detector 核心技术 精度 意义 YOLO-9000 语法树 较差 第一个large-scale detector R-FCN-3000 解耦“定位”和“分类” 较好 第一个可实用的 large-scale detector 分类 采用了YOLO-9000中的分类思想： 大类得分 × 细类得分 = 最终分类得分 定位回归 将“定位”和“分类”解耦，避免了R-FCN中对每个类都进行一次
58640发布于 2018-09-27 [Pytorch][转载]VGG模型实现
layer model (configuration "A") with batch normalization `"Very Deep Convolutional Networks For Large-Scale *kwargs): r"""VGG 13-layer model (configuration "B") `"Very Deep Convolutional Networks For Large-Scale layer model (configuration "B") with batch normalization `"Very Deep Convolutional Networks For Large-Scale *kwargs): r"""VGG 16-layer model (configuration "D") `"Very Deep Convolutional Networks For Large-Scale layer model (configuration "D") with batch normalization `"Very Deep Convolutional Networks For Large-Scale
23410编辑于 2025-07-18- 来自专栏人工智能与演化计算成长与进阶
CEC2023征稿|新兴应用中的大规模多目标优化
Call for Papers: CEC 2023 Special Session on “Large-scale multi-objective optimization in emerging applications algorithms on multi-objective optimization, they suffer from the curse of dimensionality when tackling large-scale Special techniques for handling large-scale search spaces, e.g., dimensionality reduction, gradient guidance Please select the main research topic as the Special Session on “Large-scale multi-objective optimization
49930编辑于 2023-02-23 - 来自专栏大数据智能实战
图像检索中的DELF模型（DEep Local Features）实践
： This project presents code for extracting DELF features, which were introduced with the paper "Large-Scale DELF is particularly useful for large-scale instance-level image recognition. 而DELF模型则是ICCV 2017和CVPR 2018（Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking）提到的是当前效果最好的以图搜图的模型 整个系统的四个部分如下： Our large-scale retrieval system can be decomposed into four main blocks: (i) dense
2.6K30发布于 2019-05-26 - 来自专栏图与推荐
KDD'23 Tutorial: 大规模 GNN 的过去和未来
Introduction of GNNs (20minutes) (a) Foundations and Applications of GNNs (b) Scalability Challenges of Large-Scale Large-scale Real-world Applications (20 minutes) 6. Summary and Future Directions (10minutes)
23830编辑于 2023-09-04 - 来自专栏云原生应用工坊
Container Platform and Best Practices Reference
Managing Large-Scale Application Releases Managing large-scale application releases can be a complex Here are some recommendations and best practices to help you manage large-scale applications using Helm Write custom scripts or tools to simplify management tasks for large-scale applications, such as configuration the following: Monitoring and Alerting: Implement effective monitoring and alerting strategies for large-scale following these best practices and utilizing the recommended tools, you can more effectively manage large-scale
53410编辑于 2023-12-12 - 来自专栏AIUAI
Caffe 与 Caffe2
Caffe 与 Caffe2 Caffe： - 适用于large-scale product - unparalleled performance - well tested C+ + codebase - 设计基于传统CNN应用 - 对于新的计算模式不太适应，比如分布式计算、移动计算、低精度计算，以及其它非视觉应用场景 Caffe2： - 支持 large-scale
1.4K20发布于 2019-02-18 - 来自专栏炼丹笔记
炼丹知识点：模型训练里的Tricks
▲模型并行 代表性工作： Megatron-LM: Efficient Large-Scale Language Model Training on GPU Clusters Mesh-Tensorflow 夸父：Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training 里面实现了2D，2.5D，3D并行 1.3 Parallel Approach for Training Large Models 双向化的GPipe，个人看好的一种内存计算折中方案：Chimera: efficiently training large-scale 分布式框架论文（字母序） Colossal-AI，最新的一批框架，主打多维模型并行：Colossal-AI: A Unified Deep Learning System For Large-Scale (2018) Megatron-LM，手动的DP+MP+PP性能baseline：Efficient Large-Scale Language Model Training on GPU Clusters
1.8K30编辑于 2022-05-23
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