墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
100+累计阅读
58平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
本文聚焦图挖掘领域中异质性（多种节点和边类型）与异嗜性（相连节点标签/属性差异）并存的问题，现有基准测试仅侧重单一特性，存在研究缺口。为此，作者提出异嗜性和异质性图基准测试（H²GB），涵盖9个真实世界数据集（跨学术、金融、电商、社会科学、网络安全5个领域）、28个基线模型，以及标准化的基准测试库（含数据加载器、评估器、建模框架）。
通过系统实验，作者发现现有模型在同时处理异质性和异嗜性时性能受限：同质异嗜性GNN因无法利用多样节点/边类型而表现不佳，异质同嗜性GNN的性能依赖其处理异嗜性的架构鲁棒性，部分模型存在扩展性问题。基于此，作者遵循H²GB的标准化流程，提出新模型H²G-former，通过掩码标签嵌入、异质注意力等组件，显著提升了在基准测试中的性能。
Best Papers
Graph Neural Network
AI 应用开发：四维架构落地实战——以 HR Agent 为例拆解感知·推理·记忆·执行【意图路由、规划编排、长期记忆、工具执行、面试题】
本文基于四维架构（感知·推理·记忆·执行），以HR Agent为例，系统拆解大模型应用从“聊天”到“生产可用”的落地路径。通过Spring AI实现意图路由、规划编排、RAG检索与工具调用，结合真实踩坑复盘，揭示架构化设计对安全、成本与可维护性的关键价值，为AI Agent工程化提供可复用的实战框架。
RC滤波电路工作原理：【带图+公式计算】
容抗Xc和电容C成反比，和频率f也成反比，C越大，交流电容易通过电容，说明电容量大，电容的阻碍作用小；交流电的频率高，交流电也容易通过电容，说明频率高，电容的阻碍作用也小。我们可以看到，一个2V的纹波，经常RC电路后，幅值下降到mv级别，基本可以忽略不计，这就是RC滤波电路的工作原理。一个频率为1Khz的信号，通过一个容值为0.1uf的电容，计算电容对该信号产生的阻抗。将RC滤波器电路中的电阻，取值为1KΩ，电容取值为0.47uf，纹波幅值为2V。从公式1可以看到，Xc越大，Vout值就越接近Vin的值。
支付漏洞防护绕过的一种方式
放完包后就到了提交订单的地方,可以发现,金额确实小了很多,合理的搭配的话还可以更低,再次前进可以发现,金额确实是这么多,后面就不支付了,到此就结束了。也就是说数量可以为负数,但是金额不能为0或者0以下,有什么方法可以绕过这个限制吗,其实并不难,既然可以为负数,只是对金额不能为0或者负数做了限制。我们可以尝试拼接法,利用负数加上正数让他的值变得很低,但是又大于0就可以了,这里利用购物车的结算方法,随机加一些在购物车里。这里为了方便查看数据包,我这边商品的数量选择3,点击立即购买并抓包。
非标准包 game.rgss3a 的打开方法 | 2023 年实测
生活笔记
【ATC/DTC方法第四期】GOT09-dT-tao 昼夜温度周期模型方法介绍（含Python代码实现）
GOT09-dT-tao 模型是一种经过简化和参数固定的 昼夜温度周期模型（Diurnal Temperature Cycle, DTC）。它从 GOT09 模型（Göttsche and Olesen, 2009）衍生而来，但更加轻量化，更适合在数据不足或参数估计困难的情境下使用。
直击高频编程考点：字符串知识及经典算法题总结
字符串相关基本练习题总结（最长公共子串、最长回文子串、最长无重复字符子串、最小覆盖子串、字符串相乘、中文数字表达转实际数字格式等）
uniapp [全端兼容] - 详细实现人脸识别和活体检测功能，实时监测人脸是否进入区域及动作是否正确播报，免费自动提示引导动作及语音，提供提供实名认证、身份证识别、人脸信息采集、刷脸扫脸认证示例代码
uniapp，h5移动端网页，微信小程序，支付宝小程序，Android安卓，苹果ios，app，nvue，人脸识别，扫脸认证，人脸认证，活体认证，身份认证，uni-app人脸识别详细教程，如何在app端实现免费人脸识别教程，如何在小程序端做，最好用的插件，安卓ios百度人脸识别、活体检测、人脸采集APP，H5人脸识别带前后摄像头，适合新手看的人脸识别功能教程，详细示例代码源码，有没有免费的，接入百度，阿里云人脸识别，百度，api，活体检测，数据采集，动作提示指引，语音提示指引，报错，无法核验一直出错，根本用
Roboguide与TIA V16通讯
Roboguide与TIA V16通讯
AIGC实战——多模态模型DALL.E 2
DALL.E 2 是 OpenAI 提出的一个大规模文本生成图像模型，可以根据文本提示生成各种风格的逼真图像。它通过将预训练模型 CLIP 与 GLIDE 中的扩散模型架构相结合完成任务。它还具有其他应用，例如通过文本提示编辑图像和提供给定图像的变体。虽然它也有一些局限性，例如不一致的文本渲染和属性绑定，但 DALL.E 2 是仍然一个非常强大的AI模型，推动了生成模型的发展。
lwip-2.0.3
lwip是瑞典计算机科学院(SICS)的Adam Dunkels 开发的一个小型开源的TCP/IP协议栈。实现的重点是在保持TCP协议主要功能的基础上减少对RAM 的占用。
Data-Structures-and-Algorithm-Analysis-in-Java.pdf
作者: Weiss, Mark A. 出版社: Addison Wesley Data Structures and Algorithm Analysis in Java is an “advanced algorithms” book that fits between traditional CS2 and Algorithms Analysis courses. In the old ACM Curriculum Guidelines, this course was known as CS7. This text is for readers who want to learn good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amo...
efm32 入门.pdf
本PDF共计22页，介绍的是操作驱动API，下载与否自己斟酌。
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
超级会员免费看
订阅专栏 解锁全文
920
扫一扫
