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
300+累计阅读
371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
该研究针对上下文强化学习（ICRL）面临的状态-动作-奖励数据多模态性和任务多样性/异质性两大核心挑战，提出了融合混合专家（MoE）架构的创新框架T2MIR（Token- and Task-wise MoE for In-context RL）。T2MIR通过替换Transformer的前馈层为两个并行MoE层（令牌级MoE和任务级MoE），分别处理多模态语义差异和任务梯度冲突，并结合对比学习增强任务路由精度。基于AD和DPT两种主流ICRL骨干网络，T2MIR在离散/连续控制、导航、机器人操作等多个基准测试中，显著优于现有基线方法，且在不同质量数据集上表现出强鲁棒性。
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。
ArcGIS天地图矢量化秘籍：用重分类+ArcScan高效提取建筑轮廓线
本文详细介绍了利用ArcGIS重分类和ArcScan技术高效提取天地图建筑轮廓线的半自动化流程。通过动态中断值计算、多级重分类策略和ArcScan高级矢量化技巧，显著提升城市规划中的矢量化效率与精度，适用于中级GIS用户。
避坑指南：合宙Air780EG阿里云一型一密连接时，为什么你的HmacMD5加密总失败？
本文针对合宙Air780EG模组连接阿里云物联网平台时，HmacMD5签名失败的常见问题提供深度解决方案。详细剖析了签名拼接格式、密钥使用、随机数处理及编码大小写等关键陷阱，并结合STM32嵌入式开发环境，给出了串口调试、分步验证及AT指令链路的实用排错技巧，帮助开发者顺利完成一型一密动态注册。
华为云Stack交付实战：从工勘到上线的全流程拆解
本文详细拆解华为云Stack交付全流程，从项目启动、工勘、LLD设计到硬件部署和软件安装，提供实战技巧与避坑指南。重点解析华为云Stack交付流程中的关键环节，如标准化工具使用、规范化文档管理和流程化协作，帮助团队高效完成从工勘到上线的复杂项目交付。
STM32通过ESP8266连接机智云APP（第二讲，烧录固件并测试）
今天与大家分享STM32通过ESP8266连接机智云APP，并完成数据的上传下发第二讲。带大家一起烧录机智云固件到ESP8266并且将其通过机智云串口助手与APP进行测试。
利用SIMULINK搭建一个16QAM调制解调收发系统
1发送模块设计 根据前面介绍的理论知识，我们可以知道16QAM的基本结构，首先我来设计QAM发送段的SIMULINK仿真模块。 图1 16QAM的发送端模块 他的基本工作流程为：将信号通过两路升余弦滤波器，得到滤波以后的信号，滤波器的作用就是为了防止码间干扰。然后将滤波以后的信号和SIN,COS相乘得到调制信号，将调制 下面一一介绍每个模块的具体参数设置以及各个部分信号的波形图。 图2 产生随机数据的信号源 第一部分信号是产生一组随机的数字，作为信源，第二个模块将信号...
GameFramework框架详解之 Network网络框架
NetworkManager只负责维护NetworkChannel，实际的有种我们简历网络连接的也是NetworkChannel作为一个网路频道，这样管理起来比较方便，比如：一个游戏可能有多个网络连接，那么我们建立多个网络频道即可，或者当我们一个网络频道断线后，我们可以直接再新建一个，防止数据混乱。/// /// 网络管理器。/// /// 网络频道。/// 用户自定义网络错误事件。/// 检查是否存在网络频道。/// 获取网络频道。/// 获取所有网络频道。
从0到1带大家搭建spring cloud alibaba 微服务大型应用框架（十一）spring-boot-admin 监控篇（2）springcloud 集成spring boot admin
从0到1 手把手搭建spring cloud alibaba 微服务大型应用框架（十一）spring-boot-admin 监控篇（2）springcloud 集成spring boot admin spring-boot-admin 服务端创建与集成入nacos spring-boot-admin 查看日志
ZYNQ USB按键读写操作详解：从裸机到Linux系统的完整实现
USB接口在现代嵌入式系统中扮演着至关重要的角色，而按键作为最基础的人机交互设备，其与USB的结合能够实现丰富的应用场景。这种实现不仅适用于简单的键盘应用，还可以扩展为游戏控制器、专业输入设备或工业控制面板，展现了ZYNQ在嵌入式USB应用中的强大灵活性。：USB控制器需要准确的时钟源，通常使用外部33MHz晶振，并在Vivado中正确配置。结合ZYNQ的PL（可编程逻辑）和PS（处理系统）优势，可以创建创新的USB按键应用。本文详细介绍了在ZYNQ平台上实现USB按键读写的完整方案，涵盖了从。
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。
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")
IMSL CNL 7.0.0 (x86-64)Crack
IMSL CNL 7.0.0 (x86-64)Crack intel的算法库破解文件，这是C++版本的破解文件
Camera2Demo
在Camera2 的基础上进行封装，使得Camera2一堆初始化逻辑与Activity分离，降低耦合度，完成预览拍照并保存的操作
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
735
扫一扫
