[warm-up] MS Academy Search

本文对比了MS Academy Search (MSAS) 和Google Scholar在论文及作者搜索方面的表现。MSAS不仅提供专业的搜索结果,还展示了引用和被引用论文,并通过视觉探索帮助读者了解作者的合作网络。

It's said MS Academy Search (MSAS) launched a new version. I've used it several times. I'm glad to show my experence.
When I search a paper. I found that, MSAS is more professional than Google scholar search. Google scholar's search results are a mass of webpages which related with my keywords, while MSAS's Search results are well formed webpages. They are contributed by eager live users.
It's amazing that MSAS not only list the reference of paper , but also tell the user which papers cite the paper. That's useful to user. Because it indicates where you can get the latest work by other researchers.
When I search an author, I get more surprise. MSAS know what I want. That's Publications, Citations, Interest, Co-author, Publication and Citation. But Google Scholar makes me said. It's still a mass of webpages. The visual explorer from MSAS is very interesting. It's said, love me, love my dog. You can easily find who are the author's close co-author. It's useful to get a large view of the author you are interested in.
MSAS is not only a search but also a wiki. Everyone can upload what's he know to MSAS. Due to that, MSAS could provide more information to user, such as author's information and citations.
MSAS is also a portal of academic. You can get academic information by category, such as publication, author, conference and so on.  It's helpful when you don't exactly know what you want.
On the other hand, as a portal and wiki, lots of work are done manually. I think that would be a great challenge to MSAS. I found some mistakes with the author. That wouldn't be born by strict user. By the way, the publication collected by MSAS is much less than the pages cached by Google scholar search. I think a smarter robot may bring much help.
If I'm the leader of the project. I would statistics users' preference and demand first. I think the limited publications maybe . As mentioned before, I'll focus on the smart robot. I'll enable him to collect more publication and pick up the information from the publication. And, I'll enable another robot to check the authors' information, including the information upload by users. I think another approach to get publication is cooperating with l iterature r etrieval provider.
Best wish to MSAS!

 

MicroTeam Hui

六自由度机械臂ANN人工神经网络设计:正向逆向运动学求解、正向动力学控制、拉格朗日-欧拉法推导逆向动力学方程(Matlab代码实现)内容概要:本文档围绕六自由度机械臂的ANN人工神经网络设计展开,详细介绍了正向与逆向运动学求解、正向动力学控制以及基于拉格朗日-欧拉法推导逆向动力学方程的理论与Matlab代码实现过程。文档还涵盖了PINN物理信息神经网络在微分方程求解、主动噪声控制、天线分析、电动汽车调度、储能优化等多个工程与科研领域的应用案例,并提供了丰富的Matlab/Simulink仿真资源和技术支持方向,体现了其在多学科交叉仿真与优化中的综合性价值。; 适合人群:具备一定Matlab编程基础,从事机器人控制、自动化、智能制造、电力系统或相关工程领域研究的科研人员、研究生及工程师。; 使用场景及目标:①掌握六自由度机械臂的运动学与动力学建模方法;②学习人工神经网络在复杂非线性系统控制中的应用;③借助Matlab实现动力学方程推导与仿真验证;④拓展至路径规划、优化调度、信号处理等相关课题的研究与复现。; 阅读建议:建议按目录顺序系统学习,重点关注机械臂建模与神经网络控制部分的代码实现,结合提供的网盘资源进行实践操作,并参考文中列举的优化算法与仿真方法拓展自身研究思路。
### TensorFlow Warm-Up 概念与实现 Warm-up 是机器学习训练过程中的一种常见策略,用于逐步调整优化器的学习率。其主要目的是防止在训练初期由于过高的学习率而导致模型参数更新不稳定或发散。通过逐渐增加学习率,在初始阶段让模型适应数据分布后再进入正常训练过程。 #### 温度提升(Warm-up)的作用 温度提升的核心在于控制学习率的变化曲线。通常情况下,warm-up 阶段会采用线性增长或其他平滑函数来调节学习率。这种方法特别适用于大规模深度神经网络以及分布式训练环境下的收敛加速[^1]。 以下是基于 TensorFlow 的 warm-up 实现方式: ```python import tensorflow as tf def learning_rate_with_warmup(global_step, total_steps, warmup_steps, base_lr): """ 定义带有 warm-up 的学习率调度器。 参数: global_step: 当前全局步数 total_steps: 总训练步数 warmup_steps: warm-up 步骤数量 base_lr: 基础学习率 返回: 调整后的学习率 """ # 判断当前是否处于 warm-up 阶段 if global_step < warmup_steps: # 使用线性增长的方式计算学习率 lr = (base_lr / warmup_steps) * global_step else: # 训练后期使用余弦退火等方式降低学习率 decayed_lr = 0.5 * base_lr * (1 + tf.cos( tf.constant(tf.math.pi) * (global_step - warmup_steps) / (total_steps - warmup_steps))) lr = max(decayed_lr, 1e-7) # 设置最小学习率为避免数值不稳定性 return lr # 创建自定义回调以应用上述逻辑 class LearningRateSchedulerWithWarmUp(tf.keras.callbacks.Callback): def __init__(self, total_steps, warmup_steps, base_lr): super(LearningRateSchedulerWithWarmUp, self).__init__() self.total_steps = total_steps self.warmup_steps = warmup_steps self.base_lr = base_lr def on_batch_begin(self, batch, logs=None): current_step = tf.cast(self.model.optimizer.iterations, dtype=tf.float32) new_lr = learning_rate_with_warmup(current_step, self.total_steps, self.warmup_steps, self.base_lr) tf.keras.backend.set_value(self.model.optimizer.lr, new_lr) # 示例调用 model.compile(optimizer=tf.keras.optimizers.Adam(), loss='categorical_crossentropy', metrics=['accuracy']) callback = LearningRateSchedulerWithWarmUp(total_steps=10000, warmup_steps=1000, base_lr=0.001) history = model.fit(train_dataset, epochs=10, callbacks=[callback]) ``` 此代码片段展示了如何利用 TensorFlow 自定义学习率调度器完成 warm-up 过程,并结合余弦退火机制进一步改善性能表现。 #### 并行化中的注意事项 当涉及多设备并行训练时(例如张量并行[Tensor Parallelism][^2]),需注意不同设备间同步频率可能影响 warm-up 效果。因此建议适当延长 warm-up 时间窗口或者依据具体硬件配置微调超参设置。 此外值得注意的是,尽管本文档提及的技术未来可能会集成到更高级别的 API 中去简化操作流程,但在实际项目开发期间仍推荐手动管理此类细节以便获得最佳效果。 #### 结论 综上所述,通过对 TensorFlow 学习率引入渐进式的升温机制能够显著提高复杂模型的稳定性和最终精度水平。同时也要兼顾特定应用场景下其他因素的影响作出合理权衡决策。
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