[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

### 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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