Dynamic Shape Compiler for Machine Learning Workloads EmbeddingVariable [No Hash Conflict] 特征准入/淘汰 Adaptive Embedding 训练: 推理: Ring All-reduc同步训练 [HybridBackend/SOK] 特征选择 [VariationalDropout] 通信优化 [GRPC++] [split/type conversion] Sequence Feature [side info] Op Fusion [hash + embedding] Overlap Execution [FG OP化] Item Feature增量更新 3.工程优化复 杂 4.数据获取困 难 挑战 深度模型是非线性的: • 参数很多 • 参数敏感 • 不同场景的数据上差异大

results. The trials are independent of each other which makes them a good candidate for parallel execution. For example, the trial set for two hyperparameters and where and is Figure 7-2 (a) shows results because the trials are largely independent of each other until the mutation phase. They work well for adaptive hyperparameters such as learning rate and weight decay. The hyperparameter search can be extended

各种机器学习问题 25 办比赛14来完成这项工作。 搜索 有时，我们不仅仅希望输出一个类别或一个实值。在信息检索领域，我们希望对一组项目进行排序。以网络 搜索为例，目标不是简单的“查询（query）‐网页（page）”分类，而是在海量搜索结果中找到用户最需要的 那部分。搜索结果的排序也十分重要，学习算法需要输出有序的元素子集。换句话说，如果要求我们输出字 母表中的前5个字母，返回“A、 以简单地使用 参数化的全连接层，甚至是非参数化的最大汇聚层或平均汇聚层。 因此，“是否包含自主性提示”将注意力机制与全连接层或汇聚层区别开来。在注意力机制的背景下，自主性 提示被称为查询（query）。给定任何查询，注意力机制通过注意力汇聚（attention pooling）将选择引导至 感官输入（sensory inputs，例如中间特征表示）。在注意力机制中，这些感官输入被称为值（value）。更通 key_size, query_size, num_hiddens, dropout, **kwargs): super(AdditiveAttention, self).__init__(**kwargs) self.W_k = nn.Linear(key_size, num_hiddens, bias=False) self.W_q = nn.Linear(query_size, num_hiddens

● Gradient-based optimization algorithms that adjust network parameters to reduce error. (See Adaptive Learning Rate lecture video) ● E.g. Stochastic Gradient Descent (SGD) torch.optim.SGD(model.parameters()

Multi-Scenario Ranking in E-commerce 端上 重排 场景1 场景X [CIKM2021] One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction � 预训练模型Bert GPT-3在CV/NLP⼤⾏其道， 相关技术正在进⼊推荐领域 问题1. 推荐链路的漏⽃

AdaBoost和GBDT算法 01 集成学习方法概述 02 AdaBoost和GBDT算法 03 XGBoost 04 LightGBM 11 AdaBoost算法 AdaBoost（Adaptive Boosting，自适应增强），其自适应在于：前 一个基本分类器分错的样本会得到加强，加权后的全体样本再次被用来 训练下一个基本分类器。同时，在每一轮中加入一个新的弱分类器，直 到达到

2019. 5 Mocanu, Decebal Constantin, et al. "Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science." Nature communications 9.1 (2018): 1-12. Weight

学习率. • epsilon: float >= 0. 若为 None, 默认为 K.epsilon(). • decay: float >= 0. 每次参数更新后学习率衰减值. 引用 • Adaptive Subgradient Methods for Online Learning and Stochastic Optimization 优化器 OPTIMIZERS 141 9.2.4 Adadelta >= 0. 模糊因子. 若为 None, 默认为 K.epsilon(). • decay: float >= 0. 每次参数更新后学习率衰减值. 引用 • Adadelta - an adaptive learning rate method 9.2.5 Adam [source] keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0

However, RNNs are slow to train because of their sequential design such that the current timestamp execution depends on the results of previous timestep. Another drawback of a sequential architecture is the (Luong) mechanism learns three weight matrices namely WQ (query weight), WK (key weight) and WV (value weight) which are used to compute the query, key and value matrices for input sequences. Then, a softmax softmax is applied to the scaled dot product of query and key matrices to obtain a score matrix (figure 4-16). Finally, the values are weighted based on the positional relationship encoded in the score matrix

racy，简称 acc)，精度(Precision) 和召回率(Recall)等。 考虑某个模型的训练曲线，如图 4.1 所示，横坐标为训练步数 Step，纵坐标分别为 Loss per Query Image 误差变化趋势(图 4.1(a))和准确率 Accuracy 变化趋势曲线(图 4.1(b))，其中损失值和准确率均由张量计算产生，类型为标量，因此可以直接可视化为曲 线图。 (a) 其中???(??(∙ |??)||??̅(∙ |??))指策略分布??(∙ |??)和??̅(∙ |??)之间的距离，超参数?用于平衡 原损失项与 KL 散度惩罚项。 自适应 KL 惩罚项(Adaptive KL Penalty)算法 通过设置 KL 散度的阈值KLmax来动态调 整超参数?。调整规则如下：如果?̂?[???(??(∙ |??)||??̅(∙ |??))] > KLmax，则增加