performance tradeoff. Next, the chapter goes over weight sharing using clustering. Weight sharing, and in particular clustering is a generalization of quantization. If you noticed, quantization ensures range. It creates equal sized quantization ranges (bins), regardless of the frequency of data. Clustering helps solve that problem by adapting the allocation of precision to match the distribution of the started to combine these two forms to achieve both accuracy and latency gains. Weight Sharing using Clustering Recall that in quantization, we divided the original floating point domain between and into non-overlapping

/ 46 Outline 1 Clustering 2 K-Means Method 3 K-Means Optimization Problem 4 Kernel K-Means 5 Hierarchical Clustering Feng Li (SDU) K-Means December 28, 2021 2 / 46 Clustering Usually an unsupervised labels A good clustering is one that achieves: High within-cluster similarity Low inter-cluster similarity Feng Li (SDU) K-Means December 28, 2021 3 / 46 Similarity can be Subjective Clustering only looks important in clustering Also important to define/ask: “Clustering based on what”? Feng Li (SDU) K-Means December 28, 2021 4 / 46 Clustering: Some Examples Document/Image/Webpage Clustering Image Segmentation

Discovering Clusters Feng Li (SDU) Overview September 6, 2023 29 / 57 Unsupervised Learning: Clustering (Contd.) Feng Li (SDU) Overview September 6, 2023 30 / 57 Unsupervised Learning: Discovering required compared to supervised learning. At the same time, improving the results of unsupervised clustering to the expectations of the user. With lots of unlabeled data the decision boundary becomes apparent Constrained Clustering Distance Metric Learning Manifold based Learning Sparsity based Learning (Compressed Sensing) Feng Li (SDU) Overview September 6, 2023 40 / 57 Constrained Clustering When we have

与此不同的是，在无监督学习中，我们的数据没有附带任何标签?，无 监督学习主要分为聚类、降维、关联规则、推荐系统等方面。 监督学习和无监督学习的区别 5 1.无监督学习方法概述 ✓ 聚类（Clustering） ✓ 如何将教室里的学生按爱好、身高划分为5类？ ✓ 降维（ Dimensionality Reduction ） ✓ 如何将将原高维空间中的数据点映射到低维度的空间中？ ✓ 关联规则（ 内，那么集合 S 称为凸集。反之，为非凸集。 29 密度聚类-DBSCAN DBSCAN密度聚类 与划分和层次聚类方法不同，DBSCAN(Density-Based Spatial Clustering of Applications with Noise)是一个比较有代表性的基于密度的聚类算法。它将簇 定义为密度相连的点的最大集合，能够把具有足够高密度的区域划分为簇，并 可在噪声的空间数据库中发现任意形状的聚类。 类结果与真实情况越吻合。从广义的角度 来讲，ARI衡量的是两个数据分布的吻合 程度 46 参考文献 [1] Wong J A H A . Algorithm AS 136: A K-Means Clustering Algorithm[J]. Journal of the Royal Statistical Society, 1979, 28(1):100-108. [2] Ester M . A

See the [Transport and DOSGi] section of the user guide. Finally, Cellar also provides "runtime clustering" by providing dedicated feature like: • HTTP load balancing • HTTP sessions replication • log |grep -i cellar cellar-core | 3.0.3 | | karaf-cellar-3.0.3 | Karaf clustering core hazelcast | 3.4.2 | | karaf-cellar-3.0.3 | In memory data shell support cellar | 3.0.3 | | karaf-cellar-3.0.3 | Karaf clustering cellar-dosgi | 3.0.3 | | karaf-cellar-3.0.3 | DOSGi support cellar-obr

between quantization and clustering, which one is preferable? What is the performance impact when both are used together? We have four options: none, quantization, clustering, and both. We would need to for choosing quantization and/or clustering techniques for model optimization. We have a search space which has two boolean valued parameters: quantization and clustering. A $$True$$ value means that the

Kalenichenko, D. and Philbin, J., 2015. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 815-823) Schroff, Dmitry Kalenichenko, James Philbin. FaceNet: A unified embedding for face recognition and clustering. 2015, computer vision and pattern recognition. Facebook DeepFace �������� �e��m�o�p��ruE��i��9������ Schroff, Dmitry Kalenichenko, James Philbin. FaceNet: A unified embedding for face recognition and clustering. 2015, computer vision and pattern recognition. �������� �������� �� API – �AI���� �� API –

See the transport and DOSGi section of the user guide. Finally, Cellar also provides "runtime clustering" by providing dedicated feature like: * HTTP load balancing * HTTP sessions replication * log add/remove any PID he wishes to the whitelist/blacklist. 3. The role of Hazelcast The idea behind the clustering engine is that for each unit that we want to replicate, we create an event, broadcast the event configuration is required. • No single point of failure ◦ No server or master is required for clustering ◦ The shared resource is distributed, hence we introduce no single point of failure. • Provides

scatter ▪ binscatter ▪ histogram ▪ histogram2 ▪ ksdensity 15 tall 支持的大数据机器学习算法 – K-means Clustering (kmeans) – Linear Regression (fitlm) – Logistic & Generalized Linear Regression (fitglm) – Discriminant

Caching of upstream repos (e.g., DockerHub) • Token-based auth • Image scanning improvements • Clustering – local and remote • Increase scalability • Improved RBAC • Improved multi-tenancy • harborctl