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 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 $ x_{min}

2021 ## Outline  Clustering  K-Means Method [Image](/uploads/documents/a/9/f/9/a9f935fc48c837d0fd22a39e993b2b8e/p2_5.jpg) Hierarchical Clustering ## Clustering • Usually an unsupervised learning problem • Given: N unlabeled examples $ \{x_{1},\cdots 48c837d0fd22a39e993b2b8e/p3_2.jpg) (b) Desired clustering • Loosely speaking, it is classification without ground truth labels • A good clustering is one that achieves: • High within-cluster similarity

centroids given the current clustering fn recompute_centroids( xs: &(Vec<$kind>], clustering: &(usize], recompute_centroids( xs: &(Vec<$kind>], clustering: &(usize], k: usize, ) -> Vec> centroid: Vec<$kind> = vec![0..0; ndims]; let mut n_cluster: $kind = 0..0; xs.iter().zip(clustering.iter().for_each(|(xi, &zi)| { if zi == cluster_ix { n_cluster += 1..0;

[Image](/uploads/documents/b/8/e/b/b8ebcfb1b3739d04e257a610c26124e3/p29_2.jpg) ### Unsupervised Learning: Clustering (Contd.)  ## Unsupervised required compared to supervised learning. - At the same time, improving the results of unsupervised clustering to the expectations of the user.  • Constrained Clustering • Distance Metric Learning • Manifold based Learning • Sparsity based Learning (Compressed Sensing) ## Constrained Clustering When we have any of the following:

Mapping external roles ..... 291 42.7. SASL ..... 291 42.8. Changing the username/password for clustering ..... 291 42.9. Securing the console ..... 292 42.10. Controlling JMS ObjectMessage deserialization Last-Value Property ..... 327 49.3. Forcing all consumers to be non-destructive ..... 328 49.4. Clustering ..... 328 49.5. Example ..... 328 50. Non-Destructive Queues ..... 329 50.1. Limiting the Subscription ..... 498 86.11. Clustered Grouping ..... 498 86.12. Clustered Queue ..... 498 86.13. Clustering with JGroups ..... 498 86.14. Clustered Standalone ..... 498 86.15. Clustered Static Discovery

Mapping external roles ..... 291 42.7. SASL ..... 291 42.8. Changing the username/password for clustering ..... 291 42.9. Securing the console ..... 292 42.10. Controlling JMS ObjectMessage deserialization Last-Value Property ..... 327 49.3. Forcing all consumers to be non-destructive ..... 328 49.4. Clustering ..... 328 49.5. Example ..... 328 50. Non-Destructive Queues ..... 329 50.1. Limiting the Durable Subscription 499 86.11. Clustered Grouping 499 86.12. Clustered Queue 499 86.13. Clustering with JGroups 499 86.14. Clustered Standalone 499 86.15. Clustered Static Discovery 499 86

Mapping external roles ..... 291 42.7. SASL ..... 291 42.8. Changing the username/password for clustering ..... 291 42.9. Securing the console ..... 292 42.10. Controlling JMS ObjectMessage deserialization Last-Value Property ..... 327 49.3. Forcing all consumers to be non-destructive ..... 328 49.4. Clustering ..... 328 49.5. Example ..... 328 50. Non-Destructive Queues ..... 329 50.1. Limiting the Subscription ..... 499 86.11. Clustered Grouping ..... 499 86.12. Clustered Queue ..... 499 86.13. Clustering with JGroups ..... 499 86.14. Clustered Standalone ..... 499 86.15. Clustered Static Discovery

Mapping external roles ..... 301 43.7. SASL ..... 301 43.8. Changing the username/password for clustering ..... 301 43.9. Securing the console ..... 301 43.10. Controlling JMS ObjectMessage deserialization Last-Value Property ..... 336 50.3. Forcing all consumers to be non-destructive ..... 337 50.4. Clustering ..... 337 50.5. Example ..... 337 51. Non-Destructive Queues ..... 338 51.1. Limiting the Subscription ..... 505 87.10. Clustered Grouping ..... 505 87.11. Clustered Queue ..... 505 87.12. Clustering with JGroups ..... 505 87.13. Clustered Standalone ..... 506 87.14. Clustered Static Discovery

Mapping external roles ..... 305 43.7. SASL ..... 306 43.8. Changing the username/password for clustering ..... 306 43.9. Securing the console ..... 306 43.10. Controlling JMS ObjectMessage deserialization Last-Value Property ..... 341 50.3. Forcing all consumers to be non-destructive ..... 342 50.4. Clustering ..... 342 50.5. Example ..... 342 51. Non-Destructive Queues ..... 343 51.1. Limiting the Subscription ..... 511 87.10. Clustered Grouping ..... 511 87.11. Clustered Queue ..... 511 87.12. Clustering with JGroups ..... 511 87.13. Clustered Standalone ..... 512 87.14. Clustered Static Discovery

Mapping external roles ..... 303 43.7. SASL ..... 304 43.8. Changing the username/password for clustering ..... 304 43.9. Securing the console ..... 304 43.10. Controlling JMS ObjectMessage deserialization Last-Value Property ..... 339 50.3. Forcing all consumers to be non-destructive ..... 340 50.4. Clustering ..... 340 50.5. Example ..... 340 51. Non-Destructive Queues ..... 341 51.1. Limiting the Subscription ..... 509 87.10. Clustered Grouping ..... 509 87.11. Clustered Queue ..... 509 87.12. Clustering with JGroups ..... 509 87.13. Clustered Standalone ..... 510 87.14. Clustered Static Discovery