Search Results for author:
Found 6 papers, 4 papers with code
ParChain: A Framework for Parallel Hierarchical Agglomerative Clustering using Nearest-Neighbor Chain
This paper studies the hierarchical clustering problem, where the goal is to produce a dendrogram that represents clusters at varying scales of a data set.
Fast Parallel Algorithms for Euclidean Minimum Spanning Tree and Hierarchical Spatial Clustering
Our approach is based on generating a well-separated pair decomposition followed by using Kruskal's minimum spanning tree algorithm and bichromatic closest pair computations.
Theoretically-Efficient and Practical Parallel DBSCAN
The DBSCAN method for spatial clustering has received significant attention due to its applicability in a variety of data analysis tasks.
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products
Our largest model has 6. 4 billion parameters and trains in less than 35 hours on a single p3. 16x machine.
Extreme Classification in Log Memory
With MACH we can train ODP dataset with 100, 000 classes and 400, 000 features on a single Titan X GPU, with the classification accuracy of 19. 28%, which is the best-reported accuracy on this dataset.
MACH: Embarrassingly parallel $K$-class classification in $O(d\log{K})$ memory and $O(K\log{K} + d\log{K})$ time, instead of $O(Kd)$
MACH is the first generic $K$-classification algorithm, with provably theoretical guarantees, which requires $O(\log{K})$ memory without any assumption on the relationship between classes.
