Patch Similarity Aware Data-Free Quantization for Vision Transformers

Distributed query-aware quantization for high-dimensional similarity searches

The concept of similarity is used as the basis for many data exploration and data mining tasks. Nearest Neighbor (NN) queries identify the most similar items, or in terms of distance the closest points to a query point. Similarity is traditionally characterized using a distance function between multi-dimensional feature vectors. However, when the data is high-dimensional, traditional distance f...

Class-Aware Similarity Hashing for Data Classification

This paper introduces “class-aware similarity hashes” or “classprints,” which are an outgrowth of recent work on similarity hashing. The approach builds on the notion of context-based hashing to create a framework for identifying data types based on content and for building characteristic similarity hashes for individual data items that can be used for correlation. The principal benefits are th...

Interval Similarity-Based Quantization Method for Continuous Data

Data quantization methods for continuous attributes play an extremely important role in artificial intelligence, data mining and machine learning because discrete values of attributes are required in most classification methods. In this paper, we present an interval similarity-based quantization method for continuous data. It defines an interval similarity criterion which is regarded as a new m...

Bohr: Similarity Aware Geo-distributed Data Analytics

We propose Bohr, a similarity aware geo-distributed data analytics system that minimizes query completion time. The key idea is to exploit similarity between data in different data centers (DCs), and transfer similar data from the bottleneck DC to other sites with more WAN bandwidth. Though these sites have more input data to process, these data are more similar and can be more efficiently aggr...

Statistical quantization for similarity search