Summary of Astra: Accurate and Scalable Anns-based Training Of Extreme Classifiers, by Sonu Mehta et al.
ASTRA: Accurate and Scalable ANNS-based Training of Extreme Classifiers
by Sonu Mehta, Jayashree Mohan, Nagarajan Natarajan, Ramachandran Ramjee, Manik Varma
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel deep learning paradigm for extreme classification, a task involving annotating data points with relevant labels from an enormous set of possible labels, has been developed. This approach embeds queries and labels using a deep encoder like DistilBERT, followed by linear classifiers operating on query embeddings. The architecture’s appeal lies in enabling rapid inference through approximate nearest neighbor search (ANNS). However, designing training algorithms that balance accuracy with scalability for handling massive label sets on limited computational resources remains a key challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to match a huge number of questions with relevant answers from a vast library of texts. This is what “extreme classification” is all about. Researchers have come up with a way to do this using special computer programs that can quickly find the best answer by comparing questions and answers. The goal is to make sure these programs work well even when there are millions of possible answers. |
Keywords
» Artificial intelligence » Classification » Deep learning » Encoder » Inference » Nearest neighbor
