Summary of Graph Regularized Encoder Training For Extreme Classification, by Anshul Mittal et al.
Graph Regularized Encoder Training for Extreme Classification
by Anshul Mittal, Shikhar Mohan, Deepak Saini, Siddarth Asokan, Suchith C. Prabhu, Lakshya Kumar, Pankaj Malhotra, Jain jiao, Amit Singh, Sumeet Agarwal, Soumen Chakrabarti, Purushottam Kar, Manik Varma
First submitted to arxiv on: 28 Feb 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 This paper proposes an alternative approach to deep extreme classification (XC) that leverages graph metadata without the computational expense of graph convolutional networks (GCNs). The authors formally demonstrate that GCNs are not necessary in certain XC applications, and instead, recommend regularizing encoder training with graph data. This new paradigm, called RAMEN, achieves performance boosts up to 15% higher on benchmark datasets compared to state-of-the-art methods, including those using GCNs. Additionally, RAMEN outperforms the best baseline by 10% on a proprietary recommendation dataset sourced from click logs of a popular search engine. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make computers better at organizing and labeling things. They found a way to do this without needing special “graph” computers that take a lot of time to process. Instead, they used the data itself to help the computer learn. This new way, called RAMEN, works really well and can even work with huge amounts of labels (over 1 million!). It’s like a superpower for computers to understand what things are related to each other. |
Keywords
* Artificial intelligence * Classification * Encoder
