Generalized Zero-Shot Extreme Multi-label Learning-Reference-Cited by-同舟云学术

Generalized Zero-Shot Extreme Multi-label Learning

Published:2021-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
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Author:

Gupta Nilesh¹,Bohra Sakina²,Prabhu Yashoteja¹,Purohit Saurabh¹,Varma Manik¹

Affiliation:

1. Microsoft Research, Bengaluru, India

2. Microsoft, Bengaluru, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3447548.3467426

Reference69 articles.

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2. R. Babbar and B. Schölkopf. 2017. DiSMEC: Distributed Sparse Machines for Extreme Multi-label Classification. In WSDM. R. Babbar and B. Schölkopf. 2017. DiSMEC: Distributed Sparse Machines for Extreme Multi-label Classification. In WSDM.

3. R. Babbar and B. Schölkopf. 2019. Data scarcity robustness and extreme multi-label classification. ML (2019). R. Babbar and B. Schölkopf. 2019. Data scarcity robustness and extreme multi-label classification. ML (2019).

4. K. Bhatia H. Jain P. Kar M. Varma and P. Jain. 2015. Sparse Local Embeddings for Extreme Multi-label Classification. In NIPS. K. Bhatia H. Jain P. Kar M. Varma and P. Jain. 2015. Sparse Local Embeddings for Extreme Multi-label Classification. In NIPS.

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