16 citations · 20 across the 5 of their papers we have counts for
5 papers
Retrieval-augmented Encoders for Extreme Multi-label Text Classification
Yau-Shian Wang, Wei-Cheng Chang, Jyun-Yu Jiang +3
Extreme multi-label classification (XMC) seeks to find relevant labels from an extremely large label collection for a given text input. To tackle such a vast label space, current s…
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
Eli Chien, Jiong Zhang, Cho-Jui Hsieh +4
The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extractio…
Extreme Zero-Shot Learning for Extreme Text Classification
Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh +2
The eXtreme Multi-label text Classification (XMC) problem concerns finding most relevant labels for an input text instance from a large label set. However, the XMC setup faces two…
A Greedy Approach for Budgeted Maximum Inner Product Search
Hsiang-Fu Yu, Cho-Jui Hsieh, Qi Lei +1
Maximum Inner Product Search (MIPS) is an important task in many machine learning applications such as the prediction phase of a low-rank matrix factorization model for a recommend…
A Scalable Asynchronous Distributed Algorithm for Topic Modeling
Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokun Yun +2
Learning meaningful topic models with massive document collections which contain millions of documents and billions of tokens is challenging because of two reasons: First, one need…