97 citations · 275 across the 9 of their papers we have counts for
9 papers
NGAME: Negative Mining-aware Mini-batching for Extreme Classification
Kunal Dahiya, Nilesh Gupta, Deepak Saini +16
Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels from an extremely large label set. Performing deep XC with dense, learnt representation…
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents
Kunal Dahiya, Deepak Saini, Anshul Mittal +6
Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point…
DECAF: Deep Extreme Classification with Label Features
Anshul Mittal, Kunal Dahiya, Sheshansh Agrawal +4
Extreme multi-label classification (XML) involves tagging a data point with its most relevant subset of labels from an extremely large label set, with several applications such as…
ECLARE: Extreme Classification with Label Graph Correlations
Anshul Mittal, Noveen Sachdeva, Sheshansh Agrawal +3
Deep extreme classification (XC) seeks to train deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. The core uti…
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri +2
Standard Convolutional Neural Networks (CNNs) designed for computer vision tasks tend to have large intermediate activation maps. These require large working memory and are thus un…
Extreme Regression for Dynamic Search Advertising
Yashoteja Prabhu, Aditya Kusupati, Nilesh Gupta +1
This paper introduces a new learning paradigm called eXtreme Regression (XR) whose objective is to accurately predict the numerical degrees of relevance of an extremely large numbe…