Search Results for author:
Found 8 papers, 3 papers with code
Multi-modal Extreme Classification
This paper develops the MUFIN technique for extreme classification (XC) tasks with millions of labels where datapoints and labels are endowed with visual and textual descriptors.
Are You Comfortable Now: Deep Learning the Temporal Variation in Thermal Comfort in Winters
Temporal variability in prediction accuracy is demonstrated (up to 80%).
NGAME: Negative Mining-aware Mini-batching for Extreme Classification
This paper identifies that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down.
Building Matters: Spatial Variability in Machine Learning Based Thermal Comfort Prediction in Winters
Generally, ML-based solutions are proposed for air-conditioned or HVAC ventilated buildings and the models are primarily designed for adults.
Optimizing Unlicensed Coexistence Network Performance Through Data Learning
This work studies NFRs in unlicensed LTE-WiFi (LTE-U and LTE-LAA) networks through supervised learning of network data collected from real-world experiments.
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents
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 with the most relevant subset of labels from an extremely large label set.
DECAF: Deep Extreme Classification with Label Features
This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer accurate classification at the scale of millions of labels.
DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases
The objective in deep extreme multi-label learning is to jointly learn feature representations and classifiers to automatically tag data points with the most relevant subset of labels from an extremely large label set.
