357 citations · 1.3k across the 33 of their papers we have counts for
5 papers · 1 filter
AGGLIO: Global Optimization for Locally Convex Functions
Debojyoti Dey, Bhaskar Mukhoty, Purushottam Kar
This paper presents AGGLIO (Accelerated Graduated Generalized LInear-model Optimization), a stage-wise, graduated optimization technique that offers global convergence guarantees f…
IGLU: Efficient GCN Training via Lazy Updates
S Deepak Narayanan, Aditya Sinha, Prateek Jain +2
Training multi-layer Graph Convolution Networks (GCN) using standard SGD techniques scales poorly as each descent step ends up updating node embeddings for a large portion of the g…
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…
AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks
S. K. Roy, M. E. Paoletti, J. M. Haut +4
Convolutional neural networks (CNNs) are trained using stochastic gradient descent (SGD)-based optimizers. Recently, the adaptive moment estimation (Adam) optimizer has become very…