107 citations · 146 across the 6 of their papers we have counts for
6 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…
Disentangling deep neural networks with rectified linear units using duality
Chandrashekar Lakshminarayanan, Amit Vikram Singh
Despite their success deep neural networks (DNNs) are still largely considered as black boxes. The main issue is that the linear and non-linear operations are entangled in every la…
Generalize then Adapt: Source-Free Domain Adaptive Semantic Segmentation
Jogendra Nath Kundu, Akshay Kulkarni, Amit Singh +2
Unsupervised domain adaptation (DA) has gained substantial interest in semantic segmentation. However, almost all prior arts assume concurrent access to both labeled source and unl…
GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph
Junhan Yang, Zheng Liu, Shitao Xiao +6
The representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information. Rec…
Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning
Chandrashekar Lakshminarayanan, Amit Vikram Singh
Rectified linear unit (ReLU) activations can also be thought of as 'gates', which, either pass or stop their pre-activation input when they are 'on' (when the pre-activation input…
Deep Gated Networks: A framework to understand training and generalisation in deep learning
Chandrashekar Lakshminarayanan, Amit Vikram Singh
Understanding the role of (stochastic) gradient descent (SGD) in the training and generalisation of deep neural networks (DNNs) with ReLU activation has been the object study in th…