10 citations · 15 across the 6 of their papers we have counts for
5 papers · 1 filter
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
Shubham Gupta, Sahil Manchanda, Sayan Ranu +1
Graph neural networks (GNNs), in general, are built on the assumption of a static set of features characterizing each node in a graph. This assumption is often violated in practice…
GSHOT: Few-shot Generative Modeling of Labeled Graphs
Sahil Manchanda, Shubham Gupta, Sayan Ranu +1
Deep graph generative modeling has gained enormous attraction in recent years due to its impressive ability to directly learn the underlying hidden graph distribution. Despite thei…
CoviHawkes: Temporal Point Process and Deep Learning based Covid-19 forecasting for India
Ambedkar Dukkipati, Tony Gracious, Shubham Gupta
Lockdowns are one of the most effective measures for containing the spread of a pandemic. Unfortunately, they involve a heavy financial and emotional toll on the population that of…
Pure Exploration with Structured Preference Feedback
Shubham Gupta, Aadirupa Saha, Sumeet Katariya
We consider the problem of pure exploration with subset-wise preference feedback, which contains arms with features. The learner is allowed to query subsets of size and rec…
Active Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation
Rishi Hazra, Parag Dutta, Shubham Gupta +2
While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. Ho…