4 citations · 13 across the 6 of their papers we have counts for
6 papers
Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows
Vinayak Gupta, Srikanta Bedathur
Human beings always engage in a vast range of activities and tasks that demonstrate their ability to adapt to different scenarios. Any human activity can be represented as a tempor…
Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashing
Vinayak Gupta, Srikanta Bedathur, Abir De
Temporal sequences have become pervasive in various real-world applications. Consequently, the volume of data generated in the form of continuous time-event sequence(s) or CTES(s)…
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…
Embeddings for Tabular Data: A Survey
Rajat Singh, Srikanta Bedathur
Tabular data comprising rows (samples) with the same set of columns (attributes, is one of the most widely used data-type among various industries, including financial services, he…
Modeling Spatial Trajectories using Coarse-Grained Smartphone Logs
Vinayak Gupta, Srikanta Bedathur
Current approaches for points-of-interest (POI) recommendation learn the preferences of a user via the standard spatial features such as the POI coordinates, the social network, et…
A Survey on Temporal Graph Representation Learning and Generative Modeling
Shubham Gupta, Srikanta Bedathur
Temporal graphs represent the dynamic relationships among entities and occur in many real life application like social networks, e commerce, communication, road networks, biologica…