11 citations · 40 across the 9 of their papers we have counts for
10 papers
Generative Active Learning for the Search of Small-molecule Protein Binders
Maksym Korablyov, Cheng-Hao Liu, Moksh Jain +31
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a si…
Semi-Supervised Deep Learning for Multiplex Networks
Anasua Mitra, Priyesh Vijayan, Ranbir Sanasam +3
Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations, each relation representing a distinct layer.…
Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks
Dylan Sandfelder, Priyesh Vijayan, William L. Hamilton
Graph neural networks (GNNs) have achieved remarkable success as a framework for deep learning on graph-structured data. However, GNNs are fundamentally limited by their tree-struc…
On Incorporating Structural Information to improve Dialogue Response Generation
Nikita Moghe, Priyesh Vijayan, Balaraman Ravindran +1
We consider the task of generating dialogue responses from background knowledge comprising of domain specific resources. Specifically, given a conversation around a movie, the task…
Understanding Dynamic Scenes using Graph Convolution Networks
Sravan Mylavarapu, Mahtab Sandhu, Priyesh Vijayan +3
We present a novel Multi-Relational Graph Convolutional Network (MRGCN) based framework to model on-road vehicle behaviors from a sequence of temporally ordered frames as grabbed b…
Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks
Sravan Mylavarapu, Mahtab Sandhu, Priyesh Vijayan +3
Understanding on-road vehicle behaviour from a temporal sequence of sensor data is gaining in popularity. In this paper, we propose a pipeline for understanding vehicle behaviour f…
Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling
Harshavardhan Kamarthi, Priyesh Vijayan, Bryan Wilder +2
A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network. Current state-of-the-art m…
Network Representation Learning: Consolidation and Renewed Bearing
Saket Gurukar, Priyesh Vijayan, Aakash Srinivasan +9
Graphs are a natural abstraction for many problems where nodes represent entities and edges represent a relationship across entities. An important area of research that has emerged…
Fusion Graph Convolutional Networks
Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra +2
Semi-supervised node classification in attributed graphs, i.e., graphs with node features, involves learning to classify unlabeled nodes given a partially labeled graph. Label pred…
HOPF: Higher Order Propagation Framework for Deep Collective Classification
Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra +2
Given a graph where every node has certain attributes associated with it and some nodes have labels associated with them, Collective Classification (CC) is the task of assigning la…