most citedEfficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks

1 citations · 1 across the 2 of their papers we have counts for

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5 papers

cs.LG2025

Contextual Tokenization for Graph Inverted Indices

Pritish Chakraborty, Indradyumna Roy, Soumen Chakrabarti +1

Retrieving graphs from a large corpus, that contain a subgraph isomorphic to a given query graph, is a core operation in many real-world applications. While recent multi-vector gra…

cs.LG2025

Charting the Design Space of Neural Graph Representations for Subgraph Matching

Vaibhav Raj, Indradyumna Roy, Ashwin Ramachandran +2

Subgraph matching is vital in knowledge graph (KG) question answering, molecule design, scene graph, code and circuit search, etc. Neural methods have shown promising results for s…

cs.LG2025

Iteratively Refined Early Interaction Alignment for Subgraph Matching based Graph Retrieval

Ashwin Ramachandran, Vaibhav Raj, Indrayumna Roy +2

Graph retrieval based on subgraph isomorphism has several real-world applications such as scene graph retrieval, molecular fingerprint detection and circuit design. Roy et al. [35]…

cs.LG2025

Differentiable Adversarial Attacks for Marked Temporal Point Processes

Pritish Chakraborty, Vinayak Gupta, Rahul R +2

Marked temporal point processes (MTPPs) have been shown to be extremely effective in modeling continuous time event sequences (CTESs). In this work, we present adversarial attacks…

cs.LG20241 cited

Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks

Eeshaan Jain, Tushar Nandy, Gaurav Aggarwal +3

Existing subset selection methods for efficient learning predominantly employ discrete combinatorial and model-specific approaches which lack generalizability. For an unseen archit…