2 citations · 2 across the 6 of their papers we have counts for
6 papers
Factual and Edit-Sensitive Graph-to-Sequence Generation via Graph-Aware Adaptive Noising
Aditya Hemant Shahane, Anuj Kumar Sirohi, Tanmoy Chakraborty +2
Fine-tuned autoregressive models for graph-to-sequence generation (G2S) often struggle with factual grounding and edit sensitivity. To tackle these issues, we propose a non-autoreg…
BiMol-Diff: A Unified Diffusion Framework for Molecular Generation and Captioning
Aditya Hemant Shahane, Anuj Kumar Sirohi, Devansh Arora +3
Bridging molecular structures and natural language is essential for controllable design. Autoregressive models struggle with long-range dependencies, while standard diffusion proce…
Enhancing Robustness of Graph Neural Networks through p-Laplacian
Anuj Kumar Sirohi, Subhanu Halder, Kabir Kumar +1
With the increase of data in day-to-day life, businesses and different stakeholders need to analyze the data for better predictions. Traditionally, relational data has been a sourc…
Enhancing Robustness of Graph Neural Networks through p-Laplacian
Anuj Kumar Sirohi, Subhanu Halder, Kabir Kumar +1
With the increase of data in day-to-day life, businesses and different stakeholders need to analyze the data for better predictions. Traditionally, relational data has been a sourc…
GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks
Anuj Kumar Sirohi, Anjali Gupta, Sandeep Kumar +2
Graph Neural Networks (GNNs) have demonstrated impressive performance across various tasks, leading to their increased adoption in high-stakes decision-making systems. However, con…
No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation
Nimesh Agrawal, Anuj Kumar Sirohi, Jayadeva +1
Ensuring fairness in Recommendation Systems (RSs) across demographic groups is critical due to the increased integration of RSs in applications such as personalized healthcare, fin…