50 citations · 121 across the 35 of their papers we have counts for
36 papers
Teaching LLMs to Generate Challenging MILP Instances via Solver Feedback
Jitin Singla, Parikshit Pareek, Pratik Jawanpuria +1
Generating optimization instances that are both feasible and computationally challenging is crucial for benchmarking solvers and training learning-based optimization algorithms. Ex…
Towards Spatio-Temporal World Scene Graph Generation from Monocular Videos
Rohith Peddi, Saurabh, Shravan Shanmugam +4
Spatio-temporal scene graphs provide a principled representation for modeling evolving object interactions, yet existing methods remain fundamentally frame-centric: they reason onl…
Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation
Rohith Peddi, Saurabh, Ayush Abhay Shrivastava +2
Spatio-Temporal Scene Graphs (STSGs) provide a concise and expressive representation of dynamic scenes by modeling objects and their evolving relationships over time. However, real…
Simple Augmentations of Logical Rules for Neuro-Symbolic Knowledge Graph Completion
Ananjan Nandi, Navdeep Kaur, Parag Singla +1
High-quality and high-coverage rule sets are imperative to the success of Neuro-Symbolic Knowledge Graph Completion (NS-KGC) models, because they form the basis of all symbolic inf…
SSP: Self-Supervised Prompting for Cross-Lingual Transfer to Low-Resource Languages using Large Language Models
Vipul Rathore, Aniruddha Deb, Ankish Chandresh +2
Recently, very large language models (LLMs) have shown exceptional performance on several English NLP tasks with just in-context learning (ICL), but their utility in other language…
Towards Scene Graph Anticipation
Rohith Peddi, Saksham Singh, Saurabh +2
Spatio-temporal scene graphs represent interactions in a video by decomposing scenes into individual objects and their pair-wise temporal relationships. Long-term anticipation of t…