7 papers
Evolving Excellence: Automated Optimization of LLM-based Agents
Paul Brookes, Vardan Voskanyan, Rafail Giavrimis +18
Agentic AI systems built on large language models (LLMs) offer significant potential for automating complex workflows, from software development to customer support. However, LLM a…
Self-Adaptive Graph Mixture of Models
Mohit Meena, Yash Punjabi, Abhishek A +2
Graph Neural Networks (GNNs) have emerged as powerful tools for learning over graph-structured data, yet recent studies have shown that their performance gains are beginning to pla…
Adaptive LLM Routing under Budget Constraints
Pranoy Panda, Raghav Magazine, Chaitanya Devaguptapu +2
Large Language Models (LLMs) have revolutionized natural language processing, but their varying capabilities and costs pose challenges in practical applications. LLM routing addres…
Learning Disentangled Representation in Object-Centric Models for Visual Dynamics Prediction via Transformers
Sanket Gandhi, Atul, Samanyu Mahajan +4
Recent work has shown that object-centric representations can greatly help improve the accuracy of learning dynamics while also bringing interpretability. In this work, we take thi…
Deep Learning for Bias Detection: From Inception to Deployment
Md Abul Bashar, Richi Nayak, Anjor Kothare +2
To create a more inclusive workplace, enterprises are actively investing in identifying and eliminating unconscious bias (e.g., gender, race, age, disability, elitism and religion)…
Towards an Interpretable Latent Space in Structured Models for Video Prediction
Rushil Gupta, Vishal Sharma, Yash Jain +3
We focus on the task of future frame prediction in video governed by underlying physical dynamics. We work with models which are object-centric, i.e., explicitly work with object r…
Lifted Marginal MAP Inference
Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal +2
Lifted inference reduces the complexity of inference in relational probabilistic models by identifying groups of constants (or atoms) which behave symmetric to each other. A number…