9 citations · 16 across the 9 of their papers we have counts for
9 papers
Extinction in Reaction Network Models
Pranav Agarwal, Gheorghe Craciun, Abhishek Deshpande +1
In this paper, we study extinction in dynamical systems generated by reaction networks. We introduce two notions: weak extinction and strong extinction, and relate them to the stru…
Supernova Event Dataset: Interpreting Large Language Models' Personality through Critical Event Analysis
Pranav Agarwal, Ioana Ciucă
Large Language Models (LLMs) are increasingly integrated into everyday applications. As their influence grows, understanding their decision making and underlying personality become…
Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment
Aamer Abdul Rahman, Pranav Agarwal, Rita Noumeir +3
Offline reinforcement learning has shown promise for solving tasks in safety-critical settings, such as clinical decision support. Its application, however, has been limited by the…
Learning to Play Atari in a World of Tokens
Pranav Agarwal, Sheldon Andrews, Samira Ebrahimi Kahou
Model-based reinforcement learning agents utilizing transformers have shown improved sample efficiency due to their ability to model extended context, resulting in more accurate wo…
TPTO: A Transformer-PPO based Task Offloading Solution for Edge Computing Environments
Niloofar Gholipour, Marcos Dias de Assuncao, Pranav Agarwal +2
Emerging applications in healthcare, autonomous vehicles, and wearable assistance require interactive and low-latency data analysis services. Unfortunately, cloud-centric architect…
Transformers in Reinforcement Learning: A Survey
Pranav Agarwal, Aamer Abdul Rahman, Pierre-Luc St-Charles +2
Transformers have significantly impacted domains like natural language processing, computer vision, and robotics, where they improve performance compared to other neural networks.…
Automatic Evaluation of Excavator Operators using Learned Reward Functions
Pranav Agarwal, Marek Teichmann, Sheldon Andrews +1
Training novice users to operate an excavator for learning different skills requires the presence of expert teachers. Considering the complexity of the problem, it is comparatively…
Goal-constrained Sparse Reinforcement Learning for End-to-End Driving
Pranav Agarwal, Pierre de Beaucorps, Raoul de Charette
Deep reinforcement Learning for end-to-end driving is limited by the need of complex reward engineering. Sparse rewards can circumvent this challenge but suffers from long training…
Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning models
Pranav Agarwal, Alejandro Betancourt, Vana Panagiotou +1
Image captioning models have been able to generate grammatically correct and human understandable sentences. However most of the captions convey limited information as the model us…