1 code implementation • 20 Sep 2024 • Fabiana Ferracina, Payton Beeler, Mahantesh Halappanavar, Bala Krishnamoorthy, Marco Minutoli, Laura Fierce
As a strategy for accelerating particle-resolved microphysics models, we introduce Graph-based Learning of Aerosol Dynamics (GLAD) and use this model to train a surrogate of the particle-resolved model PartMC-MOSAIC.
no code implementations • 21 Aug 2024 • Rounak Meyur, Hung Phan, Sridevi Wagle, Jan Strube, Mahantesh Halappanavar, Sameera Horawalavithana, Anurag Acharya, Sai Munikoti
Benchmarking is essential to evaluate and compare the performance of the different RAG configurations in terms of retriever and generator, providing insights into their effectiveness, scalability, and suitability for the specific domain and applications.
no code implementations • 21 Aug 2024 • Md Taufique Hussain, Mahantesh Halappanavar, Samrat Chatterjee, Filippo Radicchi, Santo Fortunato, Ariful Azad
We develop an algorithm that finds the consensus of many different clustering solutions of a graph.
no code implementations • 10 Jul 2024 • Hung Phan, Anurag Acharya, Rounak Meyur, Sarthak Chaturvedi, Shivam Sharma, Mike Parker, Dan Nally, Ali Jannesari, Karl Pazdernik, Mahantesh Halappanavar, Sai Munikoti, Sameera Horawalavithana
We test the LLMs' internal prior NEPA knowledge by providing questions without any context, as well as assess how LLMs synthesize the contextual information present in long NEPA documents to facilitate the question/answering task.
1 code implementation • 4 Apr 2024 • Fabiana Ferracina, Bala Krishnamoorthy, Mahantesh Halappanavar, Shengwei Hu, Vidyasagar Sathuvalli
We explore the application of machine learning algorithms to predict the suitability of Russet potato clones for advancement in breeding trials.
1 code implementation • 14 Nov 2023 • Wenceslao Shaw Cortez, Jan Drgona, Draguna Vrabie, Mahantesh Halappanavar
In this paper, we propose a novel predictive safety filter that is robust to bounded perturbations and is implemented in an even-triggered fashion to reduce online computation.
no code implementations • 19 Oct 2023 • Yu Wang, Yuxuan Yin, Karthik Somayaji Nanjangud Suryanarayana, Jan Drgona, Malachi Schram, Mahantesh Halappanavar, Frank Liu, Peng Li
Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge.
no code implementations • 24 Aug 2023 • Karthik Somayaji NS, Yu Wang, Malachi Schram, Jan Drgona, Mahantesh Halappanavar, Frank Liu, Peng Li
Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value function distribution.
no code implementations • 31 May 2023 • Laya Das, Sai Munikoti, Nrushad Joshi, Mahantesh Halappanavar
State-of-the-art graph self-supervision restricts training to only one graph, resulting in graph-specific models that are incompatible with different but related graphs.
no code implementations • 7 Apr 2023 • Maruti K. Mudunuru, James A. Ang, Mahantesh Halappanavar, Simon D. Hammond, Maya B. Gokhale, James C. Hoe, Tushar Krishna, Sarat S. Sreepathi, Matthew R. Norman, Ivy B. Peng, Philip W. Jones
This paper discusses the topic of the `AI Architectures and Co-design' session and associated outcomes.
no code implementations • 3 Feb 2023 • Ashutosh Dutta, Samrat Chatterjee, Arnab Bhattacharya, Mahantesh Halappanavar
Development of autonomous cyber system defense strategies and action recommendations in the real-world is challenging, and includes characterizing system state uncertainties and attack-defense dynamics.
1 code implementation • 5 Oct 2022 • Buddhika Nettasinghe, Samrat Chatterjee, Ramakrishna Tipireddy, Mahantesh Halappanavar
Conformal prediction is a widely used method to quantify the uncertainty of a classifier under the assumption of exchangeability (e. g., IID data).
1 code implementation • 3 Aug 2022 • Wenceslao Shaw Cortez, Jan Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie
We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions.
no code implementations • 16 Jun 2022 • Sai Munikoti, Deepesh Agarwal, Laya Das, Mahantesh Halappanavar, Balasubramaniam Natarajan
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming.
no code implementations • 30 May 2022 • Sai Munikoti, Balasubramaniam Natarajan, Mahantesh Halappanavar
However, there are serious limitations in current approaches such as: (1) IM formulations only consider influence via spread and ignore self activation; (2) scalability to large graphs; (3) generalizability across graph families; (4) low computational efficiency with a large running time to identify seed sets for every test network.
no code implementations • 22 May 2022 • Sayak Mukherjee, Ján Drgoňa, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie
We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees.
1 code implementation • 2 Mar 2022 • Ján Drgoňa, Sayak Mukherjee, Aaron Tuor, Mahantesh Halappanavar, Draguna Vrabie
The problem of synthesizing stochastic explicit model predictive control policies is known to be quickly intractable even for systems of modest complexity when using classical control-theoretic methods.
no code implementations • NeurIPS 2021 • Ján Drgoňa, Sayak Mukherjee, Jiaxin Zhang, Frank Liu, Mahantesh Halappanavar
Deep Markov models (DMM) are generative models that are scalable and expressive generalization of Markov models for representation, learning, and inference problems.
1 code implementation • 23 Feb 2021 • Siddhartha Shankar Das, Edoardo Serra, Mahantesh Halappanavar, Alex Pothen, Ehab Al-Shaer
Weaknesses in computer systems such as faults, bugs and errors in the architecture, design or implementation of software provide vulnerabilities that can be exploited by attackers to compromise the security of a system.
Ranked #1 on General Classification on CVE to CWE mapping
no code implementations • NeurIPS 2012 • Chad Scherrer, Ambuj Tewari, Mahantesh Halappanavar, David Haglin
We give a unified convergence analysis for the family of block-greedy algorithms.