12 papers
Intent-Level Quantum Programming with Assertion-Guided Execution and Inspectable Intermediate Representation
Ilesh Vora, Srikanth Thudumu, John Carlson +2
Quantum programs are difficult to validate: circuits are typically expressed as imperative gate sequences with limited inspectability, execution modalities must be selected manuall…
Pretraining Objective Matters in Extreme Low-Data FGVC: A Backbone-Controlled Study
Alexander Hackett, Srikanth Thudumu, Ginny Fisher +1
Extreme low-data fine-grained classification is common in expert domains where labeling is expensive, yet practitioners still need principled guidance for selecting pretrained enco…
Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teams
Hung Du, Hy Nguyen, Srikanth Thudumu +2
Connected and autonomous vehicles across land, water, and air must often operate in dynamic, unpredictable environments with limited communication, no centralized control, and part…
OpenAg: Democratizing Agricultural Intelligence
Srikanth Thudumu, Jason Fisher
Agriculture is undergoing a major transformation driven by artificial intelligence (AI), machine learning, and knowledge representation technologies. However, current agricultural…
Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
Srikanth Thudumu, Jason Fisher, Hung Du
Supervised Quantum Machine Learning (QML) represents an intersection of quantum computing and classical machine learning, aiming to use quantum resources to support model training…
Local Control Networks (LCNs): Optimizing Flexibility in Neural Network Data Pattern Capture
Hy Nguyen, Duy Khoa Pham, Srikanth Thudumu +3
The widespread use of Multi-layer perceptrons (MLPs) often relies on a fixed activation function (e.g., ReLU, Sigmoid, Tanh) for all nodes within the hidden layers. While effective…