9 citations · 19 across the 15 of their papers we have counts for
15 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…
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…
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…
The M-factor: A Novel Metric for Evaluating Neural Architecture Search in Resource-Constrained Environments
Srikanth Thudumu, Hy Nguyen, Hung Du +6
Neural Architecture Search (NAS) aims to automate the design of deep neural networks. However, existing NAS techniques often focus on maximising accuracy, neglecting model efficien…
Overcoming Semantic Dilution in Transformer-Based Next Frame Prediction
Hy Nguyen, Srikanth Thudumu, Hung Du +2
Next-frame prediction in videos is crucial for applications such as autonomous driving, object tracking, and motion prediction. The primary challenge in next-frame prediction lies…
Contextual Knowledge Sharing in Multi-Agent Reinforcement Learning with Decentralized Communication and Coordination
Hung Du, Srikanth Thudumu, Hy Nguyen +2
Decentralized Multi-Agent Reinforcement Learning (Dec-MARL) has emerged as a pivotal approach for addressing complex tasks in dynamic environments. Existing Multi-Agent Reinforceme…
CSAOT: Cooperative Multi-Agent System for Active Object Tracking
Hy Nguyen, Bao Pham, Hung Du +3
Object Tracking is essential for many computer vision applications, such as autonomous navigation, surveillance, and robotics. Unlike Passive Object Tracking (POT), which relies on…
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…
Dual-Branch HNSW Approach with Skip Bridges and LID-Driven Optimization
Hy Nguyen, Nguyen Hung Nguyen, Nguyen Linh Bao Nguyen +4
The Hierarchical Navigable Small World (HNSW) algorithm is widely used for approximate nearest neighbor (ANN) search, leveraging the principles of navigable small-world graphs. How…
Large language models for generating rules, yay or nay?
Shangeetha Sivasothy, Scott Barnett, Rena Logothetis +4
Engineering safety-critical systems such as medical devices and digital health intervention systems is complex, where long-term engagement with subject-matter experts (SMEs) is nee…
A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directions
Hung Du, Srikanth Thudumu, Rajesh Vasa +1
Research interest in autonomous agents is on the rise as an emerging topic. The notable achievements of Large Language Models (LLMs) have demonstrated the considerable potential to…
LLMs for Test Input Generation for Semantic Caches
Zafaryab Rasool, Scott Barnett, David Willie +4
Large language models (LLMs) enable state-of-the-art semantic capabilities to be added to software systems such as semantic search of unstructured documents and text generation. Ho…
Seven Failure Points When Engineering a Retrieval Augmented Generation System
Scott Barnett, Stefanus Kurniawan, Srikanth Thudumu +2
Software engineers are increasingly adding semantic search capabilities to applications using a strategy known as Retrieval Augmented Generation (RAG). A RAG system involves findin…