4 citations · 6 across the 4 of their papers we have counts for
5 papers · 1 filter
Towards Verifying Neural Networks Against Multi-Parameter Bit-Flip Perturbations
Hai Duong, Thanh Le, Ho Nguyen +1
Hardware faults can flip bits in the stored weights of a quantized neural network, potentially compromising its predictions. While such faults typically affect multiple parameters…
CLAD: Constrained Abstract Domain for Neural Network Verification
Hai Duong, Thanh Le, ThanhVu Nguyen
Neural network verification (NNV) formally verifies that a network satisfies a specified property for all inputs within a defined region. Modern NNV tools employ abstract domains t…
Verifying Neural Networks with Reinforcement Learning
Hai Duong, Thanh Le, ThanhVu Nguyen
Formal verification can play a key role in ensuring the reliability of Deep Neural Networks (DNNs) deployed in safety-critical systems. Modern DNN verifiers employ a branch-and-bou…
ZonoGPT: Towards An Abstract Domain for Verifying Large GPT Models
Hai Duong, Thanh Le, ThanhVu Nguyen
Transformer-based models are widely used for reasoning, coding, and multimodal agentic tasks. To provide formal assurance of desirable behaviors, such as robustness, safety, and fa…
Verifying DNN-based Semantic Communication Against Generative Adversarial Noise
Thanh Le, Hai Duong, ThanhVu Nguyen +1
Safety-critical applications like autonomous vehicles and industrial IoT are adopting semantic communication (SemCom) systems using deep neural networks to reduce bandwidth and inc…