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Preprint Oct 2026

Towards Verifying Neural Networks Against Multi-Parameter Bit-Flip Perturbations

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 simultaneously, existing verifiers are limited to single-parameter perturbations due to the combinatorial explosion of possible flip locatio...

Hai Duong, Thanh Le, H. Nguyen et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Neural Network Verification for Deep Joint Source-Channel Coding

Deep joint source-channel coding (DeepJSCC) transmits data end-to-end over wireless channels using a neural encoder-decoder, but reconstruction quality can degrade sharply under adversarial perturbations and channel disturbances; no method formally bounds this degradation for DeepJSCC. We present the first bound-propag...

Thanh Le, Hai Duong, Takeshi Matsumura et al. · 0 citations
#machine learning Preprint Sep 2026

CLAD: Constrained Abstract Domain for Neural Network Verification

Constrained Lagrangian Abstract Domain (CLAD), a new abstract domain that computes a sound over-approximation of neural networks over input regions defined by a combination of convex constraints, is introduced.

Hai Duong, Thanh Le, Thanhvu Nguyen · 1 citation
#machine learning Preprint Sep 2026

Verifying Neural Networks with Reinforcement Learning

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-bound framework, which alternates between branching (splitting into smaller subproblems) and bounding (pruning subproblems) to efficiently expl...

Hai Duong, Thanh Le, Thanhvu Nguyen · 5 citations
#machine learning Preprint Sep 2026

ZonoGPT: Towards An Abstract Domain for Verifying Large GPT Models

ZonoGpt, an abstract domain for verifying large transformers that maintains a space complexity independent of network depth, is introduced, an abstract domain for verifying large transformers that maintains a space complexity independent of network depth.

Hai Duong, Thanh Le, Thanhvu Nguyen · 1 citation
Jul 2026

Formal Verification for Deep Learning-based Power Control in Massive MIMO

This work proposes a formal verification framework to evaluate the robustness of deep learning-based power allocation in multi-cell massive multiple-input multiple-output (MIMO) systems against a wide range of potential adversarial input manipulations and makes the first attempt to formally verify deep neural networks...

Thanh Le, Takeshi Matsumura, Yu-Sheng Ji et al. · 1 citation

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