LipKernel parameterizes dissipative convolution kernels via 2-D Roesser state-space models so that layer-wise LMIs enforce network Lipschitz bounds while allowing standard fast convolution evaluation after training.
Deep learning,
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Random quantum circuits used as adversarial training data reduce successful attack rates on QML models for CIFAR-10 from 89.8% to 68.45% and for CINIC-10 from 94.23% to 78.68%.
Two neural network architectures achieve state-of-the-art performance in quantum state tomography for pure and mixed states by incorporating class information.
A survey that organizes Transformer-based autonomous driving models by task and architecture while analyzing compression techniques as a system-level deployment concern.
citing papers explorer
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LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
LipKernel parameterizes dissipative convolution kernels via 2-D Roesser state-space models so that layer-wise LMIs enforce network Lipschitz bounds while allowing standard fast convolution evaluation after training.
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Quantum Patches: Enhancing Robustness of Quantum Machine Learning Models
Random quantum circuits used as adversarial training data reduce successful attack rates on QML models for CIFAR-10 from 89.8% to 68.45% and for CINIC-10 from 94.23% to 78.68%.
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Optical Quantum Mixed-State Reconstruction With Multiple Deep Learning Approaches
Two neural network architectures achieve state-of-the-art performance in quantum state tomography for pure and mixed states by incorporating class information.
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Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey
A survey that organizes Transformer-based autonomous driving models by task and architecture while analyzing compression techniques as a system-level deployment concern.