Neural Networks with (Low-Precision) Polynomial Approximations: New Insights and Techniques for Accuracy Improvement
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Chi, Fan, Jingjing, Au, Man Ho, Yiu, Siu Ming |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SFPDML: Securer and Faster Privacy-Preserving Distributed Machine Learning based on MKTFHE
by: Wang, Hongxiao, et al.
Published: (2022)
by: Wang, Hongxiao, et al.
Published: (2022)
Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning
by: Shan, Junjie, et al.
Published: (2024)
by: Shan, Junjie, et al.
Published: (2024)
Deep Efficient Private Neighbor Generation for Subgraph Federated Learning
by: Zhang, Ke, et al.
Published: (2024)
by: Zhang, Ke, et al.
Published: (2024)
Certified Robust Accuracy of Neural Networks Are Bounded due to Bayes Errors
by: Zhang, Ruihan, et al.
Published: (2024)
by: Zhang, Ruihan, et al.
Published: (2024)
Accuracy Improvement in Differentially Private Logistic Regression: A Pre-training Approach
by: Hoseinpour, Mohammad, et al.
Published: (2023)
by: Hoseinpour, Mohammad, et al.
Published: (2023)
On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks
by: Ali, Ramy E., et al.
Published: (2020)
by: Ali, Ramy E., et al.
Published: (2020)
On the Adversarial Robustness of Graph Neural Networks with Graph Reduction
by: Wu, Kerui, et al.
Published: (2024)
by: Wu, Kerui, et al.
Published: (2024)
Patch Synthesis for Property Repair of Deep Neural Networks
by: Chi, Zhiming, et al.
Published: (2024)
by: Chi, Zhiming, et al.
Published: (2024)
Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations
by: Hong, Sanghyun, et al.
Published: (2025)
by: Hong, Sanghyun, et al.
Published: (2025)
An Attack-Driven Incident Response and Defense System (ADIRDS)
by: Lai, Anthony Cheuk Tung, et al.
Published: (2025)
by: Lai, Anthony Cheuk Tung, et al.
Published: (2025)
PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networks
by: Wang, Fuyi, et al.
Published: (2025)
by: Wang, Fuyi, et al.
Published: (2025)
Efficient Encrypted Computation in Convolutional Spiking Neural Networks with TFHE
by: Guo, Longfei, et al.
Published: (2026)
by: Guo, Longfei, et al.
Published: (2026)
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
by: Liu, Xin, et al.
Published: (2024)
by: Liu, Xin, et al.
Published: (2024)
A Note on Non-Composability of Layerwise Approximate Verification for Neural Inference
by: Zamir, Or
Published: (2026)
by: Zamir, Or
Published: (2026)
NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping
by: Ju, Jae Hyung, et al.
Published: (2023)
by: Ju, Jae Hyung, et al.
Published: (2023)
Certified Defense on the Fairness of Graph Neural Networks
by: Dong, Yushun, et al.
Published: (2023)
by: Dong, Yushun, et al.
Published: (2023)
Balancing Security and Accuracy: A Novel Federated Learning Approach for Cyberattack Detection in Blockchain Networks
by: Khoa, Tran Viet, et al.
Published: (2024)
by: Khoa, Tran Viet, et al.
Published: (2024)
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network
by: Xu, Renjie, et al.
Published: (2024)
by: Xu, Renjie, et al.
Published: (2024)
Node-level Contrastive Unlearning on Graph Neural Networks
by: Lee, Hong kyu, et al.
Published: (2025)
by: Lee, Hong kyu, et al.
Published: (2025)
SDNGuardStack: An Explainable Ensemble Learning Framework for High-Accuracy Intrusion Detection in Software-Defined Networks
by: Ashikuzzaman, et al.
Published: (2026)
by: Ashikuzzaman, et al.
Published: (2026)
Stealing Training Graphs from Graph Neural Networks
by: Lin, Minhua, et al.
Published: (2024)
by: Lin, Minhua, et al.
Published: (2024)
Residual Random Neural Networks
by: Andrecut, M.
Published: (2024)
by: Andrecut, M.
Published: (2024)
Certified Unlearning for Neural Networks
by: Koloskova, Anastasia, et al.
Published: (2025)
by: Koloskova, Anastasia, et al.
Published: (2025)
Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2026)
by: Zhang, Jiahao, et al.
Published: (2026)
DMGNN: Detecting and Mitigating Backdoor Attacks in Graph Neural Networks
by: Sui, Hao, et al.
Published: (2024)
by: Sui, Hao, et al.
Published: (2024)
Convex Approximation of Two-Layer ReLU Networks for Hidden State Differential Privacy
by: Romijnders, Rob, et al.
Published: (2024)
by: Romijnders, Rob, et al.
Published: (2024)
Link Stealing Attacks Against Inductive Graph Neural Networks
by: Wu, Yixin, et al.
Published: (2024)
by: Wu, Yixin, et al.
Published: (2024)
Graph Neural Network Explanations are Fragile
by: Li, Jiate, et al.
Published: (2024)
by: Li, Jiate, et al.
Published: (2024)
On Stealing Graph Neural Network Models
by: Podhajski, Marcin, et al.
Published: (2025)
by: Podhajski, Marcin, et al.
Published: (2025)
DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy
by: Zhang, Qiuchen, et al.
Published: (2022)
by: Zhang, Qiuchen, et al.
Published: (2022)
Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks
by: Lin, Minhua, et al.
Published: (2024)
by: Lin, Minhua, et al.
Published: (2024)
Is the Hard-Label Cryptanalytic Model Extraction Really Polynomial?
by: Ito, Akira, et al.
Published: (2025)
by: Ito, Akira, et al.
Published: (2025)
BeniFul: Backdoor Defense via Middle Feature Analysis for Deep Neural Networks
by: Li, Xinfu, et al.
Published: (2024)
by: Li, Xinfu, et al.
Published: (2024)
Indiscriminate Data Poisoning Attacks on Neural Networks
by: Lu, Yiwei, et al.
Published: (2022)
by: Lu, Yiwei, et al.
Published: (2022)
Multiplicative Reweighting for Robust Neural Network Optimization
by: Bar, Noga, et al.
Published: (2021)
by: Bar, Noga, et al.
Published: (2021)
Cryptographic Backdoor for Neural Networks: Boon and Bane
by: Ngo, Anh Tu, et al.
Published: (2025)
by: Ngo, Anh Tu, et al.
Published: (2025)
Preserving Node-level Privacy in Graph Neural Networks
by: Xiang, Zihang, et al.
Published: (2023)
by: Xiang, Zihang, et al.
Published: (2023)
Hard-Label Cryptanalytic Extraction of Neural Network Models
by: Chen, Yi, et al.
Published: (2024)
by: Chen, Yi, et al.
Published: (2024)
Adversarial Attacks on Locally Private Graph Neural Networks
by: Varun, Matta, et al.
Published: (2026)
by: Varun, Matta, et al.
Published: (2026)
Deep-Lock: Secure Authorization for Deep Neural Networks
by: Alam, Manaar, et al.
Published: (2020)
by: Alam, Manaar, et al.
Published: (2020)
Similar Items
-
SFPDML: Securer and Faster Privacy-Preserving Distributed Machine Learning based on MKTFHE
by: Wang, Hongxiao, et al.
Published: (2022) -
Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning
by: Shan, Junjie, et al.
Published: (2024) -
Deep Efficient Private Neighbor Generation for Subgraph Federated Learning
by: Zhang, Ke, et al.
Published: (2024) -
Certified Robust Accuracy of Neural Networks Are Bounded due to Bayes Errors
by: Zhang, Ruihan, et al.
Published: (2024) -
Accuracy Improvement in Differentially Private Logistic Regression: A Pre-training Approach
by: Hoseinpour, Mohammad, et al.
Published: (2023)