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Found 13 papers, 7 papers with code
PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks
Most methods for neural network verification focus on bounding the image, i. e., set of outputs for a given input set.
Protecting Deep Learning Model Copyrights with Adversarial Example-Free Reuse Detection
To the best of our knowledge, this is the first adversarial example-free method that exploits neuron functionality for DNN copyright protection.
Automated Design of Linear Bounding Functions for Sigmoidal Nonlinearities in Neural Networks
In this work, we propose a novel parameter search method to improve the quality of these linear approximations.
When to Trust AI: Advances and Challenges for Certification of Neural Networks
Artificial intelligence (AI) has been advancing at a fast pace and it is now poised for deployment in a wide range of applications, such as autonomous systems, medical diagnosis and natural language processing.
Weighted Automata Extraction and Explanation of Recurrent Neural Networks for Natural Language Tasks
In this paper, we propose a novel framework of Weighted Finite Automata (WFA) extraction and explanation to tackle the limitations for natural language tasks.
Provable Preimage Under-Approximation for Neural Networks (Full Version)
Neural network verification mainly focuses on local robustness properties, which can be checked by bounding the image (set of outputs) of a given input set.
Using Z3 for Formal Modeling and Verification of FNN Global Robustness
To evaluate the effectiveness of our implementation and improvements, we conduct extensive experiments on a set of benchmark datasets.
Extracting Weighted Finite Automata from Recurrent Neural Networks for Natural Languages
Compositional approaches that are scablable to natural languages fall short in extraction precision.
Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning
Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e. g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes.
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment.
Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation
In particular: i) complex inter-dependencies across different types of user behaviors; ii) the incorporation of knowledge-aware item relations into the multi-behavior recommendation framework; iii) dynamic characteristics of multi-typed user-item interactions.
Global Robustness Verification Networks
The wide deployment of deep neural networks, though achieving great success in many domains, has severe safety and reliability concerns.
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty
Based on this, we propose an automated testing technique to generate multiple types of uncommon AEs and BEs that are largely missed by existing techniques.
