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Learning Minimal NAP Specifications for Neural Network Verification
In this paper, we study the following problem: Given a neural network, find a minimal (coarsest) NAP that is sufficient for formal verification of the network's robustness.
Towards Socially and Morally Aware RL agent: Reward Design With LLM
When we design and deploy an Reinforcement Learning (RL) agent, reward functions motivates agents to achieve an objective.
Spatial Attention-based Distribution Integration Network for Human Pose Estimation
The RFM incorporates a dilated residual block and attention mechanism to expand receptive fields while enhancing sensitivity to spatial information.
Towards Reliable Neural Specifications
We show that by using NAP, we can verify a significant region of the input space, while still recalling 84% of the data on MNIST.
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