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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.
Can ChatGPT Pass An Introductory Level Functional Language Programming Course?
The recent introduction of ChatGPT has drawn significant attention from both industry and academia due to its impressive capabilities in solving a diverse range of tasks, including language translation, text summarization, and computer programming.
Scalar Invariant Networks with Zero Bias
However, we argue that biases can be disregarded for some image-related tasks such as image classification, by considering the intrinsic distribution of images in the input space and desired model properties from first principles.
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.
Novice Type Error Diagnosis with Natural Language Models
Strong static type systems help programmers eliminate many errors without much burden of supplying type annotations.
