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Found 4 papers, 1 papers with code
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human Programmers
Based on our findings, we propose DetectCodeGPT, a novel method for detecting machine-generated code, which improves DetectGPT by capturing the distinct stylized patterns of code.
Automatic and Efficient Customization of Neural Networks for ML Applications
ML APIs have greatly relieved application developers of the burden to design and train their own neural network models -- classifying objects in an image can now be as simple as one line of Python code to call an API.
Orthogonalized SGD and Nested Architectures for Anytime Neural Networks
We propose a novel variant of SGD customized for training network architectures that support anytime behavior: such networks produce a series of increasingly accurate outputs over time.
ALERT: Accurate Learning for Energy and Timeliness
An increasing number of software applications incorporate runtime Deep Neural Networks (DNNs) to process sensor data and return inference results to humans.
