Edge 265: Interpretability Methods for Deep Neural Networks
Was this email forwarded to you? Sign up here Edge 265: Interpretability Methods for Deep Neural NetworksInterpretability methods optimized for deep neural networks, OpenAI's interpretability technique to discover multimodal neurons on CLIP and the Eli5 framework.In this issue:
💡 ML Concept of the Day: Interpretability Methods for Deep Neural NetworksTo close our series about machine learning(ML) interpretability, we would like to discuss the evolution of the space given the fast growth of deep neural networks(DNNs). Most of the local and global model agnostic methods explored in this series are applicable to DNNs but they were mostly designed for simpler ML architectures. DNNs have some unique characteristics that provide new dimensions to explore by interpretability methods:
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