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Lack of transparency has been the Achilles heal of Neural Networks and their wider adoption in industry. Despite significant interest this shortcoming has not been adequately addressed. This study proposes a novel framework called Hide-and-Seek (HnS) for training Interpretable Neural Networks and establishes a theoretical foundation for exploring and comparing similar ideas. Extensive experimentation indicates that a high degree of interpretability can be imputed into Neural Networks, without sacrificing their predictive power.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2005.00130")
get_code_for_paper("2005.00130")
have("2005.00130")
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