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Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks
Sadhana Lolla · Iaroslav Elistratov · Alejandro Perez · Elaheh Ahmadi · Daniela Rus · Alexander Amini

The deployment of large-scale deep neural networks in safety-critical scenariosrequires quantifiably calibrated and reliable measures of trust. Unfortunately,existing algorithms to achieve risk-awareness are complex and adhoc. We presentcapsa, an open-source and flexible framework for unifying these methods andcreating risk-aware models. We unify state-of-the-art risk algorithms under thecapsa framework, propose a composability method for combining different riskestimators together in a single function set, and benchmark on high-dimensionalperception tasks. Code is available at: https://github.com/themis-ai/capsa

Author Information

Sadhana Lolla (Massachusetts Institute of Technology)
Iaroslav Elistratov (Themis AI)
Alejandro Perez (MIT)
Elaheh Ahmadi (Themis AI)
Daniela Rus (Massachusetts Institute of Technology)
Alexander Amini (MIT)

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