This presentation introduces the OWASP Top 10:2021.
It explains how to look at the data related to OWASP Top 10:2021, and provides detailed explanations of items with distinctive data. It also introduces the OWASP Project related to each item.
1. The document discusses energy-based models (EBMs) and how they can be applied to classifiers. It introduces noise contrastive estimation and flow contrastive estimation as methods to train EBMs.
2. One paper presented trains energy-based models using flow contrastive estimation by passing data through a flow-based generator. This allows implicit modeling with EBMs.
3. Another paper argues that classifiers can be viewed as joint energy-based models over inputs and outputs, and should be treated as such. It introduces a method to train classifiers as EBMs using contrastive divergence.
This presentation introduces the OWASP Top 10:2021.
It explains how to look at the data related to OWASP Top 10:2021, and provides detailed explanations of items with distinctive data. It also introduces the OWASP Project related to each item.
1. The document discusses energy-based models (EBMs) and how they can be applied to classifiers. It introduces noise contrastive estimation and flow contrastive estimation as methods to train EBMs.
2. One paper presented trains energy-based models using flow contrastive estimation by passing data through a flow-based generator. This allows implicit modeling with EBMs.
3. Another paper argues that classifiers can be viewed as joint energy-based models over inputs and outputs, and should be treated as such. It introduces a method to train classifiers as EBMs using contrastive divergence.