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Found 4 papers, 1 papers with code
Mapping DNN Embedding Manifolds for Network Generalization Prediction
Understanding Deep Neural Network (DNN) performance in changing conditions is essential for deploying DNNs in safety critical applications with unconstrained environments, e. g., perception for self-driving vehicles or medical image analysis.
Network Generalization Prediction for Safety Critical Tasks in Novel Operating Domains
We propose the task Network Generalization Prediction: predicting the expected network performance in novel operating domains.
Robotic Surgery With Lean Reinforcement Learning
As far as we know, this is the first time an RL-based agent is taught from visual data in a surgical robotics environment.
Dependable Neural Networks for Safety Critical Tasks
Our results demonstrate that we can accurately predict the ML Dependability, Task Undependability, and Harmful Undependability for operating conditions that are significantly different from the testing conditions.
