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Found 4 papers, 1 papers with code
How Do You Act? An Empirical Study to Understand Behavior of Deep Reinforcement Learning Agents
We show that the healthy agent's behavior is characterized by a distinct correlation pattern between the network's layer activation and the performed actions during an episode and that network ablations, which cause a strong change of this pattern, lead to the agent failing its trained control task.
Under the Hood of Neural Networks: Characterizing Learned Representations by Functional Neuron Populations and Network Ablations
The need for more transparency of the decision-making processes in artificial neural networks steadily increases driven by their applications in safety critical and ethically challenging domains such as autonomous driving or medical diagnostics.
Ablation Studies in Artificial Neural Networks
considering the growth in size and complexity of state-of-the-art artificial neural networks (ANNs) and the corresponding growth in complexity of the tasks that are tackled by these networks, the question arises whether ablation studies may be used to investigate these networks for a similar organization of their inner representations.
Ablation of a Robot's Brain: Neural Networks Under a Knife
It is still not fully understood exactly how neural networks are able to solve the complex tasks that have recently pushed AI research forward.
