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Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations
We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN.
Artificial Intelligence Based Malware Analysis
Artificial intelligence methods have often been applied to perform specific functions or tasks in the cyber-defense realm.
Structured Factored Inference: A Framework for Automated Reasoning in Probabilistic Programming Languages
Reasoning on large and complex real-world models is a computationally difficult task, yet one that is required for effective use of many AI applications.
Lazy Factored Inference for Functional Probabilistic Programming
In this paper, we present a new inference framework, lazy factored inference (LFI), that enables factored algorithms to be used for models with infinitely many variables.
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