As deep neural net architectures minimize loss, they accumulate information in a hierarchy of learned representations that ultimately serve the network’s final goal. Different architectures tackle this problem in slightly different ways, but all create intermediate representational spaces built to inform their final prediction. Here we show that very different neural networks trained on two very different tasks build knowledge representations that display similar underlying patterns. Namely, we show that the representational spaces of several distributional semantic models bear a remarkable resemblance to several Convolutional Neural Network (CNN) architectures (trained for image classification). We use this information to explore the network behavior of CNNs (1) in pretrained models, (2) during training, and (3) during adversarial attacks. We use these findings to motivate several applications aimed at improving future research on CNNs. Our work illustrates the power of using one model to explore another, gives new insights into the function of CNN models, and provides a framework for others to perform similar analyses when developing new architectures. We show that one neural network model can provide a window into understanding another.
Feb 26th 2023
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Aug 8th 2022
Read this research paper co-authored by Canada CIFAR AI Chair Angel Chang: Learning Expected Emphatic Traces for Deep RL
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