Explaining the behavior of trained neural networks remains a compelling puzzle, especially as these models grow in size and sophistication. Like other scientific challenges throughout history, reverse-engineering how artificial intelligence systems work requires a substantial amount of experimentation: making hypotheses, intervening on behavior, and even dissecting large networks to examine individual neurons.
A team at Los Alamos National Laboratory has developed a novel approach for comparing neural networks that looks within the "black box" of artificial intelligence to help researchers understand neural network behavior. Neural networks recognize patterns in datasets; they are used everywhere in society, in applications such as virtual assistants,…
A team has developed a novel approach for comparing neural networks that looks within the 'black box' of artificial intelligence to help researchers understand neural network behavior. Neural networks recognize patterns in datasets; they are used everywhere in society, in applications such as virtual assistants, facial recognition systems and self-driving…
You can learn a lot about neural networks and deep learning models by observing their performance over time during training. For example, if you see the training accuracy went worse with training epochs, you know you have issue with the optimization. Probably your learning rate is too fast. In this…