For example, neurons could be highly polysemantic (representing many distinct concepts) or could represent single concepts that humans don't understand or have words for. We want to eventually automatically find and explain entire neural circuits(opens in a new window) implementing complex ...
@incollection{NIPS2017_7062, title = {A Unified Approach to Interpreting Model Predictions}, author = {Lundberg, Scott M and Lee, Su-In}, booktitle = {Advances in Neural Information Processing Systems 30}, editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. ...
ImageNet VGG16 Model with Keras- Explain the classic VGG16 convolutional neural network's predictions for an image. This works by applying the model agnostic Kernel SHAP method to a super-pixel segmented image. Iris classification- A basic demonstration using the popular iris species dataset. It ...
" for example. Ironically, though we admire the remarkable grace of a ballet dancer, the leaps and bounds of a world-class athlete, or the painstaking care of a traditional craftsman, we take it for granted that robots will be able to zing about or make things for us with even greater ...
For this example, the black-box model is implemented using a deep neural network (DNN), which requires Deep Learning Toolbox™ software. Black-Box Model The DNN model for this example imitates an automotive lane keeping assist (LKA) system implemented using model predictive control (MPC). A...
For example, one approach was to train many different models with different goals, and examine how they perform in predicting human behaviour, thus controlling for the model’s goal12, and another approach was to use adversarial examples that meant misleading a model and thus gaining insights on...
Imagine a computer trying to commentate on a fast-moving sports event, such as a rodeo, for example. Even if it could watch and correctly interpret the action, and even if it had all the right words to speak, could it really convey the right kind of emotion? Photo by Carol M. ...
To enable explainability, we propose to include a pixel-level classification step into the neural network. Pixel-level classification, also known as image segmentation, is generally performed by an Encoder-Decoder Network (EDN) (Ronneberger, Fischer, Brox, 2015, Lin, Dollár, Girshick, He, ...
“With BERT, being a technology that creates ‘contextualized’ vectors, I will have to feed both sentences into the BERT network,” says Coyle.“That means I need to do to thousands of FLOPS as BERT is a deep neural network with many layers and neurons.” ...
example forgetting: First, we introduce a method that effectively relates semantically malfunctioned predictions to their respectful positions within the neural network representation manifold. More concrete, our method tracks how models "forget" seismic reflections during training and establishes a connection...