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 ...
We address this issue by explaining model behaviour and improving generalization properties through 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 ...
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models Interpretable generalized additiv...
Graph Neural Network(GNN) is a type of neural network that can be directly applied to graph-structured data. My previous post gave a brief introduction on GNN. Readers may be directed to this post…
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 ...
The results are consistent with former research illustrating associations between impulsivity and symptoms of social-networks-use disorder46,47,48and of other specific Internet-use disorders e.g.,33,36,59. For example, the reward system in the brain (amygdala-striatal system), which creates states...
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...
Finally, we con- would be very accurate (for example, the review "This clude with an outlook on the connection between movie was anything but terrible!" suggests a positive interpretability and training models to communicate sentiment). with natural language. Given that humans and neural networks...
One example of a black-box machine learning model is a simple neural network model with one or two hidden layers. Even though you can write out the equations that link every input in the model to every output, you might not be able to grasp the meaning of the connections simply by ...
MNIST Digit classification with Keras - Using the MNIST handwriting recognition dataset, this notebook trains a neural network with Keras and then explains predictions using shap. Keras LSTM for IMDB Sentiment Classification - This notebook trains an LSTM with Keras on the IMDB text sentiment analysi...