Inspired by Kaggle’s Satellite Imagery Feature Detection challenge, I would like to find out how easy it is to detect features (roads in this particular case) in satellite and aerial images.
Research has established that a large percentage of dental caries escape identification in routine dental examinations, even when such examinations include dental x-rays. Certain types of caries, like occlusal caries, appear to be easier to find through a normal clinical examination or x-ray review, whereas diagnosing other types of caries, such as caries below the surface of the tooth, interproximal caries, and root caries, is often not as reliable.
Understanding why a model makes a certain prediction can be as crucial as the prediction’s accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.
NVIDIA announced that hundreds of thousands of AI researchers using desktop GPUs can now tap into the power of NVIDIA GPU Cloud (NGC) as the company has extended NGC support to NVIDIA TITAN. NVIDIA also announced expanded NGC capabilities — adding new software and other key updates to the NGC container registry — to provide researchers a broader, more powerful set of tools to advance their AI and high performance computing research and development efforts.
A Bayesian neural network (BNN) refers to extending standard networks with posterior inference. Standard NN training via optimization is (from a probabilistic perspective) equivalent to maximum likelihood estimation (MLE) for the weights.
We show how to use roxygen2 tags and templates for deprecating existing documented functions.