Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks

Deep Neural Networks (DNN) have shown great promise in many classification applications, yet are widely known to have poorly calibrated predictions when they are over-parametrized. Improving DNN calibration without comprising on model accuracy is of extreme importance and interest in safety critical applications such as in the health-care sector. In this work, we show that decoupling the training of feature extraction layers and classification layers in over-parametrized DNN architectures such as Wide Residual Networks (WRN) and Visual Transformers (ViT) significantly improves model calibration whilst retaining accuracy, and at a low training cost. In addition, we show that placing a Gaussian prior on the last hidden layer outputs of a DNN, and training the model variationally in the classification training stage, even further improves calibration. We illustrate these methods improve calibration across ViT and WRN architectures for several image classification benchmark datasets.Comment: Proceedings of the 41 st International Conference on Machine Learning (ICML) 202

Reversal of a Suspected Paradoxical Reaction to Zopiclone with Flumazenil

We describe the care for an elderly woman who was admitted to the intensive care unit (ICU) to receive noninvasive ventilation for acute exacerbation of chronic obstructive pulmonary disease. After administration of the sleeping pill zopiclone, a nonbenzodiazepine receptor agonist (NBRA), the patient became agitated and was confused, a possible paradoxical reaction to benzodiazepines. These symptoms were immediately resolved after treatment with flumazenil, usually used to reverse the adverse effects of benzodiazepines or NBRAs and to reverse paradoxical reactions to benzodiazepines. This case indicates that zopiclone induced behavioral changes resembling a paradoxical reaction to benzodiazepines and these symptoms may be treated with flumazenil