NeRN -- Learning Neural Representations for Neural Networks

Neural Representations have recently been shown to effectively reconstruct a wide range of signals from 3D meshes and shapes to images and videos. We show that, when adapted correctly, neural representations can be used to directly represent the weights of a pre-trained convolutional neural network, resulting in a Neural Representation for Neural Networks (NeRN). Inspired by coordinate inputs of previous neural representation methods, we assign a coordinate to each convolutional kernel in our network based on its position in the architecture, and optimize a predictor network to map coordinates to their corresponding weights. Similarly to the spatial smoothness of visual scenes, we show that incorporating a smoothness constraint over the original network's weights aids NeRN towards a better reconstruction. In addition, since slight perturbations in pre-trained model weights can result in a considerable accuracy loss, we employ techniques from the field of knowledge distillation to stabilize the learning process. We demonstrate the effectiveness of NeRN in reconstructing widely used architectures on CIFAR-10, CIFAR-100, and ImageNet. Finally, we present two applications using NeRN, demonstrating the capabilities of the learned representations

Wavelet Feature Maps Compression for Image-to-Image CNNs

Convolutional Neural Networks (CNNs) are known for requiring extensive computational resources, and quantization is among the best and most common methods for compressing them. While aggressive quantization (i.e., less than 4-bits) performs well for classification, it may cause severe performance degradation in image-to-image tasks such as semantic segmentation and depth estimation. In this paper, we propose Wavelet Compressed Convolution (WCC) -- a novel approach for high-resolution activation maps compression integrated with point-wise convolutions, which are the main computational cost of modern architectures. To this end, we use an efficient and hardware-friendly Haar-wavelet transform, known for its effectiveness in image compression, and define the convolution on the compressed activation map. We experiment on various tasks, that benefit from high-resolution input, and by combining WCC with light quantization, we achieve compression rates equivalent to 1-4bit activation quantization with relatively small and much more graceful degradation in performance

Nationwide Outbreak of Candida auris Infections Driven by COVID-19 Hospitalizations, Israel, 2021–2022

We report an outbreak of Candida auris across multiple healthcare facilities in Israel. For the period of May 2014–May 2022, a total of 209 patients with C. auris infection or colonization were identified. The C. auris incidence rate increased 30-fold in 2021 (p = 0.00015), corresponding in time with surges of COVID-19–related hospitalization. Multilocus sequence typing revealed hospital-level outbreaks with distinct clones. A clade III clone, imported into Israel in 2016, accounted for 48.8% of typed isolates after January 2021 and was more frequently resistant to fluconazole (100% vs. 63%; p = 0.00017) and voriconazole (74% vs. 5.2%; p<0.0001) than were non–clade III isolates. A total of 23% of patients had COVID-19, and 78% received mechanical ventilation. At the hospital level, outbreaks initially involved mechanically ventilated patients in specialized COVID-19 units and then spread sequentially to ventilated non–COVID-19 patients and nonventilated patients