Why do plants' leaves shrink the further from the equator they grow? It may all be to do with maintaining a comfortable temperature. Leaves vary greatly in size, from less than 1 square millimetre to almost 1 square metre. Large-leaved plants like bananas and palms tend to live in the tropics, while small-leaved plants like heather and clover are found closer to the poles. Botanists first noticed this latitude trend in the 19th century, but nobody has convincingly explained it.
Industrial chlorofluorocarbons that cause ozone depletion have been phased out under the Montreal Protocol. A chemically driven increase in polar ozone (or "healing") is expected in response to this historic agreement. Observations and model calculations together indicate that healing of the Antarctic ozone layer has now begun to occur during the month of September. Fingerprints of September healing since 2000 include (i) increases in ozone column amounts, (ii) changes in the vertical profile of ozone concentration, and (iii) decreases in the areal extent of the ozone hole. Along with chemistry, dynamical and temperature changes have contributed to the healing but could represent feedbacks to chemistry.
Due to the high computational demands executing a rigorous comparison between hyperparameter optimization (HPO) methods is often cumbersome. The goal of this paper is to facilitate a better empirical evaluation of HPO methods by providing benchmarks that are cheap to evaluate, but still represent realistic use cases. We believe these benchmarks provide an easy and efficient way to conduct reproducible experiments for neural hyperparameter search. Our benchmarks consist of a large grid of configurations of a feed forward neural network on four different regression datasets including architectural hyperparameters and hyperparameters concerning the training pipeline. Based on this data, we performed an in-depth analysis to gain a better understanding of the properties of the optimization problem, as well as of the importance of different types of hyperparameters. Second, we exhaustively compared various different state-of-the-art methods from the hyperparameter optimization literature on these benchmarks in terms of performance and robustness.
I have a Variational autoencoder model created in Keras.Encoder is built with three 3D Convolutional layers Flatten Dense layer. Decoder is built with three 3D Transposed Convolutional layers to reconstruct the input 3D images. My goal is to replace Flatten and Dense layer in Encoder with 1x1x1 Convolutional layer. Any ideas how to do that?
Bio: Ahmed Gad received his B.Sc. degree with excellent with honors in information technology from the Faculty of Computers and Information (FCI), Menoufia University, Egypt, in July 2015. For being ranked first in his faculty, he was recommended to work as a teaching assistant in one of the Egyptian institutes in 2015 and then in 2016 to work as a teaching assistant and a researcher in his faculty. His current research interests include deep learning, machine learning, artificial intelligence, digital signal processing, and computer vision.