The researchers trained meta-models for two domain-specific datasets: ImageNet and CIFAR-10. The researchers then used graph-learning approaches to train a hyper-model to predict parameters for input network designs that minimize network loss on domain-specific data. A domain-specific dataset, such as ImageNet, and a training set of model network designs defined as computational graphs are required for this task. The researchers devised a meta-learning challenge after working on a network architecture search (NAS) technique called Differentiable ARchiTecture Search (DARTS). The hyper-model can forecast network performance metrics when given a specified network topology. Therefore, the team designed a hyper-model that is trained on a specific dataset to cut the cost of training models. In many cases, researchers have to frequently train multiple models to discover the ideal network architecture and collection of hyperparameters, further increasing the cost. However, it can take many hours of calculation and a lot of energy to minimize. For this, generally, an iterative optimization technique such as stochastic gradient descent (SGD) or Adam is used. Training a deep-learning model on a dataset is the process of finding a set of model parameters that minimize the model’s loss function when assessed on training data. The meta-model revealed parameters that achieved 60% accuracy on CIFAR-10 with no gradient updates when used to start a 24M-parameter ResNet-50. Even for architectures far larger than the ones used in training, the resulting meta-model performs better at the task. They then employed meta-learning to train a modified graph hyper-network (GHN) using this dataset, which can be used to forecast parameters for a network architecture that has never been seen before. This dataset contains one million examples of neural network architectures expressed as computational graphs. The researchers created the DeepNets-1M dataset to solve the problem of guessing initial parameters for deep-learning models. With no extra training, GHN-2 runs in less than a second on a CPU and predicts values for computer vision (CV) networks that reach up to 77 percent top-1 accuracy on CIFAR-10. To overcome such shortcomings, Facebook AI Research (FAIR) and the University of Guelph have released an updated Graph HyperNetworks (GHN-2) meta-model that predicts starting parameters for deep-learning neural networks. However, many researchers reveal that the techniques for improving neural network parameters are still mostly hand-crafted and computationally inefficient. Have you had any trouble with pop-ups on UC Browser? Share your questions via the comments below.įor more tutorials, visit our How-To section.In machine learning pipelines, deep learning has proved successful in automating feature design. If you'd like to change the pop-up blocker setting on UC Browser for iOS, follow these steps:
How to block pop-ups in UC Browser (iPhone) Go to Settings from the quick menu on the bottom of the screen.If you'd like to change the pop-up blocker setting on UC Browser for Android, follow these steps: How to block pop-ups in UC Browser (Android) While UC Browser is the most popular browser in India - across desktop, mobile, and tablet combined - we've also written about Chrome, Firefox, and Opera, if you don't use UC Browser.
Here's how you can block pop-ups in UC Browser on Android and iOS. That's bad for publishers (like us) who depend on the ads they serve, so if there's a website you like, consider white-listing them. Instead, its ad-block function takes care of both ads and pop-ups. UC Browser doesn't have a standalone setting for blocking pop-ups. We looked at how different browsers handle pop-ups, and the most popular browser in India (just ahead of Chrome) is UC Browser. This enables you to automatically prevent pop-up ads, although it's not entirely foolproof. On mobile, it's much more annoying, given pop-ups tend to take over the entire screen. With most modern browsers - like Google Chrome, UC Browser, Opera, and Firefox - you don't have to worry, since they have a built-in pop-up blocker. If you've ever experienced the frustration of a pop-up flying across the screen while reading an article, you've probably wondered how to get rid of it.