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Neural Network


Last year, MIT researchers announced that they had built “liquid” neural networks, inspired by the brains of small species: a class of flexible, robust machine learning models that learn on the job and can adapt to changing conditions, for real-world safety-critical tasks, like driving and flying. The flexibility of these “liquid” neural nets meant boosting the …

Ask a smart home device for the weather forecast, and it takes several seconds for the device to respond. One reason this latency occurs is because connected devices don’t have enough memory or power to store and run the enormous machine-learning models needed for the device to understand what a user is asking of it. …

How deep-network models take potentially dangerous ‘shortcuts’ in solving complex recognition tasks. Deep convolutional neural networks (DCNNs) don’t see objects the way humans do — using configural shape perception — and that could be dangerous in real-world AI applications, says Professor James Elder, co-author of a York University study published today. Published in the Cell …

By using hypernetworks, researchers can now preemptively fine-tune artificial neural networks, saving some of the time and expense of training. Artificial intelligence is largely a numbers game. When deep neural networks, a form of AI that learns to discern patterns in data, began surpassing traditional algorithms 10 years ago, it was because we finally had …

MIT researchers created a technique that can automatically describe the roles of individual neurons in a neural network with natural language. In this figure, the technique was able to identify “the top boundary of horizontal objects” in photographs, which are highlighted in white. Photographs courtesy of the researchers, edited by Jose-Luis Olivares, MIT Neural networks …

MIT researchers have developed a type of neural network that learns on the job, not just during its training phase. These flexible algorithms, dubbed “liquid” networks, change their underlying equations to continuously adapt to new data inputs. The advance could aid decision making based on data streams that change over time, including those involved in …

As a student pursuing a doctorate in systems design engineering at the University of Waterloo, Alexander Wong didn’t have enough money for the hardware he needed to run his experiments in computer vision. So he invented a technique to make neural network models smaller and faster. “He was giving a presentation, and somebody said, ‘Hey, …

Overview This guide shows how to install Keras. Keras is a high-level neural networks API written and for Python. It is also capable of running on top of TensorFlow.   Prerequisites Python has been installed Installation on Ubuntu Installation on Windows Optional but recommended. Setup a VirtualEnvironment and Pip has been installed. VirtualEnvironment for Ubuntu …

A common industry standard for deep learning hardware is the use of neural network quantization, as noted by an MIT study. Neural networks are computation and memory intensive. Quantization cuts down on the usage of these resources by stripping an input of details which won’t gravely affect the information conveyed. As an example, consider image …

When mundane objects such as cords, keys and cloths are fed into a live webcam, a machine-learning algorithm ‘sees’ brilliant colours and images such as seascapes and flowers instead. The London-based, Turkish-born visual artist Memo Akten applies algorithms to the webcam feed as a way to reflect on the technology and, by extension, on ourselves. …