Most AI systems in networking treat operations like a language problem. But networks are fundamentally graphs built on relationships, dependencies, and topology. At NFD40, NetAI presented a compelling case for why Graph Neural Networks may be better suited for deterministic root cause analysis and autonomous network operations

Deep neural networks, empowered by pre-trained language models, have achieved remarkable results in natural language understanding (NLU) tasks. However, their performances can drastically deteriorate when logical reasoning is needed. This is because NLU in principle depends on not only analogical reasoning, which deep neural networks are good at, but also logical reasoning. According to the dual-process theory, analogical reasoning and logical reasoning are respectively carried out by System 1 and System 2

I will take a closer look at the differences in the obtained representations between CNN and Transformers

Just like the mass of neurons in your brain, a neural network helps a computer system find the right answer to a query. Learn how it works in real life

Find 2,013 Neural images and millions more royalty free PNG & vector images from the world's most diverse collection of free icons

This is the 24th article of ISer Advent Calendar 2019. Introduction "Neural Turing Machine"(NTM) is the neural network architecture introduced by DeepMind team in 2014. *1It is the combination of recurrent neural networks and external memory resources. As the name of the architecture mentions, it is

Skip to content --> The Barak Lab Theoretical Neuroscience . Advanced topic in systems neuroscience 2016 Advanced Topics in Systems Neuroscience 2014 Reverse engineering trained networks The phones we hold can translate from one language to another. Yet, the engineers who programmed them typically do not know how this translation happens. This is because machine learning specifies a target macroscopic behavior, and a set of microscopic rules that enable networks to obtain them. In a sense, this is similar t

Neural networks are a type of machine learning program that learns from examples they’re given, rather than relying on a human programmer to invent rules. In an earlier experiment, I trained a neural network to write new names for Dungeons and Dragons spells based on a list of 365 examples. That’s a really small dataset for a neural network to work with, and I ended up struggling to find training parameters that would strike a balance between word-for-word mimicry of the original list of spells

A spiking neural network (SNN) is a type of neural network in which neurons communicate with discrete pulses, called spikes, timed like the electrical signals

In this tutorial, we'll learn how to create a recurrent neural network in TensorFlow. This type of network is designed to process sequences of data, such as