There are abundantly many explanation methods for explaining deep neural networks (DNNs), each with its advantages and disadvantages. In most cases, we are interested in local explanation methods, i.e. explanations of the network’s output for a particular input, because DNNs tend to be too complex to be explained globally (independent of an input).
… In this short article, I will present one fundamental attribution technique: occlusion analysis. The basic concept is as simple as they come: For every input dimension of an input x, we evaluate the model with that dimension missing, and observe how the output changes. Read More
Daily Archives: January 22, 2021
Evolvable neural units that can mimic the brain’s synaptic plasticity
Machine learning techniques are designed to mathematically emulate the functions and structure of neurons and neural networks in the brain. However, biological neurons are very complex, which makes artificially replicating them particularly challenging.
Researchers at Korea University have recently tried to reproduce the complexity of biological neurons more effectively by approximating the function of individual neurons and synapses. Their paper, published in Nature Machine Intelligence, introduces a network of evolvable neural units (ENUs) that can adapt to mimic specific neurons and mechanisms of synaptic plasticity. Read More
Artificial intelligence researchers rank the top A.I. labs worldwide
Artificial intelligence researchers don’t like it when you ask them to name the top AI labs in the world, possibly because it’s so hard to answer.
U.S. Big Tech — Google, Facebook, Amazon, Apple and Microsoft — have all set up dedicated AI labs over the last decade.
There’s also DeepMind, which is owned by Google parent company Alphabet, and OpenAI, which counts Elon Musk as a founding investor.
… “Reputationally, there is a good argument to say DeepMind, OpenAI, and FAIR (Facebook AI research]) are the top three,” according to Mark Riedl, associate professor at the Georgia Tech School of Interactive Computing. Read More
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