Precise control of thedistribution of specific proteins is essential for many biological processes. An LMU team has now described a new model for intracellular pattern formation. Here, the shape of the cell itself plays a ...
System assembles and parses data from WiFi internet sniffer, police checkpoints, banking records and more, and notifies police if it flags anything suspicious
You know the routine. You come across a topological space X and you need to find its fundamental group. Unfortunately, X is an unfamiliar space and it's too difficult to look at explicit loops and relations. So what do you do? You look for another space Y that is homotopy equivalent to X and whose fundamental group is much easier to compute. And voila! Since X and Y are homotopy equivalent, you know that the fundamental group of X is isomorphic to the fundamental group of Y. Mission accomplished. Below is a list of some homotopy equivalences which I think are pretty clever and useful to keep in your back pocket for, say, a qualifying exam or some other pressing topological occasion.
CMake produces Visual Studio solutions seamlessly. This post will map CMake commands to the Visual Studio IDE with an example which makes learning much easier.
This post was originally published on the All things Linguistic blog about a year ago by Gretchen McCulloch. Gretchen started blogging as a linguistics grad student at McGill University, but is now a full-time pop linguist, bridging the gap between linguistics and the general public. She writes pop linguistics articles for various places and is currently writing a book about…
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You’ve framed your problem, prepared your datasets, designed your models and revved up your GPUs. With bated breath, you start training your neural network, hoping to return in a few days to great…
Neural networks are the workhorse of many of the algorithms developed at DeepMind. For example, AlphaGo uses convolutional neural networks to evaluate board positions in the game of Go and DQN and Deep Reinforcement Learning algorithms use neural networks to choose actions to play at super-human level on video games. This post introduces some of our latest research in progressing the capabilities and training procedures of neural networks called Decoupled Neural Interfaces using Synthetic Gradients. This work gives us a way to allow neural networks to communicate, to learn to send messages between themselves, in a decoupled, scalable manner paving the way for multiple neural networks to communicate with each other or improving the long term temporal dependency of recurrent networks.
Humans excel at solving a wide variety of challenging problems, from low-level motor control through to high-level cognitive tasks. Our goal at DeepMind is to create artificial agents that can achieve a similar level of performance and generality. Like a human, our agents learn for themselves to achieve successful strategies that lead to the greatest long-term rewards.
Until two days back, the name, Alexandre Grothendieck, was vaguely familiar to me through my distant understanding of the mathematics he had done. I knew that he was responsible for a lot of upheavals in that field during the last century; other than that, Grothendieck remained very much outside the purview of my knowledge. Yesterday…
Have you tried using software from way off the beaten path? Maybe you tried to make software for your graphing calculator and realized that you were one of five people to ever try that and there was…
I spoke at the ACCU conference in April 2017 on the topic of Embracing Modern CMake. The talk was very well attended and received, but was unfortunately not recorded at the event. In September I gave the talk again at the Dublin C++ User Group, so that it could be recorded for the internet. https://www.youtube.com/watch?v=JsjI5xr1jxM…