MAI*MA Using Monte Carlo Method

The first thoughts and attempts I made to practice [the Monte Carlo Method] were suggested by a question which occurred to me in 1946 as I was convalescing from an illness and playing solitaires. The question was what are the chances that a Canfield solitaire laid out with 52 cards will come out successfully? After spending a lot of time trying to estimate them by pure combinatorial calculations, I wondered whether a more practical method than “abstract thinking” might not be to lay it out say one hundred times and simply observe and count the number of successful plays. This was already possible to envisage with the beginning of the new era of fast computers, and I immediately thought of problems of neutron diffusion and other questions of mathematical physics, and more generally how to change processes described by certain differential equations into an equivalent form interpretable as a succession of random operations. Later [in 1946], I described the idea to John von Neumann, and we began to plan actual calculations.[14]

https://en.wikipedia.org/wiki/Monte_Carlo_method?wprov=sfti1

Read moreMAI*MA Using Monte Carlo Method


 
  To access MetaSyst data, you must be logged in.

Maîtrise en informatique – Université de Montréal

Les études au niveau de la maîtrise visent une spécialisation dans un domaine de l’informatique au moyen de cours avancés. Elles ont pour but d’initier l’étudiant à la recherche par l’exploration d’un sujet limité et la rédaction d’un mémoire. Il est également possible dans l’option Générale modalité Stage d’effectuer un stage en entreprise de deux … Read more Maîtrise en informatique – Université de Montréal


 
  To access MetaSyst data, you must be logged in.

A digital god for a digital culture. Resonate 2016 – Artists + Machine Intelligence – Medium

Artificial Intelligence Alongside my arts practice, I’ve also recently started a PhD at Goldsmiths University, broadly speaking looking at this kind of stuff. Specifically investigating artificial intelligence — with a focus on machine learning / deep learning and combining with agent-based AI such as reinforcement learning — and how it can be used for this … Read more A digital god for a digital culture. Resonate 2016 – Artists + Machine Intelligence – Medium


 
  To access MetaSyst data, you must be logged in.

Predict a Mask – [1703.06870] Mask R-CNN

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, M

Source: [1703.06870] Mask R-CNN

Read morePredict a Mask – [1703.06870] Mask R-CNN


 
  To access MetaSyst data, you must be logged in.