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GPBoost is a software library for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. The GPBoost library is predominantly written in C++, and there exist both a
2Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
3Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
Do men and women have different brains? Previous neuroimage studies sought to answer this question based on morphological difference between specific brain regions, reporting unfortunately conflicting results. In the present study, we aim to use a deep learning technique to address this challenge based on a large open-access, diffusion MRI database recorded from 1,065 young healthy subjects, including 490 men and 575 women healthy subjects. Different from commonly used 2D Convolutional Neural Network (CNN), we proposed a 3D CNN method with a newly designed structure including three hidden layers in cascade with a linear layer and a terminal Softmax layer. The proposed 3D CNN was applied to the maps of factional anisotropy (FA) in the whole-brain as well as s
King’s College London
King’s Lecturer in Computer Science, Dr Helen Yannakoudakis, was part of a team that enjoyed success in Facebook AI’s ‘Hateful Memes Challenge’ competition.
The ‘Hateful Memes Challenge’ competition set by Facebook AI, Getty Images and DrivenData addressed the difficulty of using AI to decide if a meme is offensive. AI can identify ‘hateful’ text or ‘hateful’ images, but this becomes more complex when images and text that might be inoffensive on their own are combined to make a meme.
More than 3,000 people from 150 countries took part in the competition. Teams were presented with a unique dataset of over 10,000 memes, and set with the goal of developing multimodal machine learning models to automatically identify hateful content.
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Each year, researchers from around the world gather at Neural Information Processing Systems, an artificial-intelligence conference, to discuss automated translation software, self-driving cars, and abstract mathematical questions. It was odd, therefore, when Michael Levin, a developmental biologist at Tufts University, gave a presentation at the 2018 conference, which was held in Montreal. Fifty-one, with light-green eyes and a dark beard that lend him a mischievous air, Levin studies how bodies grow, heal, and, in some cases, regenerate. He waited onstage while one of Facebook’s A.I. researchers introduced him, to a packed exhibition hall, as a specialist in “computation in the medium of living systems.”