The motivation behind this project is the need for 3D representation by doctors in Orthodontics to assist diagnosis. However the limit on the amount of dose could be used for pediatric patients inhibit the amount of CT scans could be done. There are other systems on the market such EOS Imaging Flex Dose which is based on an atlas based registration method to reconstruct two 2D x-ray projections into 3D spinal representation. However such system is not only very expensive (selling in USA at $1M for one system) but also produce certain image artifacts due to the atlas is based on normal people. This work is to show the feasibility of deep learning could surpass performance of these other systems and achieve high resolution spinal 3D representations using 2D images.

In general, the hyperparams are related - if you perturb one hyperparam, you need to perturb some other hyperparams also to get satisfactory results. Some people do a random search on their hyperparam grid but if one hyperparam is very sensitive to changes in the other hyperparams, then the search will be more difficult. Personally, I've had OK results using Cyclic Learning Rate together with batchnorm and only have 3 values for the max-learning-rate hyperparam in my hyperparam grid. However, you probably won't find many papers on CLR because its efficacy and the details of the right way to use it is probably quite problem-specific and there's very little theory behind it even by deep-learning standards.

"This paper is all about reconstructing a 3D point cloud from single depth map for Anthropomorphic body part (Human Feet).

This story introduces you to a Github repository which contains an atomic up-to-date Attention layer implemented using Keras backend operations. With the unveiling of TensorFlow 2.0 it is hard to ignore the conspicuous attention (no pun intended!) There was greater focus on advocating Keras for implementing deep networks. Keras in TensorFlow 2.0 will come with three powerful APIs for implementing deep networks. For more information, get first hand information from TensorFlow team. However remember that while choosing advance APIs give more "wiggle room" for implementing complex models, they also increase the chances of blunders and various rabbit holes.

Deep learning, a type of artificial intelligence, can boost the power of MRI in predicting attention deficit hyperactivity disorder (ADHD), according to a study published in Radiology: Artificial Intelligence. Researchers said the approach could also have applications for other neurological conditions. The human brain is a complex set of networks. Advances in functional MRI, a type of imaging that measures brain activity by detecting changes in blood flow, have helped with the mapping of connections within and between brain networks. This comprehensive brain map is referred to as the connectome.

When your mother calls your name, you know it's her voice -- no matter the volume, even over a poor cell phone connection. And when you see her face, you know it's hers -- if she is far away, if the lighting is poor, or if you are on a bad FaceTime call. This robustness to variation is a hallmark of human perception. On the other hand, we are susceptible to illusions: We might fail to distinguish between sounds or images that are, in fact, different. Scientists have explained many of these illusions, but we lack a full understanding of the invariances in our auditory and visual systems.

Machine learning has been the most sought after online course. It includes … Neural Networks and Deep learning by deeplearning.ai.

Sign in to report inappropriate content. A common question asked by journalists to Yoshua in this past year. In this part of his keynote, Yoshua explains that Deep Learning IS the thing and also how we can scale each individual part including optimization methods and representations.

In the first of a four-part series, Yoshua Bengio opens his hour-long Keynote from October 2019 discussing the current state of Deep Learning and how Human-level AI capabilities have been worked toward in 2019. Yoshua's opening remarks proclaimed that there are principles giving rise to intelligence, both machine or animal, which can be described using the laws of physics. That is, that our intelligence is not gained through a big bag of tricks, but rather the use of mechanisms used to specifically acquire knowledge. Similar to the laws of physics, we should consider understanding the physical world, mostly by having figured out the laws of physics, not just by describing its consequences. Join pioneers like Yoshua at RE•WORK events in 2020!

It feels as though 2019 has gone by in a flash, that said, it has been a year in which we have seen great advancement in AI application methods and technical discovery, paving the way for future development. We are incredibly grateful to have had the leading minds in AI & Deep Learning present their latest work at our summits in San Francisco, Boston, Montreal and more, so we thought we would share thirty of our highlight videos with you as we think everybody needs to see them!. We were delighted to be joined by Dawn at the Deep Reinforcement Learning Summit in June of 2019, presenting the latest industry research on Secure Deep Reinforcement Learning, covering both the lessons leant in the lead up to her presentation, current challenges faced for advancement, and the future direction of which her research is set to take. You can see Dawn's full presentation from June here. Reinforcement Learning is somewhat of a hotbed for research, this year alone we have seen several presentations that have broken down the ins and outs of RL, that said, Doina's talk just last month gave us some new angles on the latest algorithmic development.