It is hard to answer this question without a proper definition of what "trending" means. Also "deep learning" is being used as a very broad category that even includes methods that are not per se "deep". For example, Adversarial Methods[1] are not necessarily connected to Deep Learning despite the fact that they have been popularized in that context. In any case, I will try to answer the question by looking at recent conferences (e.g.

In the last years, the extension of deep neural network architectures towards recurrent processing of temporal data has opened the way to novel approaches to effectively learn hierarchical representations of time-series featured by multiple timescales dynamics [19, 18, 10, 9, 1]. Recently, within the umbrella of randomized neural network approaches [4], Reservoir Computing (RC) [21, 15] has proved to be a useful tool for analyzing the intrinsic properties of stacked architectures in recurrent neural networks (RNNs), allowing at the same time to exploit the extreme efficiency of RC training algorithms in the design of novel deep RNN models. Stemming from the Echo State Network (ESN) approach [12] the study of the dynamics of multi-layered recurrent reservoir architectures has been introduced with the deep-ESN model in [7, 5].

We propose a novel deep network architecture for grayscale and color image denoising that is based on a non-local image model. Our motivation for the overall design of the proposed network stems from variational methods that exploit the inherent non-local self-similarity property of natural images. We build on this concept and introduce deep networks that perform non-local processing and at the same time they significantly benefit from discriminative learning. Experiments on the Berkeley segmentation dataset, comparing several state-of-the-art methods, show that the proposed non-local models achieve the best reported denoising performance both for grayscale and color images for all the tested noise levels. It is also worth noting that this increase in performance comes at no extra cost on the capacity of the network compared to existing alternative deep network architectures. In addition, we highlight a direct link of the proposed non-local models to convolutional neural networks. This connection is of significant importance since it allows our models to take full advantage of the latest advances on GPU computing in deep learning and makes them amenable to efficient implementations through their inherent parallelism.

BENGALURU: Intel and NVIDIA battle are locked in new battle for turf, the booming data centre market and at the heart of this skirmish the technology that's changing the world: Artificial Interlligence (AI). In the recent quarter ended April 30, NVIDIA's revenue increased by 48% reaching $1.94 billion compared to previous year. A big revenue bump came from its Data centre business which recorded $409 million revenue in the first quarter of this fiscal, up 186% year-on-year. The reason for the exponential increase is the spike in demand for a specific kind of microprocessor called Graphic Processing Unit (GPU) made by NVIDIA. Large technology companies like Google, Amazon, Microsoft, Facebook, IBM, and Alibaba have all installed NVIDIA's elite Tesla GPUs to power their data centres to perform machine learning to analyse data gathered from the cloud and derive insights. "We have seen the PC era, which was followed by the mobile era, and now we see the emergence of the AI era," said Vishal Dhupar, MD, NVIDIA, adding, "Once viewed just as a gaming technology, GPUs are now making inroads into data centres driving initiatives around machine learning (ML) and artificial intelligence (AI)".

There's a hall of champions at the University of Alberta that only computer science students know where to find -- more of a hallway, really, one office after the next, the achievements archived on hard drives and written in code. It's there you'll find the professors who solved the game of checkers, beat a top human player in the game of Go and used cutting-edge artificial intelligence to outsmart a handful of professional poker players for the very first time. But lately it's Richard Sutton who is catching people's attention on the Edmonton campus. He's a pioneer in a branch of artificial intelligence research known as reinforcement learning -- the computer science equivalent of treat-training a dog, except in this case the dog is an algorithm that's been incentivized to behave in a certain way. U of A computing science professors and artificial intelligence researchers (left to right) Richard Sutton, Michael Bowling and Patrick Pilarski are working with Google's DeepMind to open the AI company's first research lab outside the U.K., in Edmonton.

We'll look at some of the most important papers that have been published over the last 5 years and discuss why they're so important. The first half of the list (AlexNet to ResNet) deals with advancements in general network architecture, while the second half is just a collection of interesting papers in other subareas. The one that started it all (Though some may say that Yann LeCun's paper in 1998 was the real pioneering publication). This paper, titled "ImageNet Classification with Deep Convolutional Networks", has been cited a total of 6,184 times and is widely regarded as one of the most influential publications in the field. Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton created a "large, deep convolutional neural network" that was used to win the 2012 ILSVRC (ImageNet Large-Scale Visual Recognition Challenge). For those that aren't familiar, this competition can be thought of as the annual Olympics of computer vision, where teams from across the world compete to see who has the best computer vision model for tasks such as classification, localization, detection, and more. The next best entry achieved an error of 26.2%, which was an astounding improvement that pretty much shocked the computer vision community. Safe to say, CNNs became household names in the competition from then on out. In the paper, the group discussed the architecture of the network (which was called AlexNet).

I recently launched a new research project called RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning. I haven't seen a lot of content on /r/machinelearning that deals with biomedical data, so I thought that this project might be interesting to some of you! RIDDLE uses deep MLPs to estimate race and ethnicity information from electronic medical records. The underlying methodology is not anything new, but I think the application is meaningful. RIDDLE is primarily useful for epidemiology research where race & ethnicity can be powerful confounders.

Over the past few months, I have been collecting AI cheat sheets. From time to time I share them with friends and colleagues and recently I have been getting asked a lot, so I decided to organize and share the entire collection. To make things more interesting and give context, I added descriptions and/or excerpts for each major topic.

Even though heat and sound are both forms of energy, when you were a kid, you probably didn't need to be told not to speak in thermal convection. And each time your children come across a stray animal, they likely don't have to self-consciously rehearse a subroutine of zoological attributes to decide whether it's a cat or a dog. Human beings come pre-loaded with the cognitive gear to simply perceive these distinctions. The differences appear so obvious, and knowing the differences comes so naturally to us, that we refer to it as common sense. Computers, in contrast, need step-by-step handholding--in the form of deterministic algorithms--to render even the most basic of judgments. Despite decades of unbroken gains in speed and processing capacity, machines can't do what the average toddler does without even trying.

The University of Toronto has form when it comes to using machine learning in drug discovery. In a recent paper, they describe the use of machine learning to generate 3D structures of protein molecules to assist with drug development. They're putting such thoughts into practice via a spin-out company called Deep Genomics, which was founded by Professor Brendan Frey. The company uses deep learning to trawl through genetic data to try and identify the genes responsible for specific diseases so that medicines can be created. To date, the company has primarily focused on exploring the genome for mutations, which whilst difficult to detect can play a key role in particular diseases.