Sentences with phrase «deep learning»

This research is and be will part of a broader Frontiers Research Topic collection of articles on Deep Learning and Digital Humanities.

A copy of the paper «Scalable and Sustainable Deep Learning via Randomized Hashing» is available at: https://arxiv.org/abs/1602.08194

This article appeared in print under the headline «Deep learning smashes pro players at their own video game»

«Scientists slash computations for deep learning: «Hashing» can eliminate more than 95 percent of computations.»

«Deep learning used to reconstruct holograms, improve optical microscopy: Ability to produce more accurate images more quickly could aid diagnostic medicine.»

«But it's not as hard as the commercial applications for deep learning», such as language translation and image identification.

Melee using deep learning algorithms, and pitched it against 10 highly ranked players.

«Artificial neural networks could power up curation of natural history collections: Deep learning techniques manage to differentiate between similar plant families with up to 99 percent accuracy.»

Their study is among the first to describe the use of deep learning methods to enhance our understanding of digitized collection samples.

Richards says future research should model different brain cells and examine how they could interact together to achieve deep learning.

Digitized collections combined with deep learning will help us to automate an otherwise human task of identifying an unknown number of stained specimen sheets across a collection of over 5 million.

In the early 2000s, Richards and Lillicrap took a course with Hinton at the University of Toronto and were convinced deep learning models were capturing «something real» about how human brains work.

Firstly, it wasn't clear that deep learning could achieve human - level skill.

In a study published December 5th in eLife, CIFAR Fellow Blake Richards and his colleagues unveiled an algorithm that simulates how deep learning could work in our brains.

This is an illustration of a multi-compartment neural network model for deep learning.

Furthermore, it represents a more biologically realistic way of how real brains could do deep learning.

The trained neural nets performed with 90 % and 96 % accuracy respectively (or 94 % and 99 % if the most challenging specimens were discarded), confirming that deep learning is a useful and important technology for the future analysis of digitized museum collections.

Deep learning has brought about machines that can «see» the world more like humans can, and recognize language.

A team of researchers from the Smithsonian Department of Botany, Data Science Lab, and Digitization Program Office recently collaborated with NVIDIA to carry out a pilot project using deep learning approaches to dig into digitized herbarium specimens.

But roboticists worry that deep learning can't give machines the other visual abilities needed to make sense of the world — they need to understand the 3D nature of the objects and learn new ones quickly on the fly — so researchers are already looking beyond deep learning for the next big advance.

FacialNetwork, a U.S. company, is using its own deep learning system to develop an app called NameTag that identifies faces with a smart phone or a wearable device like Google Glass.

The training of a deep learning system begins by letting the system compare faces and discover features on its own: eyes and noses, for instance, as well as statistical features that make no intuitive sense to humans.

Thanks to an approach called deep learning, computers are gaining ground fast.

«This is actually a pretty big leap from what has been done with deep learning and animation.

Next, the team plans to integrate this work with deep learning methods to improve their ability to identify click types in new datasets recorded different regions.

This is the «deep» in deep learning: The input for each processing layer is the output of the layer beneath.

The researchers used a machine - learning technique known as deep learning to analyze letter and word patterns used in millions of existing Yelp reviews.

Like all machine learning techniques, deep learning begins with a set of training data — in this case, massive data sets of labeled faces, ideally including multiple photos of each person.

Whereas deep learning can identify a dog and even classify its breed from an image it has never seen before, it does not know the person should be walking the dog instead of the dog walking the person.

Today the big push is in «deep learning» — building artificial intelligence algorithms inspired by the brain's neural connections.

The algorithms need intensive training, the deep learning that takes advantage of computational speed and pattern - matching.

Now, Facebook has developed a deep learning system called Caffe2Go that is condensed enough to run directly in mobile apps on iOS and Android.

Deng, however, knew Hinton and decided to give his «deep learning» method a try in 2009, quickly seeing its potential.

At Gritstone Oncology, researchers pair deep learning with multi-omic profiling to characterize a patient's cancer and identify unique antigens that aren't expressed by healthy cells.

DEEP LEARNING How a neural network with multiple layers becomes sensitive to progressively more abstract patterns.

«Deep Learning predicts hematopoietic stem cell development.»

So - called Deep Learning is the key.

Since then, GPUs, which excel at carrying out hundreds of calculations simultaneously, have become the go - to processor for deep learning.

Now, researchers are eager to apply this computational technique — commonly referred to as deep learning — to some of science's most persistent mysteries.

«Scaling deep learning for science: Algorithm leverages Titan to create high - performing deep neural networks.»

To expand the benefits of deep learning for science, researchers need new tools to build high - performing neural networks that don't require specialized knowledge.

«I think we'll learn really interesting things about how deep learning works, and we'll also have better networks to do our physics.

Having recently been awarded another allocation under the Advanced Scientific Computing Research Leadership Computing Challenge program, Perdue's team is building off its deep learning success by applying MENDDL to additional high - energy physics datasets to generate optimized algorithms.

«One thing we're looking at going forward is evolving deep learning networks from stacked layers to graphs of layers that can split and then merge later,» Young said.

With the OLCF's next leadership - class system, Summit, set to come online in 2018, deep learning researchers expect to take this blossoming technology even further.

The research team's algorithm, called MENNDL (Multinode Evolutionary Neural Networks for Deep Learning), is designed to evaluate, evolve, and optimize neural networks for unique datasets.

Autonomous driving, automatic speech recognition, and the game Go: Deep Learning is generating more and more public awareness.

Meanwhile in bottom - up methods, such as «deep learning,» abstract concepts are derived by looking for patterns in concrete data.

Previous research using the combination of EEGs and deep learning, was on sleep analysis, responds to music or early detection of brain diseases.

AI is all around us — think: Siri, the iPhone - based personal assistant, or Watson, IBM's supercomputer that famously beat human contestants on Jeopardy! Both are examples of «deep learning» in which a computer absorbs and processes information via artificial neural networks that operate like the human brain.