We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine both energy and timing data that are collected in the MINERvA detector to perform classification and regression tasks. We show that the resulting network achieves higher accuracy than previous results while requiring a smaller model size and less training time. In particular, the proposed model outperforms the state-of-the-art by 4.00% on classification accuracy. For the regression task, our model achieves 0.9919 on the coefficient of determination, higher than the previous work (0.96).

Interactive Machine Learning is concerned with creating systems that operate in environments alongside humans to achieve a task. A typical use is to extend or amplify the capabilities of a human in cognitive or physical ways, requiring the machine to adapt to the users' intentions and preferences. Often, this takes the form of a human operator providing some type of feedback to the user, which can be explicit feedback, implicit feedback, or a combination of both. Explicit feedback, such as through a mouse click, carries a high cognitive load. The focus of this study is to extend the current state of the art in interactive machine learning by demonstrating that agents can learn a human user's behavior and adapt to preferences with a reduced amount of explicit human feedback in a mixed feedback setting. The learning agent perceives a value of its own behavior from hand gestures given via a spatial interface. This feedback mechanism is termed Spatial Interface Valuing. This method is evaluated experimentally in a simulated environment for a grasping task using a robotic arm with variable grip settings. Preliminary results indicate that learning agents using spatial interface valuing can learn a value function mapping spatial gestures to expected future rewards much more quickly as compared to those same agents just receiving explicit feedback, demonstrating that an agent perceiving feedback from a human user via a spatial interface can serve as an effective complement to existing approaches.

Neural networks have been used to turn words that a human has heard into intelligible, recognizable speech. It could be a step toward technology that can one day decode people's thoughts. A challenge: Thanks to fMRI scanning, we've known for decades that when people speak, or hear others, it activates specific parts of their brain. However, it's proved hugely challenging to translate thoughts into words. A team from Columbia University has developed a system that combines deep learning with a speech synthesizer to do just that.

SAS Visual Data Mining and Machine Learning, which runs on the SAS Viya engine, includes the latest statistical, machine learning, deep learning and text analysis algorithms that accelerate structured and unstructured data explorations, while also supporting popular open source languages. SAS Enterprise Miner helps identify relationships and patterns buried in a company's data. It streamlines the data mining process to create accurate predictive and descriptive analytical models to find the best fit, no matter the size of the data set.

This GitHub repository may be a bit out of date, having not been updated at all in the past 5 months, but given its wealth of quality links to other deep learning repositories I thought it was relevant enough to point out. Trending Deep Learning is a collection of, well, trending deep learning GitHub repos "sorted by the number of stars gained on a specific day." Mahmoud Badry maintians the collection (or did), and also prepared the companion collection repo Top Deep Learning (note the swapping of "trending" for "top"). Here's a list of top 100 deep learning Github trending repositories sorted by the number of stars gained on a specific day. Repositories with 50000 stars or more are excluded.

At the end of 2018, Dario "TLO" Wünsch, a well-known professional gamer from Germany, was ranked 42nd in the world in the video game StarCraft II. He'd lost some--especially as he battled debilitating carpal tunnel syndrome--but he'd won enough to still be considered among the world's best players. But last week, as he sat before his screen executing the unorthodox moves that have become his signature, he watched helplessly as his opponent slaughtered his armies and laid waste to his StarCraft II kingdom. There was no fist-pumping excitement coming from TLO's opponent. The German gamer lost to an artificial intelligence agent created by DeepMind Technologies as part of its mission to push the boundaries of AI. The company, which is measuring its progress by testing its algorithms' ability to play StarCraft II, is celebrating a major milestone: the introduction last week of AlphaStar, its StarCraft II player.

That's five classes of jobs that will exist in the future that appears to be safe. On the other hand, with the exception of the "human safety valve", all these other jobs require high level skills. Jobs of the future need to have an deep understanding of humans as well as machines, and it is in this interaction of man with machine where jobs will exist. I think what few seem to appreciate is that Deep Learning AI is technology that is like human intuition. It is an opposite technology from more classical AI technologies that focused on reasoning. At this time there remains a Semantic Gap. However, humans capabilities are stuck between a rock and a hard place.

Did humans have prehistoric ancestors we didn't know about yet? AI seems to suggest so. Did humans have prehistoric ancestors we didn't know about yet? AI seems to suggest so. An artificial intelligence system has identified a previously unknown human ancestor that roamed the planet tens of thousands of years ago and left a genomic footprint in Asian individuals, scientists say. By combining deep learning algorithms and statistical methods, researchers from the University of Tartu in Estonia, Institute of Evolutionary Biology (IBE), and the Centre for Genomic Regulation (CRG) in Spain, found that the extinct species was a hybrid of Neanderthals and Denisovans and cross bred with modern humans in Asia.

In the explosive first episode of Shane Dawson's new'Conspiracy Series', Shane takes a look at the bizarre world of'deepfakes' - but what are they? The term "deepfake" is a combination of "deep learning" and "fake". "Deep learning" is a technical term which refers to the way computer programs learn. The technology - which is rapidly advancing, uses the power of AI to digitally paint one person's face over another. The results can be so realistic that it can be difficult to differentiate between what is real and what is artificial.

SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products. SOD includes a comprehensive set of both classic and state-of-the-art deep-neural networks with their pre-trained models. Cross platform, dependency free, amalgamated (single C file) and heavily optimized. The documentation works both as an API reference and a programming tutorial.