New larger datasets contributed to the resurgence of interest in neural networks, Three years ago, we released the Omniglot dataset for such as the ImageNet dataset for objection recognition developing more humanlike learning algorithms. Omniglot that provides 1,000 classes with about 1,200 examples is a one-shot learning challenge, inspired by how each (Deng et al., 2009; Krizhevsky et al., 2012) and the people can learn a new concept from just one or a few Atari benchmark that typically provides 900 hours of experience examples. Along with the dataset, we proposed a suite playing each game (Bellemare et al., 2013; Mnih of five challenge tasks and a computational model based et al., 2015). These datasets opened important new lines on probabilistic program induction that addresses them. of work, but they offer far more experience than human The computational model, although powerful, was not learners require. People can learn a new concept from just meant to be the final word on Omniglot; we hoped that one or a handful of examples, and then use this concept the machine learning community would both build on for a range of tasks beyond recognition (Figure 1). Similarly, our work and develop novel approaches to tackling the people can learn a new Atari game in minutes rather challenge. In the time since, we have been pleased to than hundreds of hours, and then generalize to game variants see the wide adoption of Omniglot and notable technical beyond those that were trained (Lake et al., 2017).

We present in this work the first end-to-end deep learning based method that predicts both 3D hand shape and pose from RGB images in the wild. Our network consists of the concatenation of a deep convolutional encoder, and a fixed model-based decoder. Given an input image, and optionally 2D joint detections obtained from an independent CNN, the encoder predicts a set of hand and view parameters. The decoder has two components: A pre-computed articulated mesh deformation hand model that generates a 3D mesh from the hand parameters, and a re-projection module controlled by the view parameters that projects the generated hand into the image domain. We show that using the shape and pose prior knowledge encoded in the hand model within a deep learning framework yields state-of-the-art performance in 3D pose prediction from images on standard benchmarks, and produces geometrically valid and plausible 3D reconstructions. Additionally, we show that training with weak supervision in the form of 2D joint annotations on datasets of images in the wild, in conjunction with full supervision in the form of 3D joint annotations on limited available datasets allows for good generalization to 3D shape and pose predictions on images in the wild.

This paper proposes a generative moment matching network (GMMN)-based post-filter that provides inter-utterance pitch variation for deep neural network (DNN)-based singing voice synthesis. The natural pitch variation of a human singing voice leads to a richer musical experience and is used in double-tracking, a recording method in which two performances of the same phrase are recorded and mixed to create a richer, layered sound. However, singing voices synthesized using conventional DNN-based methods never vary because the synthesis process is deterministic and only one waveform is synthesized from one musical score. To address this problem, we use a GMMN to model the variation of the modulation spectrum of the pitch contour of natural singing voices and add a randomized inter-utterance variation to the pitch contour generated by conventional DNN-based singing voice synthesis. Experimental evaluations suggest that 1) our approach can provide perceptible inter-utterance pitch variation while preserving speech quality. We extend our approach to double-tracking, and the evaluation demonstrates that 2) GMMN-based neural double-tracking is perceptually closer to natural double-tracking than conventional signal processing-based artificial double-tracking is.

Check out the "Building Deep Learning Applications with Amazon SageMaker" session at the AI Conference in San Francisco, September 4-7, 2018. Hurry--early price ends July 20. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Anima Anandkumar, a leading machine learning researcher, and currently a principal research scientist at Amazon.

Object Tracking is an important domain in computer vision. It involves the process of tracking an object which could be a person, ball or a car across a series of frames. For people tracking we would start with all possible detections in a frame and give them an ID. In subsequent frames we try to carry forward a person's ID. If the person has moved away from the frame then that ID is dropped.

This article is extended version of the presentation I gave at General AI Challenge kick off meetup organized by GoodAI and MLMU. First of all, I have to explain what a meta-learning is good for. The term meta-learning has many definitions and meanings. As you can see the meta-learning is relevant for the first objective of the challenge which is developing AI with gradual learning capabilities. One of the main direction in meta-learning is having a meta level system utilizing a Knowledge Repository.

Researchers from Columbia University used deep learning to enhance speech neuroprothesis technologies, that can result in accurate and intelligible reconstructed speech from the human auditory cortex. This research has the potential to one day help patients who have lost their ability to speak, communicate with their loved ones. "Our approach takes a step toward the next generation of human-computer interaction systems and more natural communication channels for patients suffering from paralysis and locked-in syndromes," the researchers stated in their paper. The findings were published this week in Scientific Reports this week. "Our voices help connect us to our friends, family and the world around us, which is why losing the power of one's voice due to injury or disease is so devastating," said Nima Mesgarani, the paper's senior author and a principal investigator at Columbia University's Mortimer B. Zuckerman Mind Brain Behavior Institute."With We've shown that, with the right technology, these people's thoughts could be decoded and understood by any listener."

An interesting benefit of deep learning neural networks is that they can be reused on related problems. Transfer learning refers to a technique for predictive modeling on a different but somehow similar problem that can then be reused partly or wholly to accelerate the training and improve the performance of a model on the problem of interest. In deep learning, this means reusing the weights in one or more layers from a pre-trained network model in a new model and either keeping the weights fixed, fine tuning them, or adapting the weights entirely when training the model. In this tutorial, you will discover how to use transfer learning to improve the performance deep learning neural networks in Python with Keras. How to Improve Performance With Transfer Learning for Deep Learning Neural Networks Photo by Damian Gadal, some rights reserved. Transfer learning generally refers to a process where a model trained on one problem is used in some way on a second related problem.

The human brain is a remarkably complex, yet energy-efficient cognitive system. Scientists and researchers look to the brain's architecture as sources of inspiration for artificial intelligence (AI), machine learning, and deep learning. Concepts on artificial neural networks (ANNs) are somewhat analogous to the brain, with artificial nodes instead of neurons. Neuromorphic computing is an interdisciplinary endeavor that draws upon physics, mathematics, electronic engineering, biology, computer science, and neuroscience, in order to create artificial neural systems that resemble the architecture in the brain. A team of scientists from Linköping University in Sweden recently made a breakthrough in neuromorphic computing by engineering a new learning transistor.

ONNX is a open format to represent deep learning models that is supported by various frameworks and tools. This format makes it easier to interoperate between frameworks and to maximize the reach of your hardware optimization investments. In this episode, Seth Juarez sits with Rich to show us how we can use the ONNX runtime inside of our .NET applications. He gives us a quick introduction to training a model with PyTorch, and also explains some foundational concepts around prediction accuracy.