The current wave of generative machine learning models for image synthesis are impressively powerful. The Generative Adversarial Networks (GANs) algorithm in particular has become popular due to resulting in near photo-realistic images. While research into GANs is scientifically and aesthetically intriguing, what is still quite unclear is which real-world tasks are out there where powerful generative models could turn out to be indispensable. Among those mentioned in research discussions one area is often missed, likely because for most of us it sounds like an obscure parascience: Reconstructing what is happening in a human visual system – what someone is seeing or imagining. This is a small, but real neuroscience research area, and you can confidently call it a variant of brain reading.

This is the second installment in a three-part series exploring the impact of artificial intelligence (AI) on investment management. I want to thank the speakers at the AI and the Future of Financial Services Forum, hosted by CFA Institute and CFA Society Beijing, for inspiring this series. The first installment offered a primer on the AI technologies that are relevant to investment professionals. Artificial intelligence (AI) is coming to the investment world. With the help of deep learning techniques, AI researchers have made significant strides in natural language processing (NLP), speech recognition, and image recognition.

We encourage everyone who is interested to apply. Application is open to all, including students, post-doctoral candidates, research staff, industries, startups: to people from across Africa and the world. We are particularly looking for people with a passion for machine learning and who are committed to our mission of strengthening machine learning in Africa. What is the Deep Learning Indaba? The Deep Learning Indaba 2018 will be held over 6 days, from the 9th to the 14th of September at Stellenbosch University in South Africa.

Recurrent Neural Networks are the state of the art algorithm for sequential data and among others used by Apples Siri and Googles Voice Search. This is because it is the first algorithm that remembers its input, due to an internal memory, which makes it perfectly suited for Machine Learning problems that involve sequential data. It is one of the algorithms behind the scenes of the amazing achievements of Deep Learning in the past few years. In this post, you will learn the basic concepts of how Recurrent Neural Networks work, what the biggest issues are and how to solve them. Recurrent Neural Networks (RNN) are a powerful and robust type of neural networks and belong to the most promising algorithms out there at the moment because they are the only ones with an internal memory.

Even dogs have dreams, but not you, you are just a machine. Can a robot write a symphony? Can a robot turn a canvas into a beautiful masterpiece?" This famous quote from the film "I, Robot", inspired by Isaac Asimov, the science fiction writer's collection of short stories, poses questions that current technology can already answer. Computer programs that have machine learning capabilities can compose sonatas, songs, and classical pieces, and can even draw pictures at a level on a par with high art.

There is new code to play around with for those interested in machine learning and space, a model that predicts hilarious search trends for sex site YouPorn, and another funny story about an ostensibly intelligent medical chatbot in New Zealand. Hunting exoplanets with ML – The machine learning code that a Google engineer and an astrophysicist used to detect exoplanets has been published online. Christopher Shallue, a senior software engineer at Google, and Andrew Vanderburg, a postdoctoral fellow studying astrophysics at the University of Texas, USA, discovered another planet lurking in the Kepler-90 system. It was a special find. Not only was it spotted using a convolutional neural network, but it meant that the Solar System was no longer the biggest planetary system found so far.

The craze of deep learning has brought about many challenges to the information status quo. For some use cases, its success makes sense and seems inevitable. For others, like image processing, its bid to outshine hardened algorithms in compression and optimization seemed harder to predict, begging the question of what feats of computer engineering are safe from its grasp. Today, we will only look at the ways machine learning is changing how we store, create, and optimize images, but every corner of information science is seeing similar confrontations by deep learning. Last year, Google released RAISR, an algorithm combining traditional upsampling with deep learning in order to turn low-resolution images into convincing high-resolution counterparts.

Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies. Unfortunately, due to the large number of weights, all the examples in a mini-batch typically share the same weight perturbation, thereby limiting the variance reduction effect of large mini-batches. We introduce flipout, an efficient method for decorrelating the gradients within a mini-batch by implicitly sampling pseudo-independent weight perturbations for each example. Empirically, flipout achieves the ideal linear variance reduction for fully connected networks, convolutional networks, and RNNs. We find significant speedups in training neural networks with multiplicative Gaussian perturbations. We show that flipout is effective at regularizing LSTMs, and outperforms previous methods. Flipout also enables us to vectorize evolution strategies: in our experiments, a single GPU with flipout can handle the same throughput as at least 40 CPU cores using existing methods, equivalent to a factor-of-4 cost reduction on Amazon Web Services.

The move from hand-designed to learned optimizers in machine learning has been quite successful for gradient-based and -free optimizers. When facing a constrained problem, however, maintaining feasibility typically requires a projection step, which might be computationally expensive and not differentiable. We show how the design of projection-free convex optimization algorithms can be cast as a learning problem based on Frank-Wolfe Networks: recurrent networks implementing the Frank-Wolfe algorithm aka. conditional gradients. This allows them to learn to exploit structure when, e.g., optimizing over rank-1 matrices. Our LSTM-learned optimizers outperform hand-designed as well learned but unconstrained ones. We demonstrate this for training support vector machines and softmax classifiers.

This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted crowdsourcing approach, where multiple annotators with unknown expertise contribute an uncontrolled amount (often limited) of annotations. Our framework leverages the low-rank structure in annotations to learn individual annotator expertise, which then helps to infer the true labels from noisy and sparse annotations. It provides a unified Bayesian model to simultaneously infer the true labels and train the deep learning model in order to reach an optimal learning efficacy. Finally, our framework exploits the uncertainty of the deep learning model during prediction as well as the annotators' estimated expertise to minimize the number of required annotations and annotators for optimally training the deep learning model. We evaluate the effectiveness of our framework for intent classification in Alexa (Amazon's personal assistant), using both synthetic and real-world datasets. Experiments show that our framework can accurately learn annotator expertise, infer true labels, and effectively reduce the amount of annotations in model training as compared to state-of-the-art approaches. We further discuss the potential of our proposed framework in bridging machine learning and crowdsourcing towards improved human-in-the-loop systems.