Competitive methods for multi-label classification typically invest in learning labels together. To do so in a beneficial way, analysis of label dependence is often seen as a fundamental step, separate and prior to constructing a classifier. Some methods invest up to hundreds of times more computational effort in building dependency models, than training the final classifier itself. We extend some recent discussion in the literature and provide a deeper analysis, namely, developing the view that label dependence is often introduced by an inadequate base classifier, rather than being inherent to the data or underlying concept; showing how even an exhaustive analysis of label dependence may not lead to an optimal classification structure. Viewing labels as additional features (a transformation of the input), we create neural-network inspired novel methods that remove the emphasis of a prior dependency structure. Our methods have an important advantage particular to multi-label data: they leverage labels to create effective units in middle layers, rather than learning these units from scratch in an unsupervised fashion with gradient-based methods. Results are promising. The methods we propose perform competitively, and also have very important qualities of scalability.

We consider the estimation of large covariance and precision matrices from high-dimensional sub-Gaussian or heavier-tailed observations with slowly decaying temporal dependence. The temporal dependence is allowed to be long-range so with longer memory than those considered in the current literature. We show that several commonly used methods for independent observations can be applied to the temporally dependent data. In particular, the rates of convergence are obtained for the generalized thresholding estimation of covariance and correlation matrices, and for the constrained $\ell_1$ minimization and the $\ell_1$ penalized likelihood estimation of precision matrix. Properties of sparsistency and sign-consistency are also established. A gap-block cross-validation method is proposed for the tuning parameter selection, which performs well in simulations. As a motivating example, we study the brain functional connectivity using resting-state fMRI time series data with long-range temporal dependence.

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.

In this paper we investigate the family of functions representable by deep neural networks (DNN) with rectified linear units (ReLU). We give the first-ever polynomial time (in the size of data) algorithm to train to global optimality a ReLU DNN with one hidden layer, assuming the input dimension and number of nodes of the network as fixed constants. We also improve on the known lower bounds on size (from exponential to super exponential) for approximating a ReLU deep net function by a shallower ReLU net. Our gap theorems hold for smoothly parametrized families of "hard" functions, contrary to countable, discrete families known in the literature. An example consequence of our gap theorems is the following: for every natural number $k$ there exists a function representable by a ReLU DNN with $k^2$ hidden layers and total size $k^3$, such that any ReLU DNN with at most $k$ hidden layers will require at least $\frac{1}{2}k^{k+1}-1$ total nodes. Finally, we construct a family of $\mathbb{R}^n\to \mathbb{R}$ piecewise linear functions for $n\geq 2$ (also smoothly parameterized), whose number of affine pieces scales exponentially with the dimension $n$ at any fixed size and depth. To the best of our knowledge, such a construction with exponential dependence on $n$ has not been achieved by previous families of "hard" functions in the neural nets literature. This construction utilizes the theory of zonotopes from polyhedral theory.

Recent years have witnessed amazing progress in AI related fields such as computer vision, machine learning and autonomous vehicles. As with any rapidly growing field, however, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several topic specific survey papers have been written, to date no general survey on problems, datasets and methods in computer vision for autonomous vehicles exists. This paper attempts to narrow this gap by providing a state-of-the-art survey on this topic. Our survey includes both the historically most relevant literature as well as the current state-of-the-art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding and end-to-end learning. A lengthy, thorough overview, and probably the best starting place for anyone looking to get up to speed in the field quickly, and in one spot.

We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional features over frequentist competitors such as accurate predictive uncertainty estimates and automatic hyperparameter search.

An update from the Wild Wild West of fake news technologies: A team of computer scientists have figured out how to make words come out of the mouth of former President Barack Obama -- on video -- by using artificial intelligence. Soon we may see a wellspring of fake news videos. As a team out of the University of Washington explains in a new paper titled "Synthesizing Obama: Learning Lip Sync from Audio," they've made several fake videos of Obama. The researchers accomplished this feat not by cutting and pasting his body into different scenes -- but by having a computer system called a neural network study hours and hours of video footage, to see how Obama's mouth moves.

Proactive guidance and tailored insights are becoming a differentiator in financial services. Artificial intelligence (AI), machine learning and eventually the Internet of Things will all play a role in improving a consumer's financial health and well-being. At the same time, as many as three in five consumers say banks are failing to keep up with their need. To win in banking, financial institutions must take steps to serve these customers by helping them improve their financial well being. "Cognitive" Banking – a combination of Artificial Intelligence (AI), predictive analytics and conversational capabilities tailored to financial institutions – provides a huge opportunity for banks to improve their relationships with customers and support them in leading healthy financial lifestyles.

Ikea, the major furniture retailer, has been dabbling in augmented and virtual reality. Recently the company launched a survey to gauge how their consumers really feel about artificial intelligence. This indicated that Ikea is looking to AI and virtual assistant capabilities, to improve customer experience. The survey, called "Do You Speak Human?," was created by Ikea's Space10 innovation and design lab. Questions included asking respondents whether consumers want AI to be human-like, male, female or gender-neutral, and even if it should be religious, among other questions.

Samsung's digital assistant Bixby might finally find its voice next week after previous reports suggested it was'struggling to comprehend English'. When the Samsung Galaxy S8 was released in April, users were disappointed to find that their device was missing Bixby, the firm's highly-anticipated digital assistant. But now that might be about to change as a Reddit user posted a screenshot of an email saying it would launch on July 18. When the Samsung Galaxy S8 was released in April, users were disappointed to find that their device was missing Bixby, the firm's highly-anticipated digital assistant According to Samsung, Bixby is'a completely new way to use your Galaxy S8 or S8 .' Users can use voice, text, or touch to say what they need, since it understands all three. Alternatively, users can take pictures on their camera, and Bixby will search for objects within the picture.