We present an end-to-end approach that takes unstructured textual input and generates structured output compliant with a given vocabulary. Inspired by recent successes in neural machine translation, we treat the triples within a given knowledge graph as an independent graph language and propose an encoder-decoder framework with an attention mechanism that leverages knowledge graph embeddings. Our model learns the mapping from natural language text to triple representation in the form of subject-predicate-object using the selected knowledge graph vocabulary. Experiments on three different data sets show that we achieve competitive F1-Measures over the baselines using our simple yet effective approach. A demo video is included.

Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate to capture the latent structure of vast amounts of complex high-dimensional data. However, existing VAEs can still not directly handle data that are heterogenous (mixed continuous and discrete) or incomplete (with missing data at random), which is indeed common in real-world applications. In this paper, we propose a general framework to design VAEs, suitable for fitting incomplete heterogenous data. The proposed HI-VAE includes likelihood models for real-valued, positive real valued, interval, categorical, ordinal and count data, and allows to estimate (and potentially impute) missing data accurately. Furthermore, HI-VAE presents competitive predictive performance in supervised tasks, outperforming supervised models when trained on incomplete data.

Motivated by applications in Optimization, Game Theory, and the training of Generative Adversarial Networks, the convergence properties of first order methods in min-max problems have received extensive study. It has been recognized that they may cycle, and there is no good understanding of their limit points when they do not. When they converge, do they converge to local min-max solutions? We characterize the limit points of two basic first order methods, namely Gradient Descent/Ascent (GDA) and Optimistic Gradient Descent Ascent (OGDA). We show that both dynamics avoid unstable critical points for almost all initializations. Moreover, for small step sizes and under mild assumptions, the set of \{OGDA\}-stable critical points is a superset of \{GDA\}-stable critical points, which is a superset of local min-max solutions (strict in some cases). The connecting thread is that the behavior of these dynamics can be studied from a dynamical systems perspective.

Football (popularly know as soccer in USA) as a sport has always been the center of attraction and excitement among the sports lovers as well as among common mass all over the world. Although there are few other sports that has gained popularity in different subcontinents here and there in last few decades but none of them have ever dared to challenge the popularity of football anytime in the past or at present. In fact the popularity and attraction for both football and footballers has increased exponentially over the past few decades with the introduction of humongous platforms like "World Cup Football" organized by prestigious association like FIFA and support from various other independent affluent football clubs. Today, it has become the sign of dignity and status symbol for a country to host a mega-event like World Cup Football and take advantage of the tourism and business opportunities associated with it. Behind the scene a country can showcase the strength of it's infrastructure and attract foreign tourists & investors and can create huge business opportunities by hosting such an event.

Teaching Don't learn like a typical human Only what they need to know Consider a reverse card sorting exercise 30 participants How important is it that they all get it right every time? Government safety compliance Accidents related to this tire? Ecommerce chat bot Women's pants with pockets? When carefully (or not so carefully) piled books succumb to gravity Grew up with bookalanches occurring regularly Stepfather is an oncologist – would bring home piles of articles, papers, books and more. He reads everything he can get his hands on.

Addressing the South Australian government's recent Copper to the World conference in Adelaide, Newcrest's chief information and digital officer, Gavin Wood, gave a rundown on what had already been achieved at Newcrest with data science, virtual and augmented reality and artificial intelligence. He also talked about the benefits delivered by crowd sourcing, although this can also create some unique challenges of its own. "If you can imagine, an experienced operator at a site being told by a university student in Argentina the answer for optimising their part of the plant is quite different to something they believe from their experience of 20 or so years. Those are real challenges for our business," Wood said. He said data science coupled with machine learning had alr...

Asking Alexa or her cloud-based buddies to turn on lights, or activate the air conditioning ahead of your arrival home, might seem nifty – for now. As artificial intelligence takes up residence, prepare for a home that doesn't need to be told – it knows intuitively everything about you, anticipating your every move (and mood), and adjusting itself to take care of you. A glimpse of that future was unveiled at technology trade show CES Asia in Shanghai in June, when a new product category – artificial intelligence (AI) – was introduced. John T. Kelley, senior director, international programs and show director, CES Asia, describes artificial intelligence (AI) as "one of those exciting technologies that will become ubiquitous in the next decade as it becomes more deeply embedded in the products that we use day in and day out to make our lives better". AI is already being incorporated in everyday consumer technology products that provide practical benefits, including cars, smart homes, robotics, health and wellness devices and home security, he adds – and in many categories, China is leading the way.

ANZ Bank has turned to machine learning to improve existing forecasting techniques. Economists Jack Chambers and David Plank applied the technique to monthly retail sales data, and compared it to the standard error found in consensus surveys compiled by Bloomberg. The machine learning process used by the pair was called "random forest". Think of a standard decision tree model, which maps out decisions or actions and their possible consequences. It follows that the "forest" is comprised of multiple decision trees, which are calculated and averaged to find correlations with retail sales.

This paper describes how to apply self-attention with relative positional encodings to the task of relation extraction. We propose to use the self-attention encoder layer together with an additional position-aware attention layer that takes into account positions of the query and the object in the sentence. The self-attention encoder also uses a custom implementation of relative positional encodings which allow each word in the sentence to take into account its left and right context. The evaluation of the model is done on the TACRED dataset. The proposed model relies only on attention (no recurrent or convolutional layers are used), while improving performance w.r.t. the previous state of the art.

This paper studies bandit learning problems with delayed feedback, which included multi-armed bandit (MAB) and bandit convex optimization (BCO). Given only function value information (a.k.a. bandit feedback), algorithms for both MAB and BCO typically rely on (possibly randomized) gradient estimators based on function values, and then feed them into well-studied gradient-based algorithms. Different from existing works however, the setting considered here is more challenging, where the bandit feedback is not only delayed but also the presence of its delay is not revealed to the learner. Existing algorithms for delayed MAB and BCO become intractable in this setting. To tackle such challenging settings, DEXP3 and DBGD have been developed for MAB and BCO, respectively. Leveraging a unified analysis framework, it is established that both DEXP3 and DBGD guarantee an ${\cal O}\big( \sqrt{T+D} \big)$ regret over $T$ time slots with $D$ being the overall delay accumulated over slots. The new regret bounds match those in full information settings.