Object-oriented representations in reinforcement learning have shown promise in transfer learning, with previous research introducing a propositional object-oriented framework that has provably efficient learning bounds with respect to sample complexity. However, this framework has limitations in terms of the classes of tasks it can efficiently learn. In this paper we introduce a novel deictic object-oriented framework that has provably efficient learning bounds and can solve a broader range of tasks. Additionally, we show that this framework is capable of zero-shot transfer of transition dynamics across tasks and demonstrate this empirically for the Taxi and Sokoban domains.

Artificial intelligence (AI) can generate eerily realistic faces, but what about tribal artwork? That's the question Victor Dibia, a human-computer interaction researcher and Carnegie Mellon graduate, sought to answer with an AI system trained on a dataset of African masks. As Dibia explains in a blog post, the work was inspired by a trip to the 2018 Deep Learning Indaba, an annual machine learning conference held at Stellenbosch University, South Africa, in September. Attendees were provided access to second-generation Tensor Processing Units (TPUs) -- Google-designed chips purpose-built for fast training or inference of AI models -- which Dibia used for training. He tapped Google's TensorFlow machine learning framework to get a generative adversarial network (GAN) -- a two-part neural network consisting of generators that produce samples and discriminators that attempt to distinguish between the generated samples and real-world samples -- up and running on the TPUs.

Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information. A popular approach to KB completion is to infer new relations by combinatory reasoning over the information found along other paths connecting a pair of entities. Given the enormous size of KBs and the exponential number of paths, previous path-based models have considered only the problem of predicting a missing relation given two entities or evaluating the truth of a proposed triple. Additionally, these methods have traditionally used random paths between fixed entity pairs or more recently learned to pick paths between them. We propose a new algorithm MINERVA, which addresses the much more difficult and practical task of answering questions where the relation is known, but only one entity. Since random walks are impractical in a setting with combinatorially many destinations from a start node, we present a neural reinforcement learning approach which learns how to navigate the graph conditioned on the input query to find predictive paths. Empirically, this approach obtains state-of-the-art results on several datasets, significantly outperforming prior methods.

In this tutorial, you will learn how to apply deep learning to perform medical image analysis. Specifically, you will discover how to use the Keras deep learning library to automatically analyze medical images for malaria testing. Such a deep learning medical imaging system can help reduce the 400,000 deaths per year caused by malaria. Today's tutorial was inspired by two sources. They've helped me as I've been studying deep learning. I live in an area of Africa that is prone to disease, especially malaria. I'd like to be able to apply computer vision to help reduce malaria outbreaks. Do you have any tutorials on medical imaging? I would really appreciate it if you wrote one.

Digital transformation is the adoption of advanced technologies and the rise of innovations as companies and individuals reorganise to be mobile- and digital-first, multimodal, and intelligence-driven. It is a catalyst for engendering agility, and has become crucial for organisations to stay competitive, achieve successes, and even survive. A key enabler of this is artificial intelligence,which has been dubbed a'great transformer'. Artificial intelligence (AI) refers to systems that change their behaviours without being explicitly programmed to do so – simply put, they learn. These systems use things like aggregated data, usage analysis, pattern recognition, and predictive analytics to deliver intuitive insights or make choices, improving efficiency and even shifting business models across all sectors.

The Chart of the Week is a weekly Visual Capitalist feature on Fridays. Are you ready to hand your job over to R2D2? A recent study by the Mckinsey Global Institute forecasts up to 800 million workers worldwide could lose their jobs to automation by 2030. Industrial machine operators, administrators, and service workers will be the first to take a hit. Meanwhile, poorer countries with lower investment in tech are less likely to feel the pinch. Today's chart uses data from the Future of Jobs Report 2018 by the World Economic Forum to take a peek at the changes technology will bring over the next four years.

Leading enterprise Robotic Process Automation (RPA) software company, UiPath, issued its top five predictions for 2019 in Europe. The predictions are those that the UiPath automation experts believe will have the biggest impact on enterprises in Europe and beyond over the next 12 months. Acceleration of automation emerges as the dominant trend, along with the integration of artificial intelligence (AI) to provide transformative new cognitive capabilities. "We believe enterprise leaders in Europe and the Middle East will accelerate automation to achieve their digital transformation strategies in 2019," said Kulpreet Singh, MD, EMEA, UiPath. "RPA has one of the fastest rates of implementation in the history of technology and 2019 will see that exponential growth continue, diversify and reach new audiences."

Prematurity is a significant cause of mortality in neonates. Knowledge of an infant's gestational age is critical in post-delivery treatment plans to reduce neonatal deaths. In high-income countries, prenatal ultrasound scans – the ground truth measure – are the gold-standard method to track gestational aging, but in lower-income countries, access to ultrasound technology and medical experts is limited. "If we could accurately estimate gestational age for newborns using simple, portable technology, we would be able to administer a proper post-treatment plan to reduce the risk of mortality in many under-serviced regions," says Arjun Desai from Duke University, first-author on a new study into an automatic system for gestational aging. The cross-disciplinary team, led by Sina Farsiu, has developed a system based on the previously reported inverse correlation between blood vessel density in the anterior lens capsule region, and gestational age.

Hierarchical text classification, which aims to classify text documents into a given hierarchy, is an important task in many real-world applications. Recently, deep neural models are gaining increasing popularity for text classification due to their expressive power and minimum requirement for feature engineering. However, applying deep neural networks for hierarchical text classification remains challenging, because they heavily rely on a large amount of training data and meanwhile cannot easily determine appropriate levels of documents in the hierarchical setting. In this paper, we propose a weakly-supervised neural method for hierarchical text classification. Our method does not require a large amount of training data but requires only easy-to-provide weak supervision signals such as a few class-related documents or keywords. Our method effectively leverages such weak supervision signals to generate pseudo documents for model pre-training, and then performs self-training on real unlabeled data to iteratively refine the model. During the training process, our model features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism. Experiments on three datasets from different domains demonstrate the efficacy of our method compared with a comprehensive set of baselines.

Nonnegative CANDECOMP/PARAFAC (NCP) decomposition is an important tool to process nonnegative tensor. Sometimes, additional sparse regularization is needed to extract meaningful nonnegative and sparse components. Thus, an optimization method for NCP that can impose sparsity efficiently is required. In this paper, we construct NCP with sparse regularization (sparse NCP) by l1-norm. Several popular optimization methods in block coordinate descent framework are employed to solve the sparse NCP, all of which are deeply analyzed with mathematical solutions. We compare these methods by experiments on synthetic and real tensor data, both of which contain third-order and fourth-order cases. After comparison, the methods that have fast computation and high effectiveness to impose sparsity will be concluded. In addition, we proposed an accelerated method to compute the objective function and relative error of sparse NCP, which has significantly improved the computation of tensor decomposition especially for higher-order tensor.