Time series motifs play an important role in the time series analysis. The motif-based time series clustering is used for the discovery of higher-order patterns or structures in time series data. Inspired by the convolutional neural network (CNN) classifier based on the image representations of time series, motif difference field (MDF) is proposed. Compared to other image representations of time series, MDF is simple and easy to construct. With the Fully Convolution Network (FCN) as the classifier, MDF demonstrates the superior performance on the UCR time series dataset in benchmark with other time series classification methods. It is interesting to find that the triadic time series motifs give the best result in the test. Due to the motif clustering reflected in MDF, the significant motifs are detected with the help of the Gradient-weighted Class Activation Mapping (Grad-CAM). The areas in MDF with high weight in Grad-CAM have a high contribution from the significant motifs with the desired ordinal patterns associated with the signature patterns in time series. However, the signature patterns cannot be identified with the neural network classifiers directly based on the time series.

Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available supply, historical transport data typically represents a biased, or censored, version of the true underlying demand pattern. Without explicitly accounting for this inherent distinction, predictive models of demand would necessarily represent a biased version of true demand, thus less effectively predicting the needs of service users. To counter this problem, we propose a general method for censorship-aware demand modeling, for which we devise a censored likelihood function. We apply this method to the task of shared mobility demand prediction by incorporating the censored likelihood within a Gaussian Process model, which can flexibly approximate arbitrary functional forms. Experiments on artificial and real-world datasets show how taking into account the limiting effect of supply on demand is essential in the process of obtaining an unbiased predictive model of user demand behavior.

GSNR of a parameter is defined as the ratio between its gradient's squared mean and Previous work (Zhang et al., 2016; Hardt et al., 2015; Dziugaite & Roy, 2017) suggests that the The GSNR of a parameter is defined as the ratio between its gradient's squared mean and variance Previous work tried to use GSNR to conduct theoretical analysis on deep learning. For example, Rainforth et al. (2018) used GSNR to analyze variational bounds in Intuitively, GSNR measures the similarity of a parameter's gradients among different training samples. To reveal the mechanism of DNNs' good generalization ability, we show that the gradient descent We believe this is probably the key to DNNs' remarkable generalization ability. In the remainder of this paper we first analyze the relation between GSNR and generalization (Section 2). At a particular point of the parameter space, GSNR measures the consistency of a parameter's gradients across different data samples.

Artificial intelligence comes with great opportunities and but also great risks. We investigate to what extent deep learning can be used to create and detect deceptive explanations that either aim to lure a human into believing a decision that is not truthful to the model or provide reasoning that is non-faithful to the decision. Our theoretical insights show some limits of deception and detection in the absence of domain knowledge. For empirical evaluation, we focus on text classification. To create deceptive explanations, we alter explanations originating from GradCAM, a state-of-art technique for creating explanations in neural networks. We evaluate the effectiveness of deceptive explanations on 200 participants. Our findings indicate that deceptive explanations can indeed fool humans. Our classifier can detect even seemingly minor attempts of deception with accuracy that exceeds 80% given sufficient domain knowledge encoded in the form of training data. 1 Introduction Because of the limited moderation of online content, attempts at deception proliferate. Online media struggle against the plague of "fake news", and e-commerce sites spend considerable effort in detecting deceptive product reviews.

Business processes underpin a large number of enterprise operations including processing loan applications, managing invoices, and insurance claims. There is a large opportunity for infusing AI to reduce cost or provide better customer experience, and the business process management (BPM) literature is rich in machine learning solutions including unsupervised learning to gain insights on clusters of process traces, classification models to predict the outcomes, duration, or paths of partial process traces, extracting business process from documents, and models to recommend how to optimize a business process or navigate decision points. More recently, deep learning models including those from the NLP domain have been applied to process predictions. Unfortunately, very little of these innovations have been applied and adopted by enterprise companies. We assert that a large reason for the lack of adoption of AI models in BPM is that business users are risk-averse and do not implicitly trust AI models. There has, unfortunately, been little attention paid to explaining model predictions to business users with process context. We challenge the BPM community to build on the AI interpretability literature, and the AI Trust community to understand

The reconstruction of a high resolution image given a low resolution observation is an ill-posed inverse problem in imaging. Deep learning methods rely on training data to learn an end-to-end mapping from a low-resolution input to a high-resolution output. Unlike existing deep multimodal models that do not incorporate domain knowledge about the problem, we propose a multimodal deep learning design that incorporates sparse priors and allows the effective integration of information from another image modality into the network architecture. Our solution relies on a novel deep unfolding operator, performing steps similar to an iterative algorithm for convolutional sparse coding with side information; therefore, the proposed neural network is interpretable by design. The deep unfolding architecture is used as a core component of a multimodal framework for guided image super-resolution. An alternative multimodal design is investigated by employing residual learning to improve the training efficiency. The presented multimodal approach is applied to super-resolution of near-infrared and multi-spectral images as well as depth upsampling using RGB images as side information. Experimental results show that our model outperforms state-of-the-art methods.

Artificial Intelligence is here to stay. The development of AI is speeding up on a daily basis. Only recently, Google's DeepMind created the AI AlphaStar that secured a decisive victory against two grandmaster players of the game of StarCraft II. In a series of test matches they played, the algorithm won 5-0. This victory is a decisive moment for artificial intelligence, as the game of StarCraft II is fundamentally more difficult than the other games where Deepmind's algorithm already claimed victory.

Taiwan ranks highly in mathematical proficiency, and with the heavy promotion of AI by the government and the industry over the years, the country has cultivated many AI talents. It is because of this that Mike Wang decided to return to Taiwan and set up a company. However, he believes that most of the AI applications in Taiwan are currently limited to a few areas like finance and shopping. Realistically, maximum synergy can only be achieved when AI is combined with hardware. At present, there is an enormous startup manufacturing industry chain in Taiwan.

January 17, 2020 Written by: John R. Smith IBM Research has a long history as a leader in the field of Artificial Intelligence (AI). IBM's pioneering work in AI dates back to the field's inception in the 1950s, when IBM developed one of the first instances of machine learning, which was applied to the game of checkers. Since then, IBM has been responsible for achieving major milestones in AI, ranging from Deep Blue – the first chess-playing computer to defeat a reigning world champion, to Watson – the first natural language question and answering system able to win at Jeopardy!, to last year's Project Debater – the first AI system that can build persuasive arguments on its own and effectively engage in debates on complex topics. IBM's leadership in AI continued in earnest in 2019, which was notable for a growing focus on critical topics such as making trustworthy AI work in practice, creating new AI engineering paradigms to scale AI for a broader use, and continuing to advance core AI capabilities in language, speech, vision, knowledge & reasoning, human-centered AI, and more. While recent years have seen incredible progress in "narrow AI," built on technologies like deep learning, IBM Research pushed its AI research in 2019 towards developing a new foundational underpinning of AI for enterprise applications by addressing important problems like learning more from less, enabling trusted AI by ensuring the fairness, explainability, adversarial robustness, and transparency of AI systems, and integrating learning and reasoning as a way to understand more in order to do more.

Customer Analytics in Python 2020 Get udemy course coupon code Customer Analytics in Python – the place where marketing and data science meet! What will you learn in this course? We will introduce you to the relevant theory that you need to start performing customer analytics. Then we will perform cluster analysis and dimensionality reduction to help you segment your customers. What you'll learn Master beginner and advanced customer analytics Learn the most important type of analysis applied by mid and large companies Gain access to a professional team of trainers with exceptional quant skills Wow interviewers by acquiring a highly desired skill Understand the fundamental marketing modeling theory: segmentation, targeting, positioning, marketing mix, and price elasticity; Apply segmentation on your customers, starting from raw data and reaching final customer segments; Perform K-means clustering with a customer analytics focus; Apply Principal Components Analysis (PCA) on your data to preprocess your features; Combine PCA and K-means for even more professional customer segmentation; Deploy your models on a different dataset; Learn how to model purchase incidence through probability of purchase elasticity; Model brand choice by exploring own-price and cross-price elasticity; Complete the purchasing cycle by predicting purchase quantity elasticity Carry out a black box deep learning model with TensorFlow 2.0 to predict purchasing behavior with unparalleled accuracy Be able to optimize your neural networks to enhance results Description Data science and Marketing are two of the key driving forces that help companies create value and stay on top in today's fast-paced economy.