TechSee, the leaders in intelligent visual engagement, are pleased to present a new eBook. It explores the computer vision advantage in insurance, especially in terms of claims processing. Artificial intelligence is already deeply embedded in insurance processes. A 2017 study reports that the insurance industry has invested $124 million in AI, compared to an average of $70 million invested by other industries. According to McKinsey's Insurance 2030 Report, the new wave of deep learning techniques will help the insurance sector shift from its current state of "detect and repair" to "predict and prevent."

Although deep neural networks have achieved revolutionary success over various tasks, i.e., computer vision [He et al., 2016] and natural language understanding [Hochreiter and Schmidhuber, 1997], they are still in lack of a rigorous theoretical study of the optimization and generalization properties. Specifically for the optimization, because the loss of deep neural network is highly nonconvex, local search algorithms like gradient descent is hard to analyze with performance guarantee. Many recent works [Choromanska et al., 2015, Kawaguchi, 2016, Nguyen and Hein, 2017, Soudry and Hoffer, 2017] have studied the loss surface of the neural networks and a common claim is that (deep) neural networks have H. Zhang, W. Chen and TY Liu are with Microsoft Research Asia, Beijing, 100080 China (email: {huzhang, wche, tyliu}@microsoft.com); D. Yu is with School of Data and Computer Science at Sun Yat-sen University, Guangzhou, 510275, China (email: yuda3@mail2.sysu.edu.cn).

Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multiple biomarkers jointly, and need an alignment of subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which hinders the application of standard RNNs and requires a pre-processing step such as imputation of the missing values. Instead, we propose a generalized training rule for the most widely used RNN architecture, long short-term memory (LSTM) networks, that can handle both missing predictor and target values. The proposed LSTM algorithm is applied to model the progression of Alzheimer's disease (AD) using six volumetric magnetic resonance imaging (MRI) biomarkers, i.e., volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is compared to standard LSTM networks with data imputation and a parametric, regression-based DPM method. The results show that the proposed algorithm achieves a significantly lower mean absolute error (MAE) than the alternatives with p < 0.05 using Wilcoxon signed rank test in predicting values of almost all of the MRI biomarkers. Moreover, a linear discriminant analysis (LDA) classifier applied to the predicted biomarker values produces a significantly larger AUC of 0.90 vs. at most 0.84 with p < 0.001 using McNemar's test for clinical diagnosis of AD. Inspection of MAE curves as a function of the amount of missing data reveals that the proposed LSTM algorithm achieves the best performance up until more than 74% missing values. Finally, it is illustrated how the method can successfully be applied to data with varying time intervals.

Most of the existing methods for anomaly detection use only positive data to learn the data distribution, thus they usually need a pre-defined threshold at the detection stage to determine whether a test instance is an outlier. Unfortunately, a good threshold is vital for the performance and it is really hard to find an optimal one. In this paper, we take the discriminative information implied in unlabeled data into consideration and propose a new method for anomaly detection that can learn the labels of unlabelled data directly. Our proposed method has an end-to-end architecture with one encoder and two decoders that are trained to model inliers and outliers' data distributions in a competitive way. This architecture works in a discriminative manner without suffering from overfitting, and the training algorithm of our model is adopted from SGD, thus it is efficient and scalable even for large-scale datasets. Empirical studies on 7 datasets including KDD99, MNIST, Caltech-256, and ImageNet etc. show that our model outperforms the state-of-the-art methods.

Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. In order to better approximate this process, we train a convolutional neural network to complete partial musical scores, and explore the use of blocked Gibbs sampling as an analogue to rewriting. Neither the model nor the generative procedure are tied to a particular causal direction of composition. Our model is an instance of orderless NADE (Uria et al., 2014), which allows more direct ancestral sampling. However, we find that Gibbs sampling greatly improves sample quality, which we demonstrate to be due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from Yao et al. (2014) yields better samples than ancestral sampling, based on both log-likelihood and human evaluation.

High-dimensional data in many machine learning applications leads to computational and analytical complexities. Feature selection provides an effective way for solving these problems by removing irrelevant and redundant features, thus reducing model complexity and improving accuracy and generalization capability of the model. In this paper, we present a novel teacher-student feature selection (TSFS) method in which a 'teacher' (a deep neural network or a complicated dimension reduction method) is first employed to learn the best representation of data in low dimension. Then a 'student' network (a simple neural network) is used to perform feature selection by minimizing the reconstruction error of low dimensional representation. Although the teacher-student scheme is not new, to the best of our knowledge, it is the first time that this scheme is employed for feature selection. The proposed TSFS can be used for both supervised and unsupervised feature selection. This method is evaluated on different datasets and is compared with state-of-the-art existing feature selection methods. The results show that TSFS performs better in terms of classification and clustering accuracies and reconstruction error. Moreover, experimental evaluations demonstrate a low degree of sensitivity to parameter selection in the proposed method.

StellarGraph is a Python 3 library. The StellarGraph library implements several state-of-the-art algorithms for applying machine learning methods to discover patterns and answer questions using graph-structured data. Added GraphConvolution layer, GCN class for a stack of GraphConvolution layers, and FullBatchNodeGenerator class for feeding data into GCN (from version 0.5.0) We provide examples of using StellarGraph to solve such tasks using several real-world datasets.

"Deep Learning Is Setting Records!!" There is tremendous growth in people searching or showing interests about deep learning & AI in last few years. Every day hundreds of new articles get published on it in social media & press media. Above chart broadly explains as why search trend is ever growing for deep learning & AI. Fundamentally deep learning is a subset of Machine Learning. The reason as why it is exciting is that more data you give to deep learning usually you get more accuracy out from the model.

The algorithm, GPT-2, was trained on some 8 million web pages, according to the new research. Given a prompt, GPT-2 is tasked with predicting the next word based how those words have been used on the websites it read. In the end, the algorithm churns out passages of text that are far more coherent than past attempts to build AI with contextual knowledge of language.

Every March US chip giant NVIDIA hosts its GPU Technology Conference in Santa Clara, rolling out new chip designs and products while showcasing its latest tech and AI breakthroughs. GTC 2019 runs next Monday through Thursday (March 18–21), and while we can only speculate what surprises NVIDIA CEO Jensen Huang might have in store for us, we can get some sense of where the company is headed by looking at what it's been up to for the last 12 months. NVIDIA is steadily expanding its foothold in artificial intelligence and in 2018 contributed numerous noteworthy research results to the machine learning community, including StyleGAN, video-to-video translation, WaveGlow, and more. The company's AI-related efforts make sense, as NVIDIA is a major supplier of graphics cards to AI researchers and developers. Because GPUs remain the dominant option for training machine learning models, NVIDIA sales will obviously benefit from AI development and deployment.