In the banking business, Digital Transformation means much more than just transactions, apps and automation. There's a whole new shift that will make banking a different experience altogether. Pivoted around customers experience, self-help and omni channel enablement, this new wave will define banking of the future. Standard Chartered India's CIO Mr Zuzar Tinwalla in an exclusive interaction with Muqbil Ahmar (DC), Executive Editor, dynamicCIO reveals some of the interesting dimensions of the company's digital journey and how it is reinventing itself to empower millennials. DC: Banking and finance sector is pioneering the adoption of Digital.

Aug 20, 2018 (Heraldkeeper via COMTEX) -- A new research document is added in HTF MI database of 90 pages, titled as'Global Deep Learning Market (2018-2023)' with detailed analysis, Competitive landscape, forecast and strategies. The study covers geographic analysis that includes regions like North America, Europe, Asia-Pacific, Latin America, and the Middle East and Africa and important players/vendors such as Google, Microsoft, IBM, Intel etc. The report will help you gain market insights, future trends and growth prospects for forecast period of 2018-2023. In enterprise computing, deep learning is evolving into one of the most advanced technologies. Deep learning is a subset of machine learning in Artificial Intelligence (AI) that has networks capable of learning unsupervised, from data that is unstructured or unlabeled.

Now a research team from Kyoto University, Osaka University, and the Advanced Telecommunications Research Institute, or ATR, have significantly upgraded the interaction system for conversational android ERICA, giving her even greater dialog skills. ERICA is an android created by Hiroshi Ishiguro of Osaka University and ATR, specifically designed for natural conversation through incorporation of human-like facial expressions and gestures. The research team demonstrated the updates during a symposium at the National Museum of Emerging Science in Tokyo. "When we talk to one another, it's never a simple back and forward progression of information," states Tatsuya Kawahara of Kyoto University's Graduate School of Informatics, and an expert in speech and audio processing. We express agreement by nodding or saying'uh-huh' to maintain the momentum of conversation.

We characterize the statistical properties of network Lasso for semi-supervised regression problems involving network- structured data. This characterization is based on the con- nectivity properties of the empirical graph which encodes the similarities between individual data points. Loosely speaking, network Lasso is accurate if the available label informa- tion is well connected with the boundaries between clusters of the network-structure datasets. We make this property precise using the notion of network flows. In particular, the existence of a sufficiently large network flow over the empirical graph implies a network compatibility condition which, in turn, en- sures accuracy of network Lasso.

State-of-the-art distributed machine learning suffers from significant delays due to frequent communication and synchronizing between worker nodes. Emerging communication-efficient SGD algorithms that limit synchronization between locally trained models have been shown to be effective in speeding-up distributed SGD. However, a rigorous convergence analysis and comparative study of different communication-reduction strategies remains a largely open problem. This paper presents a new framework called Coooperative SGD that subsumes existing communication-efficient SGD algorithms such as federated-averaging, elastic-averaging and decentralized SGD. By analyzing Cooperative SGD, we provide novel convergence guarantees for existing algorithms. Moreover this framework enables us to design new communication-efficient SGD algorithms that strike the best balance between reducing communication overhead and achieving fast error convergence.

Topology optimization is computationally demanding that requires the assembly and solution to a finite element problem for each material distribution hypothesis. As a complementary alternative to the traditional physics-based topology optimization, we explore a data-driven approach that can quickly generate accurate solutions. To this end, we propose a deep learning approach based on a 3D encoder-decoder Convolutional Neural Network architecture for accelerating 3D topology optimization and to determine the optimal computational strategy for its deployment. Analysis of iteration-wise progress of the Solid Isotropic Material with Penalization process is used as a guideline to study how the earlier steps of the conventional topology optimization can be used as input for our approach to predict the final optimized output structure directly from this input. We conduct a comparative study between multiple strategies for training the neural network and assess the effect of using various input combinations for the CNN to finalize the strategy with the highest accuracy in predictions for practical deployment. For the best performing network, we achieved about 40% reduction in overall computation time while also attaining structural accuracies in the order of 96%.

Cubic-regularized Newton's method (CR) is a popular algorithm that guarantees to produce a second-order stationary solution for solving nonconvex optimization problems. However, existing understandings of the convergence rate of CR are conditioned on special types of geometrical properties of the objective function. In this paper, we explore the asymptotic convergence rate of CR by exploiting the ubiquitous Kurdyka-Lojasiewicz (KL) property of nonconvex objective functions. In specific, we characterize the asymptotic convergence rate of various types of optimality measures for CR including function value gap, variable distance gap, gradient norm and least eigenvalue of the Hessian matrix. Our results fully characterize the diverse convergence behaviors of these optimality measures in the full parameter regime of the KL property. Moreover, we show that the obtained asymptotic convergence rates of CR are order-wise faster than those of first-order gradient descent algorithms under the KL property.

The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results in comparison to previous machine learning algorithms. However, finding the suitable structure for these models has been a challenge for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. In short, RMDL trains multiple randomly generated models of Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines their results to produce better result of any of those models individually. In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for image classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text classification. Lastly, we used ORL dataset to compare the model performance on face recognition task.

In statistical learning, high covariate dimensionality poses challenges for robust prediction and inference. To address this challenge, supervised dimension reduction is often performed, where dependence on the outcome is maximized for a selected covariate subspace with smaller dimensionality. Prevalent dimension reduction techniques assume data are $i.i.d.$, which is not appropriate for longitudinal data comprising multiple subjects with repeated measurements over time. In this paper, we derive a decomposition of the Hilbert-Schmidt Independence Criterion as a supervised loss function for longitudinal data, enabling dimension reduction between and within clusters separately, and propose a dimensionality-reduction technique, $sklPCA$, that performs this decomposed dimension reduction. We also show that this technique yields superior model accuracy compared to the model it extends.

In this paper, we study how to make clustering benefiting from differentiable programming whose basic idea is treating the neural network as a language instead of a machine learning method. To this end, we recast the vanilla $k$-means as a novel feedforward neural network in an elegant way. Our contribution is two-fold. On the one hand, the proposed \textit{k}-meansNet is a neural network implementation of the vanilla \textit{k}-means, which enjoys four advantages highly desired, i.e., robustness to initialization, fast inference speed, the capability of handling new coming data, and provable convergence. On the other hand, this work may provide novel insights into differentiable programming. More specifically, most existing differentiable programming works unroll an \textbf{optimizer} as a \textbf{recurrent neural network}, namely, the neural network is employed to solve an existing optimization problem. In contrast, we reformulate the \textbf{objective function} of \textit{k}-means as a \textbf{feedforward neural network}, namely, we employ the neural network to describe a problem. In such a way, we advance the boundary of differentiable programming by treating the neural network as from an alternative optimization approach to the problem formulation. Extensive experimental studies show that our method achieves promising performance comparing with 12 clustering methods on some challenging datasets.