THIRUVANANTHAPURAM: The Keralite who offered his expert advice and assistance during the Nipah days, made it to the list of top 100 Artificial Intelligence Experts in the world in the Drug Discovery and Advanced Healthcare report compiled and published by DeepTech Analytics in the United States. The US-based Shameer Khader, Senior Director, Advanced Analytics, Data Science, Bioinformatics, AstraZeneca is a native of Orumanayur village in Thrissur is ranked 87 in the list. The AI experts featured in the list come from Academia, Pharmaceutical sector, AI companies, Tech corporations and other related sectors. "It's a humbling experience to be on the list with the global leaders of AI, drug discovery and healthcare. I am excited to see what this cohort would be able to do for the field over the next decade. I believe the value of technologies lies in its impact to improve human lives. Applying AI techniques to drug discovery and healthcare is a core theme that will ultimately help humanity to live longer with lower global disease burden," said Shameer.

This story starts with your average magazine rack circa March last year. Mindlessly browsing the covers, my eyes glazing over at the endless rows of healthy people, one magazine stood out. There was something uncanny about this model staring out under the headlines. She was adorned in an outrageous silver gown. Her face was almost contorted in a painful show of happiness.

Take a moment to observe and look at your surroundings. Even if you are sitting still on your chair or lying on your bed, your brain is constantly trying to analyze the dynamic world around you. Without your conscious effort your brain is continuously making predictions and acting upon them. After just a brief look at this photo you identified that there are humans and objects in the scene. You immediately identified some of the objects in the scene as wine glasses, plate, table, lights etc.

Giants like Oracle, newcomers like Siebel Systems and innovators like Salesforce towards the dawn of the century gave birth to the most advanced and widely used concept in the world of marketing – Customer Relationship Management. Though the concept of collecting customer data, analyzing it to customize communication with customer was present much before the term CRM was coined, the modern-day CRM solutions go much beyond the traditional task of collecting and analyzing data. In the past four decades, CRM solutions have come a long way where it has given birth to a new concept, Visitor Relationship Management – Visitor Relationship Management, to provide justice to the new and still evolving online business models. According to Statista, the worldwide retail e-commerce sales in 2016 was USD 1.86 trillion and is expected to reach USD 4.5 trillion by 2021. In 2016, an estimated 19 percent of all retail sales in China occurred via internet; while, the same percentage in Japan was 6.7 percent.

We propose a new way of interpreting active learning methods by analyzing the information `lost' upon sampling a random variable. We use some recent analytical developments of these losses to formally prove that facility location methods reduce these losses under mild assumptions, and to derive a new data dependent bound on information losses that can be used to evaluate other active learning methods. We show that this new bound is extremely tight to experiment, and further show that the bound has a decent predictive power for classification accuracy.

We consider the problem of recovering a low-rank matrix from a noisy observed matrix. Previous work has shown that the optimal method for recovery depends crucially on the choice of loss function. We use a family of weighted loss functions, which arise naturally in many settings such as heteroscedastic noise and missing data. Weighted loss functions are challenging to analyze because they are not orthogonally-invariant. We derive optimal spectral denoisers for these weighted loss functions. By combining different weights, we then use these optimal denoisers to construct a new denoiser that exploits heterogeneity in the signal matrix for more accurate recovery with unweighted loss.

Real-time traffic volume inference is key to an intelligent city. It is a challenging task because accurate traffic volumes on the roads can only be measured at certain locations where sensors are installed. Moreover, the traffic evolves over time due to the influences of weather, events, holidays, etc. Existing solutions to the traffic volume inference problem often rely on dense GPS trajectories, which inevitably fail to account for the vehicles which carry no GPS devices or have them turned off. Consequently, the results are biased to taxicabs because they are almost always online for GPS tracking. In this paper, we propose a novel framework for the citywide traffic volume inference using both dense GPS trajectories and incomplete trajectories captured by camera surveillance systems. Our approach employs a high-fidelity traffic simulator and deep reinforcement learning to recover full vehicle movements from the incomplete trajectories. In order to jointly model the recovered trajectories and dense GPS trajectories, we construct spatiotemporal graphs and use multi-view graph embedding to encode the multi-hop correlations between road segments into real-valued vectors. Finally, we infer the citywide traffic volumes by propagating the traffic values of monitored road segments to the unmonitored ones through masked pairwise similarities. Extensive experiments with two big regions in a provincial capital city in China verify the effectiveness of our approach.

Machine learning algorithms have been effectively applied into various real world tasks. However, it is difficult to provide high-quality machine learning solutions to accommodate an unknown distribution of input datasets; this difficulty is called the uncertainty prediction problems. In this paper, a margin-based Pareto deep ensemble pruning (MBPEP) model is proposed. It achieves the high-quality uncertainty estimation with a small value of the prediction interval width (MPIW) and a high confidence of prediction interval coverage probability (PICP) by using deep ensemble networks. In addition to these networks, unique loss functions are proposed, and these functions make the sub-learners available for standard gradient descent learning. Furthermore, the margin criterion fine-tuning-based Pareto pruning method is introduced to optimize the ensembles. Several experiments including predicting uncertainties of classification and regression are conducted to analyze the performance of MBPEP. The experimental results show that MBPEP achieves a small interval width and a low learning error with an optimal number of ensembles. For the real-world problems, MBPEP performs well on input datasets with unknown distributions datasets incomings and improves learning performance on a multi task problem when compared to that of each single model.

Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks. However, typical GANs require fully-observed data during training. In this paper, we present a GAN-based framework for learning from complex, high-dimensional incomplete data. The proposed framework learns a complete data generator along with a mask generator that models the missing data distribution. We further demonstrate how to impute missing data by equipping our framework with an adversarially trained imputer. Generative adversarial networks (GANs) (Goodfellow et al., 2014) provide a powerful modeling framework for learning complex high-dimensional distributions. Unlike likelihood-based methods, GANs are referred to as implicit probabilistic models (Mohamed & Lakshminarayanan, 2016). They represent a probability distribution through a generator that learns to directly produce samples from the desired distribution. The generator is trained adversarially by optimizing a minimax objective together with a discriminator.

We address the problem of predicting procedure names in stripped executables which contain no debug information. Predicting procedure names can dramatically ease the task of reverse engineering, saving precious time and human effort. We present a novel approach that leverages static analysis of binaries with encoder-decoder-based neural networks. The main idea is to use static analysis to obtain enriched representations of API call sites; encode a set of sequences of these call sites; and finally, attend to the encoded sequences while decoding the target name token-by-token. We evaluate our model by predicting procedure names over $60,000$ procedures in $10,000$ stripped executables. Our model achieves $81.70$ precision and $80.12$ recall in predicting procedure names within GNU packages, and $55.48$ precision and $51.31$ recall in a diverse, cross-package, dataset. Comparing to previous approaches, the predictions made by our model are much more accurate and informative.