Gaurav Dhama

MasterCard

Entity Alignment (EA) identifies entities across databases that refer to the same entity. Knowledge graph-based embedding methods have recently dominated EA techniques. Such methods map entities to a low-dimension space and align them based on their similarities. With the corpus of EA methodologies growing rapidly, this paper presents a comprehensi...

Planning based on long and short term time series forecasts is a common practice across many industries. In this context, temporal aggregation and reconciliation techniques have been useful in improving forecasts, reducing model uncertainty, and providing a coherent forecast across different time horizons. However, an underlying assumption spanning...

Time series forecasting methods have been widely implemented in various domains across industry and academics. For decision-makers in the forecasting sector, decision processes like planning of facilities, an optimal day-to-day operation within the domain, etc. are complex with several different levels to be considered. These decisions address wide...

In the context of time series forecasting, it is a common practice to evaluate multiple methods and choose one of these methods or an ensemble for producing the best forecasts. However, choosing among different ensembles over multiple methods remains a challenging task that undergoes a combinatorial explosion as the number of methods increases. In...

The large variety of digital payment choices available to consumers today has been a key driver of e-commerce transactions in the past decade. Unfortunately, this has also given rise to cybercriminals and fraudsters who are constantly looking for vulnerabilities in these systems by deploying increasingly sophisticated fraud attacks. A typical fraud...

Recent years of research in Natural Language Processing (NLP) have witnessed dramatic growth in training large models for generating context-aware language representations. In this regard, numerous NLP systems have leveraged the power of neural network-based architectures to incorporate sense information in embeddings, resulting in Contextualized W...

Generative Adversarial Networks (GANs) have seen their use for generating synthetic data expand, from unstructured data like images to structured tabular data. One of the recently proposed models in the field of tabular data generation, CTGAN, demonstrated state-of-the-art performance on this task even in the presence of a high class imbalance in c...

The inception of modeling contextual information using models such as BERT, ELMo, and Flair has significantly improved representation learning for words. It has also given SOTA results in almost every NLP task - Machine Translation, Text Summarization and Named Entity Recognition, to name a few. In this work, in addition to using these dominant con...

Recent years of research in Natural Language Processing (NLP) have witnessed dramatic growth in training large models for generating context-aware language representations. In this regard, numerous NLP systems have leveraged the power of neural network-based architectures to incorporate sense information in embeddings, resulting in Contextualized W...

The pandemics like Coronavirus disease 2019 (COVID-19) require Governments and health professionals to make time-sensitive, critical decisions about travel restrictions and resource allocations. This paper identifies various factors that affect the spread of the disease using transaction data and proposes a model to predict the degree of spread of...

We investigate polarity reversals in the geodynamo using a rotating, convection-driven dynamo model. As the flow in rapidly rotating convection is dominated by columns aligned with the axis of rotation, the focus is on the dynamics of columnar vortices. By studying the growth of a seed magnetic field to a stable axial dipole field, we show that the...