Customer-obsessed science

Demand forecasting is a prominent business use case that allows retailers to optimize inventory planning, logistics, and core business decisions. One of the key challenges in demand forecasting is accounting for relationships and interactions between articles. Most modern forecasting approaches provide independent article-level predictions that do not consider the impact of related articles. Recent research

Deep learning training compilers accelerate and achieve more resource-efficient training. We present a deep learning compiler for training consisting of three main features, a syncfree optimizer, compiler caching and multi-threaded execution. We demonstrate speedups for common language and vision problems against native and XLA baselines implemented in PyTorch.

Deep neural networks are a powerful tool for a wide range of applications, including natural language processing (NLP) and computer vision (CV). However, training these networks can be a challenging task, as it requires careful selection of hyperparameters such as learning rates and scheduling strategies. Despite significant advances in designing dynamic (and adaptive) learning rate schedulers, choosing

Mixup is a domain-agnostic approach for data augmentation, originally proposed for training Deep Neural Networks (DNNs) for image classification. It obtains additional data for training by sampling from linear interpolations of model inputs and their labels. While proven to be effective for computer vision (CV) and natural language processing (NLP) tasks, it remains unknown if mixup can bring performance

Building operations account for a significant portion of global emissions, contributing approximately 28% of global greenhouse gas emissions, according to the International Energy Agency. With the anticipated increase in cooling demand due to rising global temperatures, the optimization of rooftop units (RTUs) in buildings becomes crucial for reducing energy consumption and associated emissions. We focus