Behrooz Tahmasebi—an MIT Ph.D. student in the Department of Electrical Engineering and Computer Science (EECS) and an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL)—was taking a mathematics course on differential equations in late 2021 when a glimmer of inspiration struck. In that class, he learned for the first time about Weyl’s law, …
Read more “How symmetry can come to the aid of machine learning”
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In the realm of real estate, understanding the intricacies of property features and their impact on sale prices is paramount. In this exploration, we’ll dive deep into the Ames Housing dataset, shedding light on the relationships between various features and their correlation with the sale price. Harnessing the power of data visualization, we’ll unveil patterns, …
Read more “Feature Relationships 101: Lessons from the Ames Housing Data”
An internet-connected light bulb is one of the easiest way to start building a smarter home.
I’m trying to generate images of people using Stable Diffusion, but they always look directly at the camera. It’s starting to drive me crazy! I know that nobody looks directly at the camera when they’re taking a selfie in the mirror. So why does Stable Diffusion keep doing this? I’ve tried everything I can think …
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The Biden administration aims to deploy offshore wind turbines capable of generating 30 gigawatts of power by 2030. With less than a decade to go, the country remains woefully behind target.
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Posted by Rajat Sen and Yichen Zhou, Google Research Time-series forecasting is ubiquitous in various domains, such as retail, finance, manufacturing, healthcare and natural sciences. In retail use cases, for example, it has been observed that improving demand forecasting accuracy can meaningfully reduce inventory costs and increase revenue. Deep learning (DL) models have emerged as …
Read more “A decoder-only foundation model for time-series forecasting”
Almost a year ago, IBM encountered a data validation issue during one of our time-sensitive mergers and acquisitions data flows. We faced several challenges as we worked to resolve the issue, including troubleshooting, identifying the problem, fixing the data flow, making changes to downstream data pipelines and performing an ad hoc run of an automated …
Read more “IBM Databand: Self-learning for anomaly detection”
Posted by Rajat Sen and Yichen Zhou, Google Research Time-series forecasting is ubiquitous in various domains, such as retail, finance, manufacturing, healthcare and natural sciences. In retail use cases, for example, it has been observed that improving demand forecasting accuracy can meaningfully reduce inventory costs and increase revenue. Deep learning (DL) models have emerged as …
Read more “A decoder-only foundation model for time-series forecasting”