TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation

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Denoising Diffusion models have demonstrated their proficiency for generative sampling. However, generating good samples often requires many iterations. Consequently, techniques such as binary time-distillation (BTD) have been proposed to reduce the number of network calls for a fixed architecture. In this paper, we introduce TRAnsitive Closure Time-distillation (TRACT), a new method that extends BTD. For …

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Generate a counterfactual analysis of corn response to nitrogen with Amazon SageMaker JumpStart solutions

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In his book The Book of Why, Judea Pearl advocates for teaching cause and effect principles to machines in order to enhance their intelligence. The accomplishments of deep learning are essentially just a type of curve fitting, whereas causality could be used to uncover interactions between the systems of the world under various constraints without …

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Vertex AI Experiments Autologging

How you can automate ML experiment tracking with Vertex AI Experiments autologging

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Practical machine learning (ML) is a trial and error process. ML practitioners compare different performance metrics by running ML experiments till you find the best model with a given set of parameters. Because of the experimental nature of ML, there are many reasons for tracking ML experiments and making them reproducible including debugging and compliance.  …

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Using artificial intelligence to design innovative materials

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Advanced materials become increasingly complex due to the high requirements they have to fulfill regarding sustainability and applicability. Dierk Raabe and colleagues reviewed the use of artificial intelligence in materials science and the untapped spaces it opens if combined with physics-based simulations. Compared to traditional simulation methods AI has several advantages and will play a …

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