Learn to detect data abnormalities before they impact your business by using XGBoost, autoencoders, and GANs. Workshops are available in both the NALA and EMEA regions.
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This is part of a series on how researchers at NVIDIA have developed methods to improve and accelerate sampling from diffusion models, a novel and powerful class of generative models. Part 1 introduced diffusion models as a powerful class for deep generative models and examined their trade-offs in addressing the generative learning trilemma. While diffusion models satisfy both the first and��
]]>This is part of a series on how NVIDIA researchers have developed methods to improve and accelerate sampling from diffusion models, a novel and powerful class of generative models. Part 2 covers three new techniques for overcoming the slow sampling challenge in diffusion models. Generative models are a class of machine learning methods that learn a representation of the data they are trained��
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The desire to edit photos of cats, cars, or even antique paintings, has never been more accessible thanks to a generative adversarial network (GAN) model called EditGAN. The work��from NVIDIA, the University of Toronto, and MIT researchers��builds off DatasetGAN, an artificial intelligence vision model that can be trained with as few as 16 human-annotated images and performs as effectively as other��
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Scientists at NVIDIA and Cornell University introduced a hybrid unsupervised neural rendering pipeline to represent large and complex scenes efficiently in voxel worlds. Essentially, a 3D artist only needs to build the bare minimum, and the algorithm will do the rest to build a photorealistic world. The researchers applied this hybrid neural rendering pipeline to Minecraft block worlds to generate��
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The NVIDIA Deep Learning Institute (DLI) is offering instructor-led, hands-on training on how to build applications of AI for anomaly detection. Anomaly detection is the process of identifying data that deviates abnormally within a data set. Different from the simpler process of identifying statistical outliers, anomaly detection seeks to discover data that should not be considered normal��
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You may soon be able to see how future flooding could hit your city with a newly developed AI model. The study, from a team of Canadian and U.S. researchers, uses generative adversarial networks (GANs) to produce realistic images of climate change-induced flooding. Named ClimateGAN, the team developed the model to underscore the destruction of extreme weather events and prompt collective action��
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Astrophysics researchers have long faced a tradeoff when simulating space�� simulations could be either high-resolution or cover a large swath of the universe. With the help of generative adversarial networks, they can accomplish both at once. Carnegie Mellon University and University of California researchers developed a deep learning model that upgrades cosmological simulations from low to high��
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BMW today unveiled a virtual art installation that projects AI-generated artwork onto a virtual rendition of the automaker��s 8 Series Gran Coupe. Dubbed ��The Ultimate AI Masterpiece,�� the installation harnessed NVIDIA StyleGAN �� a generative model for high-resolution images �� to create original artwork projection-mapped onto the virtual vehicle. The project debuts in conjunction with the��
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A recent National Poetry Month feature in The Washington Post presented AI-generated artwork alongside five original poems reflecting on seasons of the past year. Created by the Lede Lab �� an experimental news team at The Post dedicated to exploring emerging technologies and new storytelling techniques �� the artwork combined the output of machine learning models including NVIDIA StyleGAN2.
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Recently, one of Sweden��s largest banks trained generative adversarial neural networks (GANs) using NVIDIA GPUs as part of its fraud and money-laundering prevention strategy. Financial fraud and money laundering pose immense challenges to financial institutions and society. Financial institutions invest huge amounts of resources in both identifying and preventing suspicious and illicit activities.
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AI will soon massively empower architects in their day-to-day practice. This potential is around the corner and my work provides a proof of concept. The framework used in my work offers a springboard for discussion, inviting architects to start engaging with AI, and data scientists to consider Architecture as a field of investigation. In this post, I summarize a part of my thesis��
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In part 1 of this series I introduced Generative Adversarial Networks (GANs) and showed how to generate images of handwritten digits using a GAN. In this post I will do something much more exciting: use Generative Adversarial Networks to generate images of celebrity faces. I am going to use CelebA [1], a dataset of 200,000 aligned and cropped 178 x 218-pixel RGB images of celebrities.
]]>You heard it from the Deep Learning guru: Generative Adversarial Networks [2] are a very hot topic in Machine Learning. In this post I will explore various ways of using a GAN to create previously unseen images. I provide source code in Tensorflow and a modified version of DIGITS that you are free to use if you wish to try it out yourself. Figure 1 gives a preview of what you will learn to do in��
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