NSF
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How to fake a medical record in order to mitigate privacy risks
In machine learning, generative adversarial networks (GANs) involve two artificial neural networks squaring off, one, the generator, trying to delude the other, the discriminator, into accepting synthetic data as real. Beyond their science and engineering applications, GANs can generate utterly convincing “photographs” of people who do not exist. Unrestricted… Read MoreNov. 4, 2019
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Vanderbilt engineers lead $1 million NSF pilot to develop tech for workforce inclusion of people with autism
In machine learning, generative adversarial networks (GANs) involve two artificial neural networks squaring off, one, the generator, trying to delude the other, the discriminator, into accepting synthetic data as real. Beyond their science and engineering applications, GANs can generate utterly convincing “photographs” of people who do not exist. Unrestricted… Read MoreOct. 11, 2019
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On-the-move cancer cells prefer a “comfort cruise,” follow predictable paths of least resistance
In machine learning, generative adversarial networks (GANs) involve two artificial neural networks squaring off, one, the generator, trying to delude the other, the discriminator, into accepting synthetic data as real. Beyond their science and engineering applications, GANs can generate utterly convincing “photographs” of people who do not exist. Unrestricted… Read MoreSep. 13, 2019
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$3 million NSF grant sets up graduate trainee program for autism-related workplace innovations
In machine learning, generative adversarial networks (GANs) involve two artificial neural networks squaring off, one, the generator, trying to delude the other, the discriminator, into accepting synthetic data as real. Beyond their science and engineering applications, GANs can generate utterly convincing “photographs” of people who do not exist. Unrestricted… Read MoreSep. 12, 2019
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13 engineering students awarded prestigious NSF Graduate Research Fellowships
In machine learning, generative adversarial networks (GANs) involve two artificial neural networks squaring off, one, the generator, trying to delude the other, the discriminator, into accepting synthetic data as real. Beyond their science and engineering applications, GANs can generate utterly convincing “photographs” of people who do not exist. Unrestricted… Read MoreApr. 11, 2019
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DC innovation showcase to feature student-led Zeno Power Systems
In machine learning, generative adversarial networks (GANs) involve two artificial neural networks squaring off, one, the generator, trying to delude the other, the discriminator, into accepting synthetic data as real. Beyond their science and engineering applications, GANs can generate utterly convincing “photographs” of people who do not exist. Unrestricted… Read MoreApr. 8, 2019
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Like geese and race cars, cancer cells draft their way to new sites
In machine learning, generative adversarial networks (GANs) involve two artificial neural networks squaring off, one, the generator, trying to delude the other, the discriminator, into accepting synthetic data as real. Beyond their science and engineering applications, GANs can generate utterly convincing “photographs” of people who do not exist. Unrestricted… Read MoreMar. 25, 2019
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New biomaterial could improve bone grafting
In machine learning, generative adversarial networks (GANs) involve two artificial neural networks squaring off, one, the generator, trying to delude the other, the discriminator, into accepting synthetic data as real. Beyond their science and engineering applications, GANs can generate utterly convincing “photographs” of people who do not exist. Unrestricted… Read MoreFeb. 25, 2019
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Study links Celebrex, heart valve calcification after earlier research declared drug safe
In machine learning, generative adversarial networks (GANs) involve two artificial neural networks squaring off, one, the generator, trying to delude the other, the discriminator, into accepting synthetic data as real. Beyond their science and engineering applications, GANs can generate utterly convincing “photographs” of people who do not exist. Unrestricted… Read MoreFeb. 22, 2019
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First step toward model brain: turning iPSCs into working blood-brain barrier
In machine learning, generative adversarial networks (GANs) involve two artificial neural networks squaring off, one, the generator, trying to delude the other, the discriminator, into accepting synthetic data as real. Beyond their science and engineering applications, GANs can generate utterly convincing “photographs” of people who do not exist. Unrestricted… Read MoreFeb. 21, 2019