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Há 21 horas · In 2014, Ian Goodfellow, then a PhD candidate at the Université de Montréal, came up with an ingenious idea. Instead of following the traditional method of relying on a single deep learning network, Goodfellow proposed a novel framework.
Há 1 dia · Goodfellow et al. (2016) Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. 2016. Deep learning, volume 1. MIT press Cambridge. He et al. (2016) Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition.
Há 1 dia · Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. 2018. Adversarial Examples in the Physical World. In Artificial Intelligence Safety and Security.
Há 21 horas · Since their inception in 2014 by Ian Goodfellow and his associates, GANs have grown to be among the most widely used generative AI models. They run on two neural networks, which are trained concurrently using an adversarial training approach. Generator: It produces synthetic data that mimics the real-world data it has been trained on.
Há 4 dias · We choose cGANs over other learning methods because of several compelling advantages that cGANs offer (Goodfellow et al., 2014), particularly in terms of generating high-quality synthetic data and improving the accuracy and efficiency of path loss predictions.
Há 3 dias · “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Known as the “Bible” of deep learning, this book covers deep learning theory and practice in great detail. “Deep Learning for Coders with Fastai and PyTorch” by Jeremy Howard and Sylvain Gugger : A hands-on guide to deep learning with practical applications using the Fastai library and PyTorch.
In 2014, Ian Goodfellow and colleagues developed the generative adversarial network (GAN), setting up two neural networks to compete (i.e., train) against each other. One network generated data while the other tried to determine if the data was real or fake. Transformer models were introduced in 2017.
