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  1. Il y a 21 heures · Ian Goodfellow is a prominent AI researcher known for his contributions to the field of machine learning and generative AI. He is the creator of the generative adversarial network (GAN), a breakthrough deep learning architecture that has revolutionized the ability to generate realistic and high-quality synthetic data. Goodfellow’s work on GANs has had a profound impact on various ...

  2. Il y a 4 jours · Goodfellow et al. [2014] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014.

  3. Il y a 3 jours · Ian Goodfellow; Jean Pouget-Abadie; Mehdi Mirza; Y. Bengio; We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a ...

  4. Il y a 4 jours · A year earlier, Ian Goodfellow, having just joined Google Brain, had developed an idea to pit two convolutional neural networks against each other. One, having been trained on a particular kind of data, would attempt to generate new examples of a similar kind, while the other would study the results, blended with the original training set, and aim to work out which were original and which ...

  5. Il y a 2 jours · For an in-depth understanding of GANs, refer to Ian Goodfellow’s original research paper. Variational Autoencoders (VAEs) Overview: VAEs are a type of autoencoder designed to generate new data samples by learning the underlying distribution of the training data. They work by encoding input data into a lower-dimensional latent space and then ...

  6. Il y a 4 jours · Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. GANs have gained significant attention for their...

  7. Il y a 2 jours · Joan Bruna, Christian Szegedy, Ilya Sutskever, Ian Goodfellow, Wojciech Zaremba, Rob Fergus, Dumitru Erhan. 01 Jul 2024 (modified: 24 Dec 2013) ICLR 2014 conference submission Readers: Everyone. Decision: submitted, no decision. Abstract: Deep neural networks are highly expressive models that have recently achieved state of the art ...