NIPS 2016 Tutorial: Generative Adversarial Networks · HackerPapers discussions

NIPS 2016 Tutorial: Generative Adversarial Networks — mentioned by nl

Wed, 11 Jan 2017 03:07:04 GMT

GANs (and InfoGAN[0] in particular) has shown there are still pretty huge breakthoughs out there. Some other things you may be interested in: Learning to learn by gradient descent by gradient descent[1] Neural Architecture Search with Reinforcement Learning[2] [0] https://arxiv.org/abs/1606.03657, https://arxiv.org/pdf/1701.00160.pdf [1] https://arxiv.org/abs/1606.04474 [2] https://openreview.net/pdf?id=r1Ue8Hcxg

Paper and discussion history. A mention is not an endorsement.

NIPS 2016 Tutorial: Generative Adversarial Networks — HN submission

Sun, 08 Jan 2017 20:24:45 GMT

Generative Adversarial Networks

3 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

NIPS 2016 Tutorial: Generative Adversarial Networks — mentioned by brandonb

Sun, 08 Jan 2017 19:48:31 GMT

Interesting idea! I think this is analogous to the idea of a de-noising autoencoder in computer vision. Here, instead of introducing Gaussian noise at the pixel level and using a CNN, you're introducing grammatical "noise" at the world level and using an LSTM. I think that general framework applies to many different domains. For example, we trained a denoising sequence autoencoder on HealthKit data (sequences of step counts and heart rate measurements) in order to predict whether somebody is likely to have diabetes, high blood pressure, or a heart rhythm disorder based on wearable data. I've…

Paper and discussion history. A mention is not an endorsement.

NIPS 2016 Tutorial: Generative Adversarial Networks — HN submission

Wed, 04 Jan 2017 11:03:11 GMT

DL tutorial by Ian Goodfellow on generative adversarial networks (GANs)

3 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

NIPS 2016 Tutorial: Generative Adversarial Networks — HN submission

Tue, 03 Jan 2017 21:06:04 GMT

NIPS 2016 Tutorial: Generative Adversarial Networks

3 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

NIPS 2016 Tutorial: Generative Adversarial Networks — mentioned by cranmer

Tue, 03 Jan 2017 14:36:10 GMT

Lots of topics and links throughout. Approximate Bayesian Computation website does a good job of framing what is meant by likelihood free inference. https://approximatebayesiancomputational.wordpress.com/paper... Here's an alternative technique for likelihood free inference: https://arxiv.org/abs/1506.02169 and a more recent approach http://beta.briefideas.org/ideas/5c2f74aedbf3618ca180382e393... making machine learning more robust to systematic uncertainties https://arxiv.org/abs/1611.01046 A tech report summarizing Goodfellow's NIPS tutorial on GANs https://arxiv.org/abs/1701.00160

Paper and discussion history. A mention is not an endorsement.

NIPS 2016 Tutorial: Generative Adversarial Networks — HN submission

Tue, 03 Jan 2017 07:35:02 GMT

NIPS 2016 Tutorial: Generative Adversarial Networks

4 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.