NIPS 2016 - Generative Adversarial Networks - Ian Goodfellow

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  1. Hao Du (Alex)
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Ian Goodfellow: Generative 1:55:54

Ian Goodfellow: Generative Adversarial Networks (NIPS 2016 tutorial)

NIPS 2016 - 1:55:54

NIPS 2016 - Generative Adversarial Networks - Ian Goodfellow

Introduction to GANs, 0:31:25

Introduction to GANs, NIPS 2016 | Ian Goodfellow, OpenAI

Learning to Draw 0:19:21

Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Training

A Connection Between 0:04:27

A Connection Between GANs, Inverse Reinforcement Learning, and Energy Based Models, NIPS 2016

Connecting Generative Adversarial 0:04:21

Connecting Generative Adversarial Networks and Actor Critic Methods, NIPS 2016 | David Pfau

Unrolled Generative Adversarial 0:04:41

Unrolled Generative Adversarial Networks, NIPS 2016 | Luke Metz, Google Brain

GANs explained | 0:11:37

GANs explained | Generative Adversarial Networks video with showcase!

Energy-Based Adversarial Training 0:36:39

Energy-Based Adversarial Training and Video Prediction, NIPS 2016 | Yann LeCun, Facebook AI Research

Semantic Segmentation using 0:05:58

Semantic Segmentation using Adversarial Networks, NIPS 2016 | Pauline Luc, Facebook AI Research

Adversarial Approaches to 0:28:00

Adversarial Approaches to Bayesian Learning and Bayesian Approaches to Adversarial Robustness

f-GAN: Training Generative 0:03:24

f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization (NIPS 2016)

Adversarial Training Methods 0:05:43

Adversarial Training Methods for Semi-Supervised Text Classification, NIPS 2016 | Andrew M. Dai

What are generative 0:00:17

What are generative adversarial networks ? GANs #artificialintelligence #ai #datascience

Conditional Image Synthesis 0:04:14

Conditional Image Synthesis with Auxiliary Classifier GANs, NIPS 2016 | Augustus Odena, Google Brain

f-GAN: Training Generative 0:34:22

f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization, NIPS 2016

Generative Adversarial Networks 0:01:07

Generative Adversarial Networks (GANs)

Learning in Implicit 0:04:54

Learning in Implicit Generative Models, NIPS 2016 | Shakir Mohamed, Google DeepMind

How to train 0:31:59

How to train a GAN, NIPS 2016 | Soumith Chintala, Facebook AI Research

What are GANs 0:08:23

What are GANs (Generative Adversarial Networks)?

Generative Adversarial Networks 0:06:04

Generative Adversarial Networks

Bayesian GAN (NIPS 0:03:27

Bayesian GAN (NIPS 2017)

PR-087: Spectral Normalization 0:34:31

PR-087: Spectral Normalization for Generative Adversarial Networks

Ian Goodfellow, Research 0:38:24

Ian Goodfellow, Research Scientist OpenAI : Generative Adversarial Networks (GANs) #AIWTB 2016

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If anyone wants to listen to the confrontation between Goodfellow and Schmidhuber, here it is: 1:02:58

JY-ejig
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Thanks so much for sharing !! Its amazing to hear from Ian himself...

mayankpj
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Did anyone notice that the person who introduced Ian was Aaron Courville..!

manishandu
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Any idea, at 33:28, What do Ex and Ez represent in the objective function J of the discriminator ?

sairocks
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When answering the question of 1:25:44 he told that the guy he mentored at Google was working on something related. Did someone get the name of his former intern? I am interested in his project!

superaluis
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hello sir, i'm doing a study having 19 predictors. 2 are continuous variable and 17 are categorical variable but my outcome is categorical variable such as 1 and 0. but now i have great interest in this newly model such as GAN.. i'm asking is it possible to use this kind of model in my study? thanks

im_karamo
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I have only a slight understanding of calculus. What exactly is d(x) and g(x).
Derivatives? Derivatives of what? What is the way into understanding these long equations?

Throwingness