Saturday Morning Videos: NIPS2017 Meta Learning Symposium videos

Pieter Abbeel mentioned that the #nips2017 Meta Learning Symposium videos are now available here.

Thanks to Risto Miikkulainen, Quoc Le, Kenneth Stanley, and Chrisantha Fernando for organizing and getting the videos online !

Opening remarks, Quoc Le (slides, video)

Topic I: Evolutionary Optimization

• Evolving Multitask Neural Network Structure, Risto Miikkulainen (slides, video)
• Evolving to Learn through Synaptic Plasticity, Ken Stanley (slides, video)
• PathNet and Beyond, Chrisantha Fernando (slides, video)

Topic II: Bayesian Optimization

• Bayesian Optimization for Automated Model Selection, Roman Garnett (slides, video)
• Automatic Machine Learning (AutoML) and How To Speed It Up, Frank Hutter (slides, video)

Topic III: Gradient Descent

• Contrasting Model- and Optimization-based Metalearning, Oriol Vinyals (slides, video)
• Population-based Training for Neural Network Meta-Optimization, Max Jaderberg (slides, video)
• Learning to Learn for Robotic Control, Pieter Abbeel (slides, video)
• On Learning How to Learn Learning Strategies, Juergen Schmidhuber (slides, video)

Topic IV: Reinforcement Learning

• Intrinsically Motivated Reinforcement Learning, Satinder Singh (video)
• Self-Play, Ilya Sutskever (slides, video)
• Neural Architecture Search, Quoc Le (slides, video)
• Multiple scales of reward and task learning, Jane Wang (slides, video)

Panel discussion, Moderator: Risto Miikkulainen, Panelists: Frank Hutter, Juergen Schmidhuber, Ken Stanley, Ilya Sutskever (video)


Credit photn: NASA, Starshine 2 , more on project Starshine.
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#NIPS2017 Video: "Why AI Will Make it Possible to Reprogram the Human Genome", Brendan Frey

When watching Brendan Frey's presentation at NIPS, I immediately thought of Hal Dietz's words :"...The Window Doesn't Close...". The title of the talk is "Why AI Will Make it Possible to Reprogram the Human Genome". Brendan mentions the work of Jennifer Listgarten who did a talk at the Paris Machine Learning meetup two years ago. Enjoy !

Related topics that we covered here on Nuit Blanche:

• Predicting the Future: Randomness and Parsimony
• Predicting the Future: The Steamrollers
• Saturday Morning Video: Life at the Speed of Light - Craig Venter ( and some remarks )
• Sunday Morning Insight: Matrix Factorizations and the Grammar of Life
• It's quite simply, the stuff of Life...

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NIPS 2017 accepted papers

So it looks it is going to be difficult to get a ticket to NIPS if you are not buying one of these in the coming days !
The workshop are still open for submissions. The list is here. 
Accepted papers are now showing up on

•  Arxiv,. This is the query I use to get paper posted there.
• Twitter, a few authors are coming out of the woodwork to post their papers and codes.

Oh! and if you are a company. The sponsorship for NIPS is already oversubscribed. They cannot take your money anymore.

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Saturday Morning Video: #NIPS2016 Symposium, Recurrent Neural Networks and Other Machines that Learn Algorithms

From the page of the minisymposium: 

Program
Full session videos are available here: Session 1, Session 2, Session 3.
We provide individual videos and slides below. You can also watch this Playlist.  

2:00 - 2:20	Jürgen Schmidhuber  Introduction to Recurrent Neural Networks and Other Machines that Learn Algorithms  Slides Video
2:20 - 2:40	Paul Werbos  Deep Learning in Recurrent Networks: From Basics To New Data on the Brain  Slides Video
2:40 - 3:00	Li Deng  Three Cool Topics on RNN  Slides Video
3:00 - 3:20	Risto Miikkulainen  Scaling Up Deep Learning through Neuroevolution  Slides Video
3:20 - 3:40	Jason Weston  New Tasks and Architectures for Language Understanding and Dialogue with Memory  Slides Video
3:40 - 4:00	Oriol Vinyals  Recurrent Nets Frontiers  Slides Unavailable Video
4:00 - 4:30	Coffee Break
4:30 - 4:50	Mike Mozer  Neural Hawkes Process Memories  Slides Video
4:50 - 5:10	Ilya Sutskever  Meta Learning in the Universe  Slides Video
5:10 - 5:30	Marcus Hutter  Asymptotically fastest solver of all well-defined problems  Slides Video
	(unfortunately cannot come - J. Schmidhuber will stand in for him)
5:30 - 5:50	Nando de Freitas  Learning to Learn, to Program, to Explore and to Seek Knowledge  Slides Video
5:50 - 6:10	Alex Graves  Differentiable Neural Computer  Slides Video
6:30 - 7:30	Light dinner break/Posters
7:30 - 7:50	Nal Kalchbrenner  Generative Modeling as Sequence Learning  Slides Video
7:50 - 9:00	Panel Discussion  Topic: The future of machines that learn algorithms Panelists: Ilya Sutskever, Jürgen Schmidhuber, Li Deng, Paul Werbos, Risto Miikkulainen, Sepp Hochreiter  Moderator: Alex Graves  Video

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Videos: #NIPS2016 Workshop on Adversarial Training

Woohoo, David just made available the videos of the #NIPS2016 Workshop on Adversarial Training

 

NIPS 2016 Workshop on Adversarial Training - Ian Goodfellow - Introduction to GANs

31:25

 

NIPS 2016 Workshop on Adversarial Training - Soumith Chintala - How to train a GAN

31:59

 

NIPS 2016 Workshop on Adversarial Training - Sebastian Nowozin - fGAN

34:22

 

NIPS 2016 Workshop on Adversarial Training - Panel Discussion

45:26

 

NIPS 2016 Workshop on Adversarial Training - Aaron Courville - ALI and BiGANs

31:42

 

NIPS 2016 Workshop on Adversarial Training - Yann LeCun - Energy Based Adversarial Training

36:39

NIPS 2016 Workshop on Adversarial Training - Spotlights

1:05

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Saturday Morning Videos: Deep Learning Symposium, #NIPS2016

Here are the three sessions of the Deep Learning Symposium  at NIPS2016, enjoy !

Deep Learning Symposium Session 1

01:54:39

Deep Learning Symposium Session 201:05:19

Neural Information Processing Systems Conference - NIPS 2016

Deep Learning Symposium Session 301:21:49

Neural Information Processing Systems Conference - NIPS 2016

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The #NIPS2016 Videos are out

The videos for the Neural Information Processing Systems Conference - NIPS 2016 are out (first three days), enjoy !

Deep Reinforcement Learning Through Policy Optimization19:19

Neural Information Processing Systems Conference - NIPS 2016

Theory and Algorithms for Forecasting Non-Stationary Time Series01:45:04

Neural Information Processing Systems Conference - NIPS 2016

Crowdsourcing: Beyond Label Generation01:50:18

Neural Information Processing Systems Conference - NIPS 2016

ML Foundations and Methods for Precision Medicine and Healthcare02:08:17

Neural Information Processing Systems Conference - NIPS 2016

Variational Inference: Foundations and Modern Methods01:53:04

Neural Information Processing Systems Conference - NIPS 2016

Generative Adversarial Networks01:55:53

Neural Information Processing Systems Conference - NIPS 2016

Predictive Learning

56:52

Neural Information Processing Systems Conference - NIPS 2016

Intelligent Biosphere

49:42

Neural Information Processing Systems Conference - NIPS 2016

Value Iteration Networks19:40

Neural Information Processing Systems Conference - NIPS 2016

Tractable Operations for Arithmetic Circuits of Probabilistic Models19:15

Neural Information Processing Systems Conference - NIPS 2016

Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity16:51

Neural Information Processing Systems Conference - NIPS 2016

NIPS 2016

SDP Relaxation with Randomized Rounding for Energy Disaggregation

Neural Information Processing Systems Conference - NIPS 2016

SDP Relaxation with Randomized Rounding for Energy Disaggregation

List of Episodes

Neural Information Processing Systems Conference - NIPS 2016

Bayesian Intermittent Demand Forecasting for Large Inventories18:01

Neural Information Processing Systems Conference - NIPS 2016

Synthesis of MCMC and Belief Propagation17:26

Neural Information Processing Systems Conference - NIPS 2016

Deep Learning for Predicting Human Strategic Behavior

Neural Information Processing Systems Conference - NIPS 2016

Using Fast Weights to Attend to the Recent Past21:02

Neural Information Processing Systems Conference - NIPS 2016

Sequential Neural Models with Stochastic Layers20:18

Neural Information Processing Systems Conference - NIPS 2016

Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences20:55

Neural Information Processing Systems Conference - NIPS 2016

Graphons, mergeons, and so on!17:10

Neural Information Processing Systems Conference - NIPS 2016

Hierarchical Clustering via Spreading Metrics17:40

Neural Information Processing Systems Conference - NIPS 2016

Clustering with Same-Cluster Queries18:05

Neural Information Processing Systems Conference - NIPS 2016

Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA22:50

Neural Information Processing Systems Conference - NIPS 2016

Fast and Provably Good Seedings for k-Means19:44

Neural Information Processing Systems Conference - NIPS 2016

Supervised learning through the lens of compression16:52

Neural Information Processing Systems Conference - NIPS 2016

Supervised learning through the lens of compression

Neural Information Processing Systems Conference - NIPS 2016

MetaGrad: Multiple Learning Rates in Online Learning16:36

Neural Information Processing Systems Conference - NIPS 2016

Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning15:52

Neural Information Processing Systems Conference - NIPS 2016

Global Analysis of Expectation Maximization for Mixtures of Two Gaussians18:58

Neural Information Processing Systems Conference - NIPS 2016

Machine Learning and Likelihood-Free Inference in Particle Physics50:37

Neural Information Processing Systems Conference - NIPS 2016

Matrix Completion has No Spurious Local Minimum18:26

Neural Information Processing Systems Conference - NIPS 2016

Large-Scale Price Optimization via Network Flow18:59

Neural Information Processing Systems Conference - NIPS 2016

Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks18:17

Neural Information Processing Systems Conference - NIPS 2016

Supervised Word Mover's Distance21:30

Neural Information Processing Systems Conference - NIPS 2016

Beyond Exchangeability: The Chinese Voting Process19:34

Neural Information Processing Systems Conference - NIPS 2016

Protein contact prediction from amino acid co-evolution using convolutional networks for…20:12

Neural Information Processing Systems Conference - NIPS 2016

Deep Learning without Poor Local Minima19:19

Neural Information Processing Systems Conference - NIPS 2016

Learning to Poke by Poking: Experiential Learning of Intuitive Physics

Neural Information Processing Systems Conference - NIPS 2016

Learning to Poke by Poking: Experiential Learning of Intuitive Physics

Neural Information Processing Systems Conference - NIPS 2016

Learning What and Where to Draw21:37

Neural Information Processing Systems Conference - NIPS 2016

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural…22:55

Neural Information Processing Systems Conference - NIPS 2016

Achieving the KS threshold in the general stochastic block model with linearized acyclic…15:04

Neural Information Processing Systems Conference - NIPS 2016

Orthogonal Random Features18:42

Neural Information Processing Systems Conference - NIPS 2016

Poisson-Gamma dynamical systems16:40

Neural Information Processing Systems Conference - NIPS 2016

The Multiscale Laplacian Graph Kernel20:34

Neural Information Processing Systems Conference - NIPS 2016

Stochastic Online AUC Maximization14:40

Neural Information Processing Systems Conference - NIPS 2016

Without-Replacement Sampling for Stochastic Gradient Methods19:55

Neural Information Processing Systems Conference - NIPS 2016

Regularized Nonlinear Acceleration18:52

Neural Information Processing Systems Conference - NIPS 2016

Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back16:21

Neural Information Processing Systems Conference - NIPS 2016

Bayesian Optimization with Robust Bayesian Neural Networks14:50

Neural Information Processing Systems Conference - NIPS 2016

Learning About the Brain: Neuroimaging and Beyond51:37

Neural Information Processing Systems Conference - NIPS 2016

Learning About the Brain: Neuroimaging and Beyond

Neural Information Processing Systems Conference - NIPS 2016

Reproducible Research: the Case of the Human Microbiome55:53

Neural Information Processing Systems Conference - NIPS 2016

Interpretable Distribution Features with Maximum Testing Power22:45

Neural Information Processing Systems Conference - NIPS 2016

Examples are not enough, learn to criticize! Criticism for Interpretability20:39

Neural Information Processing Systems Conference - NIPS 2016

Showing versus doing: Teaching by demonstration19:03

Neural Information Processing Systems Conference - NIPS 2016

Relevant sparse codes with variational information bottleneck17:53

Neural Information Processing Systems Conference - NIPS 2016

Dense Associative Memory for Pattern Recognition24:11

Neural Information Processing Systems Conference - NIPS 2016

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