#ICML2019 live from Long Beach, CA, via icmlconf Learn more
→ https://mld.ai/icml2019-live #machinelearning #ML #mldcmu #ICML

✴️ @AI_Python_EN

deep learning for breast cancer screening at the AI for Social Good Workshop at #ICML2019

Paper: https://arxiv.org/abs/1903.08297
Code: https://github.com/nyukat/breast_cancer_classifier

✴️ @AI_Python_EN

Adaptive Neural Trees (ANTs)
Microsoft Research ,We aimed to combine the benefits of decision trees and deep neural networks.
Paper: http://proceedings.mlr.press/v97/tanno19a.html
Code: https://github.com/rtanno21609/AdaptiveNeuralTrees

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Notes from Thirty-sixth International Conference on Machine Learning here:
https://david-abel.github.io/notes/icml_2019.pdf
#ICML2019

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Best paper award at #ICML2019 main idea: unsupervised learning of disentangled representations is fundamentally impossible without inductive biases. Verified theoretically & experimentally.
https://arxiv.org/pdf/1811.12359.pdf

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✴️ @AI_Python_EN

Do you want to improve your generator for free? Just output low energy samples (i.e. filter them with the discriminator)! «Metropolis-Hastings GANs»

✴️ @AI_Python_EN

I'll be sharing 5 Lessons Learned Helping 200,000 non-ML experts* use ML as an #ICML2019 AutoML workshop keynote
https://sites.google.com/view/automl2019icml/schedule?authuser=0

✴️ @AI_Python_EN

Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift Paper: https://arxiv.org/abs/1906.02530

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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations"
http://goo.gle/2IyFqTO
recipient of an #ICML2019 Best Paper Award! Learn more in the blog post at
http://goo.gle/2KaMs48 .

✴️ @AI_Python_EN

Conceptualizing systemically and in terms of conditional probabilities, rather than categorically, are perhaps the two keys to statistical thinking.

"What the heck does that mean?" you might wonder. A concrete example is medicine. Doctors are usually not biostatisticians but are trained to think statistically.

This is why they say things like: "If there are no serious complications and no side effects from the medication, and no deterioration in liver function, then the prognosis is good."

One way to define statistical thinking is a series of questions we can ask ourselves, such as:

Am I asking the right questions?

Are there rival explanations I haven't considered?

Am I confusing the possible with the plausible or the plausible with fact?

Are there unobserved variables or other confounders I haven’t accounted for?

Am I confusing cause with effect, or correlation with causation?

Am I drawing conclusions about fruit based only on apples?

Given A and B, if I do C and D, what are the likely outcomes?

✴️ @AI_Python_EN

For those of you who have been waiting for the Neural Network Visualization course to be completed before you purchase, you are in luck!
https://lnkd.in/eNeue6N

For those who invested early, your foresight is rewarded. At your leisure, you can now smugly visit the course curriculum page and walk through the entire course.

The course has been a pleasure to put together and I hope you get as much out of it as I have. I look forward to reading your comments on the lessons.

✴️ @AI_Python_EN

Deep Learning: AlphaGo Zero Explained In One Picture

By L.V.: https://lnkd.in/eJMAKfy

#ArtificialIntelligence #DeepLearning #NeuralNetworks #ReinforcementLearning

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The state-of-the-art in expert recommendation systems

https://www.sciencedirect.com/science/article/abs/pii/S0952197619300703

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Facebook Open-sources AI Habitat, an advanced simulation platform for embodied AI research
Details:
https://lnkd.in/eT-xvqh
#ArtificialIntelligence #MachineLearning #DataScience

✴️ @AI_Python_EN

Learn about Learning from Unlabeled Videos at #CVPR2019

https://sites.google.com/view/luv2019/home?authuser=0

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Berkeley/FAIR AI revolution slogans:
- Jitendra Malik: "Supervision is the opium of the AI researcher"
- Alyosha Efros: "The AI revolution will not be supervised"
- Yann LeCun: "self-supervised learning is the cake,

https://www.facebook.com/722677142/posts/10156036317282143/

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Despite their popularity in media and among amateurs, GANs have quite limited practical application. But this specific result has a huge cultural value.

A neural network was used to recreate the Doom Guy in high-res: https://lnkd.in/eERQ7MJ

✴️ @AI_Python_EN

Group For Who Have a Passion For:

1. Artificial Intelligence
2. Machine Learning
3. Deep Learning
4. Data Science
5. Computer vision
6. Image Processing

https://t.me/DeepLearningML

✴️ @AI_Python_EN

Great to see lots of interest in meta-learning at #CVPR2019 ! Had trouble getting in the room to give my talk. My talk was based on our icmlconf tutorial with Slides, video, references
here: http://sites.google.com/view/icml19metalearning

✴️ @AI_Python_EN

[about] https://medium.com/@jasonphang/deep-neural-networks-improve-radiologists-performance-in-breast-cancer-screening-565eb2bd3c9f

[code] https://github.com/nyukat/breast_cancer_classifier

[preprint] https://arxiv.org/pdf/1903.08297.pdf

[data specs] https://cs.nyu.edu/~kgeras/reports/datav1.0.pdf

[ICML '19] https://aiforsocialgood.github.io/icml2019/acceptedpapers.htm

#AI #radiology #breastcancer

✴️ @AI_Python_EN