Dr. Andrew Fitzgibbon is an expert in 3D #computervision and graphics. Discover work on body- and hand-tracking for tech like Kinect and HoloLens and hear how research on dolphins helped build models for the human hand:
https://aka.ms/AA5b1q9 #CVPR2019
✴️ @AI_Python_EN

hierarchical localization paper won the visual localization challenge at #CVPR2019
Paper: https://arxiv.org/abs/1812.03506

✴️ @AI_Python_EN

Facebook & Partnership AI are organizing the 1st Computer Vision for Global Challenges workshop #CVPR2019. they want to help build partnerships between researchers and humanitarian orgs, and discuss how AI can advance the UN sustainable development goals. https://research.fb.com/computer-vision-and-global-challenges-new-research-and-applications/

✴️ @AI_Python_EN

Facebook AI:
Researchers have created 2.5D visual sound by injecting spatial information contained in video frames that accompany a typical monaural audio stream. We've open sourced our data set & videos w/ binaural audio are included. We'll present this at #CVPR2019.
https://ai.facebook.com/blog/visual-sound/

✴️ @AI_Python_EN

When in doubt, people ask for help. What if our personal digital assistants could do the same? Microsoft researchers have created a novel method of training agents to strategically ask for assistance during vision-language tasks:
https://aka.ms/AA5auc5 #CVPR2019

✴️ @AI_Python_EN

Introducing Text2Scene, an interpretable compositional text-to-image synthesis approach https://arxiv.org/abs/1809.01110 // No GANs! but results as good or superior to GANs when it comes to generating scenes.

✴️ @AI_Python_EN

Keyword presence in cvpr2019 paper titles:
- 'Deep' decreasing (taken for granted?).
- 'GAN' saturating.
What's the new 🔥 keyword we should be checking?
#CVPR2019
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How to make a pizza: Learning a compositional layer-based GAN model. Or “MIT’s AI learns to Become Pizza Guru. All pizza design will soon be automated. ”
https://arxiv.org/abs/1906.02839
#gan #ai #computervision

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You can find ⁦Women in Computer Vision Workshop papers here from #CVPR2019
https://openaccess.thecvf.com/content_CVPRW_2019/html/WiCV/
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Creating xBD: A Dataset for Assessing Building Damage from Satellite Imagery" https://bit.ly/xview2-dataset

✴️ @AI_Python_EN

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

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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: https://proceedings.mlr.press/v97/tanno19a.html
Code: https://github.com/rtanno21609/AdaptiveNeuralTrees

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

✴️ @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»

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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

✴️ @AI_Python_EN

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