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

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

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

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/

✴️ @AI_Python_EN

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

A major benefit of self-supervision is we can truly scale and adapt on the fly. It could be 10% behind supervised ImageNet, it would still do better in real life. We show in http://online-objects.github.io that the longer our model looks at objects, the better it understands them.

✴️ @AI_Python_EN

#ACL2019 paper "Self-attentional Models for Lattice Inputs" proposes a method to apply Transformers to graphs encoding ambiguity from an upstream system such as speech recognition. Nice results, and much faster on speech translation benchmarks!
https://arxiv.org/abs/1906.01617

✴️ @AI_Python_EN

Oldies but Goldies: D. Lee et S. Seung, Learning the parts of objects by non-negative matrix factorization, Nature (1999). Lee and Seung proposed the most popular matrix factorization algorithm, which operates by multiplicative updates.
https://en.wikipedia.org/wiki/Non-negative_matrix_factorization

✴️ @AI_Python_EN

A video is now online of our ICML Tutorial on Recent Advances in Population-Based Search for Deep Neural Networks: Quality Diversity, Indirect Encodings, and Open-Ended Algorithms. We hope you find it valuable!

https://www.youtube.com/watch?v=g6HiuEnbwJE&feature=youtu.be
✴️ @AI_Python_EN

AlphaStar: Mastering the Game of StarCraft II

Talk by David Silver: https://slideslive.com/38916905/alphastar-mastering-the-game-of-starcraft-ii

#ArtificialIntelligence #DeepLearning #ReinforcementLearning

✴️ @AI_Python_EN

Best Papers Awards #ICML2019
The Thirty-sixth International Conference on Machine Learning, Long Beach, USA

(1)Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations https://arxiv.org/pdf/1811.12359.pdf
Congratulations to the GoogleAI team of Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly,Bernhard Schölkopf, Olivier Bachem

(2)Rates of Convergence for Sparse Variational Gaussian Process Regression
https://arxiv.org/pdf/1903.03571.pdf
Kudos to David R. Burt, Carl E. Rasmussen, Mark van der Wilk of University of Cambridge

✴️ @AI_Python_EN

Learning the Depths of Moving People by Watching Frozen People” (http://goo.gle/2x4tEuQ ), recipient of a #CVPR2019 Best Paper Honorable Mention Award. Learn more about the paper at
http://goo.gle/2ZuZtJt

✴️ @AI_Python_EN

Rigorously testing Machine learning models using meta-learning. We show how a Neural Process-based meta-learning formulation allows us to efficiently search for hard examples.
inDeepMind, Interested in adversarial tests and reinforcement learning? We combine meta-learning in a general probabilistic paradigm to detect failures, helping us build robust algorithms. Includes results on recommender systems and control: http://arxiv.org/abs/1903.11907

✴️ @AI_Python_EN

Slides: "Three Challenging Research Avenues (in language and vision)" from my VQA workshop #cvpr2019 talk.
https://yoavartzi.com/slides/2019_06_17_vqa_workshop.pdf
Includes a quick summary of some of our recent vision+language work and resources

✴️ @AI_Python_EN

One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization is accepted to Interspeech 2019.
By combining VAE and adaIN, our model is able to do one-shot VC by a reference source utterance and a target utterance.

✴️ @AI_Python_EN

Text similarity
Are these two sentences similar ?!
1) President greets the press in Chicago
2) Obama speaks in Illinois


— Jaccard
— Cosine
— WMD

#naturallanguageprocessing


https://medium.com/@adriensieg/text-similarities-da019229c894

✴️ @AI_Python_EN