Another paper on automl: Neural Nets learning to design Neural Nets.
A reinforcement learning agent that learns to program new neural network architectures.
Same/better results as LSTMs but with funky nonlinearities (sine, SeLus, etc) and new connections that result in different activation patterns.
Arxiv: https://arxiv.org/abs/1712.07316
Post: https://einstein.ai/research/domain-specific-language-for-automated-rnn-architecture-search
Video displaying progress of GANs for photo generation. Now you can use neural networks to generate HD photo of a person who never existed.
https://www.youtube.com/watch?v=XOxxPcy5Gr4
#GAN #youtube
An article about the impossibility of intelligence explosion. There will be no singularity or significant breakthrough and humanity will die off becuase of sun explosion.
francois.chollet/the-impossibility-of-intelligence-explosion-5be4a9eda6ec" rel="nofollow">https://medium.com/@francois.chollet/the-impossibility-of-intelligence-explosion-5be4a9eda6ec
#CapsNet #tutorial on the YouTube
https://www.youtube.com/watch?v=pPN8d0E3900
#deeplearning
And another posts on #CapsNet and how they work.
Capsule Networks Are Shaking up AI — Here’s How to Use Them: https://hackernoon.com/capsule-networks-are-shaking-up-ai-heres-how-to-use-them-c233a0971952
Understanding Hinton’s Capsule Networks. Part I: Intuition:
pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b" rel="nofollow">https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
Understanding Hinton’s Capsule Networks. Part II: How Capsules Work:
pechyonkin/understanding-hintons-capsule-networks-part-ii-how-capsules-work-153b6ade9f66" rel="nofollow">https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-ii-how-capsules-work-153b6ade9f66
On 1st of November Geoff Hinton — one of the top NN researches has published two papers introducing new approach for #CV problems: Capsule Networks.
These architecture allows to recognize a face on the picture by detecting eyes, nose, mouth, regardless of the position / scaling / rotating the elements.
In other words, these approach allows neural network to be invariant to transformation of object.
First of papers: https://arxiv.org/abs/1710.09829
Second paper: https://openreview.net/forum?id=HJWLfGWRb&noteId=HJWLfGWRb
Article on Wired: https://www.wired.com/story/googles-ai-wizard-unveils-a-new-twist-on-neural-networks/
Explanation on hackernoon: https://hackernoon.com/what-is-a-capsnet-or-capsule-network-2bfbe48769cc
Another post with explanation: https://kndrck.co/posts/capsule_networks_explained/
Google's open source candy for all ML community:
Source-to-Source Debuggable Derivatives
https://opensource.googleblog.com/2017/11/tangent-source-to-source-debuggable.html?m=1
#opensource #nn #python #google
The State of Data Science & Machine Learning 2017 by Kaggle.
Very informative article about age, job titles, most popular languages and everything related to DS / ML.
Not to mention that source data is included.
https://www.kaggle.com/surveys/2017
#kaggle #statistics
Winning approaches for solving Advanced Driver Assistance System challenge on Kaggle:
https://blog.getnexar.com/how-a-22-year-old-from-shanghai-won-a-global-deep-learning-challenge-76f2299446a1
#deeplearning #kaggle #cv
NLP for beginners
http://blog.kaggle.com/2017/08/25/data-science-101-getting-started-in-nlp-tokenization-tutorial/
#tutorial #nlp
Comparison of 13 classic ML algorithms on 165 datasets.
https://arxiv.org/pdf/1708.05070.pdf
#meta #arxiv #ml
Neural net for removing copyright marks.
https://www.theverge.com/2017/8/18/16162108/google-research-algorithm-watermark-removal-photo-protection
#cv #dl #google
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs.
Now mankind can generate content for social networks without taking photoes.
Github: https://github.com/NVIDIA/pix2pixHD
Arxiv: https://arxiv.org/pdf/1711.11585.pdf
AI index report, demonstrating hype around AI techonologies: https://aiindex.org/2017-report.pdf
Читать полностью…#DeepLearning predicts when patients die with Average Precision 0.69 (that’s high).
Andrew Ng announced new project in his twitter: ML to help prioritize palliative (end-of-life) care. Model uses an 18-layer Deep Neural Network that inputs the EHR data of a patient, and outputs the probability of death in the next 3-12 months.
The trained model achieves an AUROC score of 0.93 and an Average Precision score of 0.69 on cross validation.
Site: https://stanfordmlgroup.github.io/projects/improving-palliative-care/
Arxiv: https://arxiv.org/abs/1711.06402
#project #DSinthewild #casestudy
StarGAN — a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model.
GitHub: https://github.com/yunjey/StarGAN
Arxiv: https://arxiv.org/abs/1711.09020
#deeplearning #gan #cv
Realtime object detection by Google.
https://research.googleblog.com/2017/11/automl-for-large-scale-image.html
YouTube demo: https://www.youtube.com/watch?time_continue=70&v=ERglPgx8wFg
#deeplearning #google #caption #detection
An article about #BigBrother. How Facebook is able to track users interests based on 3 likes.
Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals
http://online.liebertpub.com/doi/full/10.1089/big.2017.0074
Imitation learning for structured prediction in natural language processing
https://sheffieldnlp.github.io/ImitationLearningTutorialEACL2017
#nlp #tutorial
Release of a nice NLP-processing library.
https://www.techleer.com/articles/404-spacy-20-released-natural-language-processing-with-python/
#nlp #python
Great example of feature visualisation
https://distill.pub/2017/feature-visualization/
ARkit Sudoku solver built with CoreML
https://blog.prototypr.io/behind-the-magic-how-we-built-the-arkit-sudoku-solver-e586e5b685b0
#ar #cv #keras #coreml
Another breakthrough with generative models.
BEGAN: Boundary Equilibrium Generative Adversarial Networks
https://arxiv.org/abs/1703.10717
#gan #cv
Netflix shared its recommendation engine scheme:
https://medium.com/netflix-techblog/distributed-time-travel-for-feature-generation-389cccdd3907
#ml #rs #spark #hadoop
Architecture for real-time scene annotation (BlitzNet)
http://thoth.inrialpes.fr/research/blitznet/
ArxiV: https://arxiv.org/abs/1708.02813
GitHub: https://github.com/dvornikita/blitznet
#ICCV #github #dl #video
Beautiful thematic maps with ggplot2
https://timogrossenbacher.ch/2016/12/beautiful-thematic-maps-with-ggplot2-only/
#viz #ggplot #maps