Tips & Tricks on Image Generation
Generating images with AI tools is a skill, which can be improved and enhanced. So here is couple of articles, covering tips & tricks on how to generate better images with #midjourney. Most interesting one is #huggingface prompt generator, which uses #NLP model to generate sample prompts.
As an example, we tried to reproduce and improve our group avatar, following ideas in the articles. Prompt for an illustration to this post was generated with query ferrofluids in form of a brain, beautiful connections chaos, swirling black network --ar 3:4 --iw 9 --q 2 --s 1250
Midjourney Prompt Generator: https://huggingface.co/spaces/doevent/prompt-generator
List of Midjourney prompts: https://www.followchain.org/midjourney-prompts/
An advanced guide to writing prompts for Midjourney ( text-to-image): https://medium.com/mlearning-ai/an-advanced-guide-to-writing-prompts-for-midjourney-text-to-image-aa12a1e33b6
#visualization #gan #generation #generatinveart #aiart #artgentips
#events : ML-тренировка
Когда: 17 (четверг) ноября 2022, 19:00 - 21:30 (сбор с 18:00)
Место: офис Яндекса (Москва, улица Льва Толстого, 16) + онлайн
Язык - русский
В этот раз нас ждёт 3 доклада:
- призер только что завершившегося Yandex ML Cup,
- 2ое место хакатона AgroCode Hack по анализу спутниковых снимков для виноградников
- организатор ML соревнований в информационной безопасности
Подробная программа по ссылке ниже
Будем рады видеть всех очно и онлайн ;)
Регистрация обязательна
Amos: An Adam-style Optimizer with Adaptive Weight Decay towards Model-Oriented Scale
Amos is a new optimizer that we propose to pre-train large language models. It is more efficient and converges faster than AdamW: ≤ 51% memory for slot variables, and better valid loss within ≤ 70% training time!Amos is a new optimizer that we propose to pre-train large language models. It is more efficient and converges faster than AdamW: ≤ 51% memory for slot variables, and better valid loss within ≤ 70% training time!
ArXiV: https://arxiv.org/abs/2210.11693
#NLU #NLP #optimizer
State of AI Report 2022
TLDR: We are moving forward and effective international collaboration is the key to progress.
Major Themes:
* New independent research labs are rapidly open sourcing the closed source output of major labs
* Safety is gaining awareness among major AI research entities
* The China-US AI research gap has continued to widen
* AI-driven scientific research continues to lead to breakthroughs
Website: https://www.stateof.ai
#report #stateofai #AI
“Listing Embeddings for Similar Listing Recommendations and Real-time Personalization in Search”
From #Airbnb team
https://medium.com/airbnb-engineering/listing-embeddings-for-similar-listing-recommendations-and-real-time-personalization-in-search-601172f7603e
Unfortunately, discrimination against ML competition participants becomes more frequent. CrowdANALYTIX recently launched a competition that simply bans different countries from opportunity to participate, this time including Russia.
Spread the word so that we could make Data Science and ML more open, without obsolete discriminatory rules on competition platforms:
https://www.facebook.com/DataChallenges/photos/a.136318350296824.1073741827.136313013630691/182693245659334/?type=3&theater
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
🔥Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding
TLDR: Scientists kinda learned how to read thoughts. Paper on the reconstruction of the visual stimuli based on fMRI readings.
Website: https://mind-vis.github.io
Github: https://github.com/zjc062/mind-vis
#fMRI #visualstimulireconstruction #mindreading #dl
Reinforcement learning course from MIPT.
The course consists of:
- Theoretical and practical material for beginners and advanced users
- Classical approaches based on utility functions and strategy gradient, as well as modern trends in improving the efficiency of the study of the environment, interaction with planning, using memory and hierarchical approaches.
- The best of David Silver's lectures, Sutton and Barto's book, and OpenAI, DeepMind works and articles from 2019-2022.
Materials:
- PDF slides and video lectures on each topic, Colab master classes and video lectures in Russian.
Course: https://clck.ru/32a3c9
If you are interested in an internship at MIPT in the areas of Reinforcement Learning, Computer Vision, Robotics or Self Driving Cars, you can apply here: https://cogmodel.mipt.ru/internship
Nature has published an article with a #superresolution approach for #CT scans.
https://www.sciencedaily.com/releases/2018/03/180321155324.htm
#arxiv: https://arxiv.org/abs/1704.08841
Graph shows what people really mean when they use vague terminology describing the probability of an event.
Читать полностью…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