https://www.linkedin.com/jobs/view/3346079750

تویتر یک موقعیت شغلی ریموت داده که اصلا خوراک ایرانیاس که تویتهایی رو شناسایی و ازش حذف کنن و افرادی ک نباید باشند رو محدود کنن😂

پ.ن: ببینم چکار میکنین 😂😜
پ.ن: تویتر موقعیتهای شغلی Remote زیادی رو اعلام کرده که میتونین براساس چیزی ک پژوهش کردید و سابقه دارین اقدام کنید

آموزش رایگان یادگیری ماشین از دکتر شریفی زارچی استاد دانشگاه شریف

https://mktb.me/b1zp

#آموزش_کلاسی #منابع #یادگیری_ماشین #فیلم

✳️ @AI_Python

This week was dominated by molecular ML and drug discovery events:

- Broad Institute published a YouTube playlist of talks from the recent Machine Learning and Drug Discovery Symposium and leading drug discovery researchers.

- ELLIS Machine Learning for Drug Discovery Workshop will take place online in Zoom and GatherTown on Nov 28th, registration is free!

- Valence Discovery launched a blog platform for Drug Discovery related posts, the inaugural post by Clemens Isert talks about Quantum ML for drug-like molecules.

And a new work from GemNet authors: How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? The work supports a recent line of works on ML force fields particularly focusing on hot dynamics where low test MAE does not necessarily correspond to good simulations. The most important robustness factor seems to be more training data!

Molecular dynamics is one of the booming Geometric DL areas where equivariant models show the best qualities. The two cool recent papers on that topic:

⚛️ Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations by Fu et al. introduces a new benchmark for molecular dynamics - in addition to MD17, the authors add datasets on modeling liquids (Water), peptides (Alanine dipeptide), and solid-state materials (LiPS). More importantly, apart from Energy as the main metric, the authors consider a wide range of physical properties like Stability, Diffusivity, and Radial Distribution Functions. Most SOTA molecular dynamics models were probed including SchNet, ForceNet, DimeNet, GemNet (-T and -dT), NequIP.

Density Functional Theory (DFT) calculations are one of the main workhorses of molecular dynamics (and account for a great deal of computing time in big clusters). DFT is O(n^3) to the input size though, so can ML help here? Learned Force Fields Are Ready For Ground State Catalyst Discovery by Schaarschmidt et al. present the experimental study of models of learned potentials - turns out GNNs can do a very good job in O(n) time!
🪐 For astrophysics aficionados: Mangrove: Learning Galaxy Properties from Merger Trees by Jespersen et al. apply GraphSAGE to merger trees of dark matter to predict a variety of galactic properties like stellar mass, cold gas mass, star formation rate, and even black hole mass. The paper is heavy on the terminology of astrophysics but pretty easy in terms of GNN parameterization and training. Mangrove works 4-9 orders of magnitude faster than standard models (that is, 10 000 - 1 000 000 000 times faster). Experimental charts are pieces of art that you can hang on a wall.

🤖 Compositional Semantic Parsing with Large Language Models by Drozdov, Schärli et al. pretty much solve the compositional semantic parsing task (natural language query - structured query like SPARQL) using only code-davinci-002 language model from OpenAI (which is InstructGPT fine-tuned on code). No need for hefty tailored semantic parsing models - turns out a smart extension of the Chain-of-thought prompting (aka "let's think step by step") devised as Least-to-Most prompting (where we first answer easy subproblems before generating a full query) yields whopping 95% accuracy even on hardest Compositional Freebase Questions (CFQ) dataset. CFQ was introduced at ICLR 2020, and just after two years LMs cracked this task - looks like it's time for the new, even more complex dataset.

#مقاله

نگارش نسخه دوم کتاب:
Mastering Transformers
رو شروع کردیم.
دوستانی که نسخه قبلی رو خوندن و اگر موضوعی یا مطلبی به نظرتون جذاب میاد و فکر میکنید که باید اضافه بشه بفرمایید خوشحال میشم نظراتتون رو بدونم.

@ai_person

SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation

🖥 Github: https://github.com/zhu-xlab/ssl4eo-s12

📝 Paper: https://arxiv.org/abs/2211.07044v1

🖥 Dataset: https://mediatum.ub.tum.de/1660427

#مقاله

✳️ @AI_Python

سلام به همه دوستان یک سایتی با عنوان 1500 در حال انتشار در کانالهای تلگرامی و اینستاگرامی هست به هیچ عنوان روی این سایت کلیک نکنید و تاکید میشود که اصلا روی این سایت کلیک نکنید که عواقب جبران ناپذیر دارد.

Apply for Ph.D. positions starting this year!

We have room for several outstanding machine learning Ph.D. students to join our elite group at the University of Cambridge.

We are looking for:

leaders who want to realise their full potential and shape the future of machine learning
explorers who think boldly and prefer pushing boundaries to sitting still
builders who see projects through to completion and want to make a real-world impact
If you’re interested, please click the URL below. Note that we have already received a lot of applications. If you’re considering applying, earlier is definitely better!

Join the van der Schaar Lab:
https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.vanderschaar-lab.com%2Fjoin&data=05%7C01%7Ctl522%40universityofcambridgecloud.onmicrosoft.com%7C25eb48cdd35849dde6d208dac1c40d86%7C49a50445bdfa4b79ade3547b4f3986e9%7C1%7C0%7C638035345048429672%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=koklpmvj4TDjLmx8OXWKFUyWVlKRA%2B%2FzuRstdljgIbI%3D&reserved=0

#اپلای

مدل زبانی خیلی بزرگ Galactica که می‌تواند:
Can summarize academic literature, solve math problems, generate Wiki articles, write scientific code, annotate molecules and proteins, and more.

To accelerate science, we open source all models including the 120 billion model with no friction. You can access them here.

galactica.org/paper.pdf

Explore and get weights: galactica.org

#مقاله

✳️ @AI_Python

Versatile Diffusion: Text, Images and Variations All in One Diffusion Model

🖥 Github

🗒 Paper

Demo

🖥 Dataset
https://huggingface.co/spaces/shi-labs/Versatile-Diffusion
#مقاله

✳️ @AI_Python

چطور پوستر تحقیقاتی بنویسی

https://twitter.com/DrasmaJabeen1/status/1592582737491554304?t=BGjESajjmn7hKROWTCUr_A&s=19

Data Science

Python Tutorial for Beginners - Learn Python Programming from Scratch
Data Science with Python for Beginners - Full Course
Image Analysis with Convolutional Neural Networks and TensorFlow

https://www.youtube.com/playlist?list=PL7mOFdpoBB6QiW3_n7aKn_eHTCCftPJLw

#علم_داده #منابع #آموزش_کلاسی #فیلم
✳️ @AI_Python

1st Two Lessons of From Deep Learning Foundations to Stable Diffusion

👉 https://www.fast.ai/posts/part2-2022-preview.html

#منابع

✳️ @AI_Python

مدلهای مبتنی بر دیفیوژن چطوری کار می‌کنند ؟


Link: https://twitter.com/iScienceLuvr/status/1592860019057250304

@ai_person

استراتژی نوشتن مقاله پژوهشی

https://www.researchgate.net/publication/317470212_Strategies_for_writing_a_research_paper?__cf_chl_rt_tk=wxodnmjyoJ3dt_p7pI109eGsYnahVfxdAfz1BA.hpKs-1668685969-0-gaNycGzNDhE

#مقاله


✳️ @AI_Python

انتشار کورس رایگان پایتون دانشگاه هاروارد
🔰 فیلم کلاس

🔰 سایت استاد

#پایتون #منابع #کلاس_آموزشی #فیلم
#Python

✳️ @AI_Python

جلسه اول از ارایه کتاب

designing machine learning systems

در حال حاضر درحال برگزاری است لطفا جوین بشید .

https://meet.google.com/nzj-pyds-wvv

پی‌نوشت: مال یه سری از بچه هاست دوست داشتید ببینید.

Hugging face با همکاری آرکایو
قراره قابلیت دمو مدل و کارهای مربوط به مقاله را به آرکایو اضافه بکنند:
https://huggingface.co/blog/arxiv

@ai_person