ML track at YaTalks 2022

YaTalks, Yandex’s main conference for the IT community, will be held on December 3 and 4. More than 100 tech experts from around the globe will gather to discuss technology and life in today’s ever-changing world. In the program, there are tracks about backend, frontend, mobile development, and, of course, machine learning.

Speakers will discuss:
• what significant events have happened in the sphere of machine learning for the last 10 years;
• how neural network-driven translation works;
• how generative neural networks create pictures and whether they are able to replace illustrators;
• and many other topical issues.

This year YaTalks will be streamed simultaneously in two languages — Russian and English — using neural network-driven voice-over translation technologies. The conference is online, so you can join it from anywhere in the world.

Learn more and register on the website

Best Python Concurrency Guides

- https://superfastpython.com/multiprocessing-in-python/
- https://superfastpython.com/python-asyncio/
- https://superfastpython.com/multiprocessing-pool-python/
- https://superfastpython.com/threadpool-python/

They are a bit bloated and explain the same concepts 10 times, but they try to explain the most unexplored parts of Python in detail in plain language with examples.

You can just read examples and intro.

Good stuff.

Dear all,

Our friends are organizing AI & Natural Language conference in Yerevan next year, 21-22 April 2023. Guys are open for collaboration, if you want to organize a workshop on a thriving topic or a challenge, please contact them. All the info is in their channel: https://t.iss.one/ainlconf

Top Python libraries `22
by @tryolabs

link: https://tryolabs.com/blog/2022/12/26/top-python-libraries-2022

#python #tools

GPT-3 for self-therapy

Just came across an interesting article about using #GPT-3 to analyze past journal entries and summarize therapy sessions for gaining new perspectives on personal struggles. Dan Shipper loaded person journal into the neural network so he could ask different questions, including asking about his own Myers-Briggs personality type (INTJ for those who wondered).

It's a powerful example of how AI tools can help individuals become more productive, effective, and happy. As we continue to see the integration of #AI in various industries, it's important for modern blue collar workers to learn how to properly work with these tools in order to stay at the peak of efficiency.

Let's embrace the future and learn to use AI to our advantage rather than to spread FUD about AI replacing workforce. It won’t but it will enable some people to achieve more and be way more productive.

Link: https://every.to/chain-of-thought/can-gpt-3-explain-my-past-and-tell-me-my-future

#aiusecase #toolsnotactors

​​StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis

In this paper, the authors propose StyleGAN-T, a model designed for large-scale text-to-image synthesis. With its large capacity, stable training on diverse datasets, strong text alignment, and controllable variation-text alignment tradeoff, StyleGAN-T outperforms previous GANs and even surpasses distilled diffusion models, the previous frontrunners in fast text-to-image synthesis in terms of sample quality and speed.

StyleGAN-T achieves a better zero-shot MS COCO FID than current state of-the-art diffusion models at a resolution of 64×64. At 256×256, StyleGAN-T halves the zero-shot FID previously achieved by a GAN but continues to trail SOTA diffusion models.

Paper: https://arxiv.org/abs/2301.09515

Project link: https://sites.google.com/view/stylegan-t?pli=1

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-stylegan-t

#deeplearning #cv #gan #styletransfer

​​Cut and Learn for Unsupervised Object Detection and Instance Segmentation

CutLER (Cut-and-LEaRn) is a new approach for training unsupervised object detection and segmentation models without using any human labels. It uses a combination of a MaskCut approach to generate object masks and a robust loss function to learn a detector. The model is simple and compatible with different detection architectures and can detect multiple objects. It is a zero-shot detector, meaning it performs well without additional in-domain data and is robust against domain shifts across various types of images. CutLER can also be used as a pretrained model for supervised detection and improves performance on few-shot benchmarks. Results show improved performance over previous work, including being a zero-shot unsupervised detector and surpassing other low-shot detectors with finetuning.

Paper: https://arxiv.org/abs/2301.11320

Code link: https://github.com/facebookresearch/CutLER1

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-cutler

#deeplearning #cv #objectdetection #imagesegmentation