Data Science by ODS.ai 🦜

2022-03-11 16:09:10 StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis

Github: https://github.com/facebookresearch/StyleNeRF

Video: http://jiataogu.me/style_nerf

Paper: https://arxiv.org/abs/2110.08985

Project: http://jiataogu.me/style_nerf/

Dataset: https://github.com/facebookresearch/StyleNeRF#dataset

@ai_machinelearning_big_data Open / Comment

2022-03-07 14:45:55 This meme is stolen. Open / Comment

2022-03-03 15:36:50 Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study


Some time ago in a different world one of the channel editors shared permmission to use data from sleep & activity tracker Oura Ring to develop an algorithm for COVID-19 prediction.

Results of this study continue to arrive. Today team shared the second manuscript from the first TemPredict Study in Nature Scientific Reports. This manuscript details an algorithm designed to detect COVID-19 using data from the Oura Ring. Alogirthm publication: www.nature.com/articles/s41598-022-07314-0 

The first publication from the first TemPredict Study will continue to be available online for you to access at any time, at this link: https://www.nature.com/articles/s41598-020-78355-6

The first publication from the second TemPredict Study (correlations between data from the Oura Ring and data from a LabCorp antibody blood test) will also continue to be available online for you to access at any time, at this link: https://www.mdpi.com/2076-393X/10/2/264

That's the power of the international collaboration

#oura #covid #biolearning #medical #health Open / Comment

2022-02-26 00:08:55 Models based on graphs are quite important for a lot of tasks in NLP. There is an overview from Michael Bronstein about what he is expecting for upcoming year for the Graph ML field:

1. Geometry becomes increasingly important in ML.
2. Message passing is still the dominant paradigm in GNNs.
3. Differential equations give rise to new GNN architectures.
4. Old ideas from Signal Processing, Neuroscience, and Physics get a new life.
5. Modeling complex systems requires going beyond graphs.
6. Reasoning, axiomatisation, and generalisation are still big open questions in Graph ML.
7. Graphs become increasingly popular in Reinforcement Learning, but probably still have a way to go.
8. AlphaFold 2 is a triumph of Geometric ML and a paradigm shift in structural biology.
9. Drug discovery and design benefits from GNNs and their confluence with Transformers.
10. AI-first drug discovery is increasingly using Geometric and Graph ML.
11. Quantum ML benefits from graph-based methods.

[link] Open / Comment

2022-02-20 22:36:51 How Useful is Self-Supervised Pretraining for Visual Tasks?

A relatively old paper (CVPR2020), by our fast life standards. Nevertheless, it has a pair of practical takeaways.

Authors created a synthetic dataset with several degrees of freedom to vary difficulty. It varies from almost monochrome objects to randomized textures and positioning on image.

The target was to compare how good different self-supervised approaches help to tune for different downstream tasks. From classification to depth estimation.

Two practical takeways are:
1. The self-supervised method utility is wildly dependent on task, markup amount and even data complexity.
2. A linear evaluation score, so popular in papers, has almost no correlation with actual fine-tuning results.

Authors found out, that there is no improvement by self-supervised training when lots of labeled data presented (which became kinda well known since then). Based on this, they hypothesise, that improvement of SSL pre-training is rather kind of a regularization than optimization. That is, SSL pre-training helps to find wider optimum, not better. Though, to claim this, some kind of loss plane investigation would be more helpful.

Source: here Open / Comment

2022-02-20 11:04:18 One Voice Detector to Rule Them All

A brief English article about our VAD got released on The Gradient!

Please follow the link to learn:

- Which values we did pursue;
- Why we decided to create our own VAD;
- Which criteria and metrics we optimized;
- A brief overview of what is available in general;
- How it compares with well-established and similar class solutions;

Links:

- The article https://thegradient.pub/one-voice-detector-to-rule-them-all/
- The VAD is always available on Github (please give us a ) here - https://github.com/snakers4/silero-vad

PS

- Also new features probably will be reserved for later quarters, but you can vote here
- Also you can find a Russian article here Open / Comment

2022-02-16 12:59:36 OCTIS : Optimizing and Comparing Topic Models is Simple!

Github: https://github.com/mind-Lab/octis

Paper: https://arxiv.org/abs/2202.07631v1

Dataset: https://paperswithcode.com/dataset/20-newsgroups

@ai_machinelearning_big_data Open / Comment

2022-02-09 20:32:06 AlphaCode Explained: AI Code Generation

AlphaCode is DeepMind's new massive language model for generating code. It is similar to OpenAI Codex, except for in the paper they provide a bit more analysis. The field of NLP within AI and ML has exploded get a lot more papers all the time. This video can help you understand how AlphaCode works and what some of the key takeaways are.


youtube:


blog post: https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode
paper: https://storage.googleapis.com/deepmind-media/AlphaCode/competition_level_code_generation_with_alphacode.pdf Open / Comment

2022-02-07 08:42:32 ​​Simple book about #ML — Machine Learning Simplified

The main purpose of the book is to build an intuitive understanding of how algorithms work through basic examples. In order to understand the presented material, it is enough to know basic mathematics and linear algebra.

After reading this book, you will know the basics of supervised learning, understand complex mathematical models, understand the entire pipeline of a typical ML project, and also be able to share your knowledge with colleagues from related industries and with technical professionals.

And for those who find the theoretical part not enough - the book is supplemented with a repository on GitHub, which has Python implementation of all methods and algorithms described in chapters.

Book is absolutely free to read.

Link: themlsbook.com

#wheretostart #book Open / Comment

2022-02-06 12:30:12 ​​There had been less posts than usual as you might have noticed, only because editor-in-chief's (mine) attention been directed to DeFi space in general and NFT in particular. However once involved with the beauty of AI and art, one can't just exit it, so… Open / Comment