Data Science by ODS.ai 🦜

2021-11-15 16:35:04 READ//ABLE NLP competition(s)

Registration for the technology contests – Satellites is open.
These contests are made for a wide range of junior developers interested in natural language processing (NLP).
Contests-satellites held in a separate from the main competition READ//ABLE schedule.
The contests are series of competitions of text analysis, so the participating teams will be able to use the results of developments as a basis for participating in the main competition.

Fund: ~ 14,000 USD per sub-competition
Deadline: December 1 here
Link: https://ai.upgreat.one/satellites/

#NLP #contest Open / Comment

2021-11-13 02:21:33 ​​On Neural Rendering

What is Neural Rendering? In a nutshell, neural rendering is when we take classic algorithms for image rendering from computer graphics and replace a part of the pipeline with neural networks (stupid, but effective). Neural rendering learns to render and represent a scene from one or more input photos by simulating the physical process of a camera that captures the scene. A key property of 3D neural rendering is the disentanglement of the camera capturing process (i.e., the projection and image formation) and the representation of a 3D scene during training. That is, we learn an explicit (voxels, point clouds, parametric surfaces) or an implicit (signed distance function) representation of a 3D scene. For training, we use observations of the scene from several camera viewpoints. The network is trained on these observations by rendering the estimated 3D scene from the training viewpoints, and minimizing the difference between the rendered and observed images. This learned scene representation can be rendered from any virtual camera in order to synthesize novel views. It is important for learning that the entire rendering pipeline is differentiable.

You may have noticed, that the topic of neural rendering, including all sorts of nerfs-schmerfs, is now a big hype in computer vision. You might say that neural rendering is very slow, and you'd be right. A typical training session on a small scene with ~ 50 input photos takes about 5.5 hours for the fastest method on a single GPU, but neural rendering methods have made significant progress in the last year improving both fidelity and efficiency. To catch up on all the recent developments in this direction, I highly recommend reading this SOTA report "Advances in Neural Rendering".

The gif is from Volume Rendering of Neural Implicit Surfaces paper. Open / Comment

2021-10-29 11:30:37 Dear advertisers, who spammed @opendatasciencebot, you are kindly welcome to advertise on this channel for 1 ETH (~ $4,300).

This might seem unreasonable overpriced, but don’t fall for it — it is. We do not promote anything we won’t post here for free, because we are privileged and blessed to work on the sphere with :goodenough: compensation to put up a higher price tag on it.

Open / Comment

2021-10-12 14:00:50 ​​ Alias-Free Generative Adversarial Networks (StyleGAN3) release

King is dead! Long live the King! #StyleGAN2 was #SOTA and default standard for generating images. #Nvidia released update version, which will lead to more realistic images generated by the community.

Article: https://nvlabs.github.io/stylegan3/
GitHub: https://github.com/NVlabs/stylegan3
Colab: https://colab.research.google.com/drive/1BXNHZBai-pXtP-ncliouXo_kUiG1Pq7M

#GAN #dl Open / Comment

2021-10-11 17:41:43 ​​A Recipe For Arbitrary Text Style Transfer with Large Language Models

Text style transfer is rewriting text to incorporate additional or alternative stylistic elements while preserving the overall semantics and structure.

Large language models are trained only for continuation, but recently many approaches showed that it is possible to perform other NLP tasks by expressing them as prompts that encourage the model to output the desired answer as the continuation.

The authors present a new prompting method (augmented zero-shot learning), which frames style transfer as a sentence rewriting task and requires only natural language instruction.

There are many great examples in the paper and on the project page - both formal and informal.
For example, "include the word "oregano"" and "in the style of a pirate".

Paper: https://arxiv.org/abs/2109.03910
Code: https://storage.googleapis.com/style-transfer-paper-123/index.html

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

#deeplearning #nlp #styletransfer Open / Comment

2021-10-10 12:20:40 Entropy and complexity unveil the landscape of memes evolution

Sunday research about how memes evolved from 2011 to present.
TLDR: memes are getting more complex and require more contextual knowledge to understand.

Link: https://www.nature.com/articles/s41598-021-99468-6
Data: https://github.com/cdcslab/MemesEvolution

#memes #openresearch Open / Comment

2021-10-07 14:11:33 AI for Earth Monitoring course

Course is about how to apply data science to datasets of Earth images collected by satellites. This course would benefit people interested in jumping into the real world application and working with real Earth observation image data.

Start date: 18 Oct. 2021
Duration: 6 weeks
Cost: Free
Link: https://bit.ly/3lerMti Open / Comment

2021-10-06 18:19:18 We Have Published a Model For Text Repunctuation and Recapitalization

The model works with SINGLE sentences (albeit long ones) and:

- Inserts capital letters and basic punctuation marks (dot, comma, hyphen, question mark, exclamation mark, dash for Russian);
- Works for 4 languages (Russian, English, German, Spanish) and can be extended;
- By design is domain agnostic and is not based on any hard-coded rules;
- Has non-trivial metrics and succeeds in the task of improving text readability;

Links:

- Model repo - https://github.com/snakers4/silero-models#text-enhancement
- Colab notebook - https://colab.research.google.com/github/snakers4/silero-models/blob/master/examples_te.ipynb
- Russian article - https://habr.com/ru/post/581946/
- English article - https://habr.com/ru/post/581960/ Open / Comment

2021-10-06 12:31:58 It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning

Researchers from #Yandex have discovered that the reasoning capabilities of cross-lingual Transformers are concentrated in a small set of attention heads. A new multilingual dataset could encourage research on commonsense reasoning in Russian, French, Chinese and other languages.

Link: https://research.yandex.com/news/a-few-attention-heads-for-reasoning-in-multiple-languages

ArXiV: https://arxiv.org/abs/2106.12066

#transformer #nlu #nlp Open / Comment

2021-10-06 12:00:23 RoBERTa English Toxicity Classifier

We have released our fine-tuned RoBERTa based toxicity classifier for English language on :
https://huggingface.co/SkolkovoInstitute/roberta_toxicity_classifier

The model was trained on the merge of the English parts of the three datasets by Jigsaw. The classifiers perform closely on the test set of the first Jigsaw competition, reaching the AUC-ROC of 0.98 and F1-score of 0.76.
So, you can use it now conveniently for any of your research or industrial tasks Open / Comment