Data Science by ODS.ai 🦜

2021-06-06 12:12:23 Implementing original #UNet paper using #PyTorch

Video tutorial on how to code your own neural network from scratch.

Link:


Paper: https://arxiv.org/abs/1505.04597 Open / Comment

2021-06-02 20:32:13 ​​ByT5: Towards a token-free future with pre-trained byte-to-byte models

Pre-trained language models usually operate on the sequences of tokens, which are based on words or subword units.

Token-free models operate directly on the raw text (characters or bytes) instead. They can work with any language, are more robust to the noise, and don’t require preprocessing.

The authors use a modified mT5 architecture and show that their approach is competitive with token-level models.

Paper: https://arxiv.org/abs/2105.13626
Code: https://github.com/google-research/byt5

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-byt5

#nlp #deeplearning #transformer #pretraining Open / Comment

2021-05-21 17:00:25 ​​Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence

Modern NLP models still struggle with generating long and coherent texts, especially for open-ended dialogues such as story generation. The authors of the paper suggest a new model architecture HINT (a generation model equipped with HIgh-level representations for loNg Text generation) with two pre-training objectives to improve the language generation models: predicting inter-sentence semantic similarity and distinguishing between normal and shuffled sentence orders. Experiments and ablation studies show that these improvements result in more coherent texts than state-of-the-art baselines.


Paper: https://arxiv.org/abs/2105.08963

Code: https://github.com/thu-coai/HINT

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

#deeplearning #nlp #nlg #pretraining Open / Comment

2021-05-21 13:27:42 Last Call: register to participate in the EMERGENCY DATAHACK

Online-hackathon for data-scientists and specialists in the fields of machine-learning, geography and geology.

Best solutions designed by the contestants during the event will be later utilized by the Ministry of the Russian Federation for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters (EMERCOM).

The contestants will be able to research and analyze data, for the first time provided by the Ministry. Also, the contestants will be able to work with data provided by the partners of the event: the Federal Service for Hydrometeorology (Roshydromet), the Federal Road Agency (Rosavtodor), GLONASS BDD, Tele2, Rostelecom, the Federal Water Resources Agency (Rosvodresources), the Main Directorate for Traffic Safety of Russia.

Date: May 28 – 30
Format: online
Registration: open until May 24 (the date is inclusive)

Link: https://emergencydatahack.ru

The aggregated prize fund for the event – 12 200 USD (in the national currency). Open / Comment

2021-05-21 12:21:11 Testing Python Applications with Pytest.

Pytest is a testing framework and test runner for Python. In this guide we will have a look at the most useful and common configuration and usage, including several pytest plugins and external libraries. Although Python comes with a unittest module in the standard library and there are other Python test frameworks like nose2 or Ward, pytest remains my favourite. The beauty of using simple functions instead of class hierarchies, one simple assert instead of many different assert functions, built-in parametrized testing, a nice system of fixtures and the number of available plugins makes it a pleasure to use.

#guide #testing #python #pytest Open / Comment

2021-05-14 22:26:39 From comments Open / Comment

2021-05-14 22:10:33 GAN Prior Embedded Network for Blind Face Restoration in the Wild

New proposed method allowed authors to improve the quality of old photoes

ArXiV: https://arxiv.org/abs/2105.06070
Github: https://github.com/yangxy/GPEN

#GAN #GPEN #blind_face_restoration #CV #DL Open / Comment

2021-05-14 15:48:20 Data Fest returns! And pretty soon

Starting May 22nd and until June 19th we host an Online Fest just like we did last year:

Our YouTube livestream return to a zoo-forest with and this time a bear cub! (RU)

Unlimited networking in our spatial.chat - May 22nd will be the real community maelstrom (RU & EN)

Tracks on our ODS.AI platform, with new types of activities and tons of new features (RU & EN)

Registration is live! Check out Data Fest 2021 website for the astonishing tracks we have in our programme and all the details Open / Comment

2021-05-12 14:03:06 Advancing sports analytics through AI research

Deepmind blog : https://deepmind.com/blog/article/advancing-sports-analytics-through-ai

A Dataset and Benchmarks: https://soccer-net.org/

Dataset: https://github.com/statsbomb/open-data

Paper: https://sites.google.com/view/ijcai-aisa-2021/

@ai_machinelearning_big_data Open / Comment

2021-05-12 13:53:21 Open / Comment