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2022-01-03 17:16:21 2021 WrapUps and Summaries


Those are two technical posts summarizing the progress which were published during 2021.

Papers with Code 2021 : A Year in Review post by Papers with Code

Medium: https://medium.com/paperswithcode/papers-with-code-2021-a-year-in-review-de75d5a77b8b

Post on KDNuggers
Post: https://www.kdnuggets.com/2021/12/2021-year-review-amazing-ai-papers.html

#summary Open / Comment

2022-01-03 13:24:02 Please share other cool summary posts if you know any through @opendatasciencebot Open / Comment

2022-01-03 11:40:36 Please share other cool summary posts if you know any through @opendatasciencebot Open / Comment

2022-01-03 11:39:55 Papers with Code 2021 : A Year in Review

https://www.kdnuggets.com/2021/12/2021-year-review-amazing-ai-papers.html

https://medium.com/paperswithcode/papers-with-code-2021-a-year-in-review-de75d5a77b8b

@ai_machinelearning_big_data Open / Comment

2021-12-30 18:05:51 All the reactions had been enabled for @opendatascience Open / Comment

2021-12-29 11:36:30 Stats by @TGStat_Bot Open / Comment

2021-12-16 20:48:08 ​​Perceiver IO: a scalable, fully-attentional model that works on any modality

#HuggingFace added neural network which is capable of working on all kinds of modailities: text, images, audio, video, coordinates, etc to the transformers library.

Blog: https://huggingface.co/blog/perceiver Open / Comment

2021-12-10 16:50:36 We continue to conquer the time series together with ETNA! Using our library, we built a model to predict the number of new COVID-19 cases in different countries. You can see the results we got in our recent article on Medium: Forecasting with ETNA - Fast and Furious. The article also shows in detail what a typical forecasting pipeline looks like and how you can quickly get a good baseline for a specific dataset. For all questions and suggestions - welcome to ETNA Community in Telegram. For all news related to AI/ML at Tinkoff — stay tuned to this channel. Open / Comment

2021-12-10 16:21:12 ​​NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

This paper presents a new participatory Python-based natural language augmentation framework that supports the creation of transformations (modifications to the data) and filters (data splits according to specific features).

The current version of the framework contains 117 transformations and 23 filters for a variety of natural language tasks.

The authors demonstrate the efficacy of NL-Augmenter by using several of its transformations to analyze the robustness of popular natural language models.

Paper: https://arxiv.org/abs/2112.02721
Code: https://github.com/GEM-benchmark/NL-Augmenter

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

#deeplearning #nlp #augmentation #robustness Open / Comment

2021-12-08 16:19:26 New V3 Silero VAD is Already Here

Main changes

- One VAD to rule them all!
- New model includes the functionality of all of the previous ones with improved quality and speed!
- As far as we know, our VAD is the best in the world now;
- Flexible sampling rate, 8000 Hz and 16000 Hz are supported;
- Flexible chunk size, minimum chunk size is just 30 milliseconds!
- Only 100k parameters;
- GPU inference and batching are supported (the model is small, so we decided not to publish a quantized model);
- Radically, drastically simplified examples;

We also drastically polished and simplified README, wiki and repo in general.

Links:

- Silero VAD repo - https://github.com/snakers4/silero-vad
- The migration to V3 is quite simple, here are some examples
- Quality metrics
- Performance metrics
- Examples and dependencies
- Colab with examples

If you like Silero VAD, please give us a and spread the news! Open / Comment