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2022: We reviewed this year’s AI breakthroughs

Applied Data Science

In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. Just wait until you hear what happened in 2022. Who should I follow? How is this even possible?

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Selective Classification Can Magnify Disparities Across Groups

The Stanford AI Lab Blog

In Proceedings of the IEEE International Conference on Computer Vision, pp. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. 590–597, 2019. ↩ ↩ 2 Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman.

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ACL 2022 Highlights

Sebastian Ruder

This was my first in-person conference since ACL 2019. This is also my first conference highlights post since NAACL 2019. The initiative focuses on making Computational Linguistics (CL) research accessible in 60 languages and across all modalities, including text/speech/sign language translation, closed captioning, and dubbing.

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Explainable AI and ChatGPT Detection

Mlearning.ai

Classifiers based on neural networks are known to be poorly calibrated outside of their training data [3]. 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics. [7] Attention is not not Explanation (2019). Attention is not Explanation. Weigreffe, Y.

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ChatGPT4 still leads ChatBot/LLM Leaderboard

Bugra Akyildiz

Natural language processing (NLP) or computational linguistics is one of the most important technologies of the information age. Through lectures, assignments and a final project, students will learn the necessary skills to design, implement, and understand their own neural network models, using the Pytorch framework.

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The State of Transfer Learning in NLP

Sebastian Ruder

This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. 2019 ) of recent years. A taxonomy that highlights the variations can be seen below: A taxonomy for transfer learning in NLP ( Ruder, 2019 ). 2019 ; Artetxe and Schwenk, 2019 ; Mulcaire et al., 2019 ; Lample and Conneau, 2019 ).

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Major trends in NLP: a review of 20 years of ACL research

NLP People

The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) is starting this week in Florence, Italy. Neural Networks are the workhorse of Deep Learning (cf. Follow us for a review of ACL 2019 and more updates on NLP trends! Neural Network Methods in Natural Language Processing.

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