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Google Research, 2022 & beyond: Algorithms for efficient deep learning

Google Research AI blog

The explosion in deep learning a decade ago was catapulted in part by the convergence of new algorithms and architectures, a marked increase in data, and access to greater compute. Using this approach, for the first time, we were able to effectively train BERT using simple SGD without the need for adaptivity.

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Understanding BERT

Mlearning.ai

Pre-training of Deep Bidirectional Transformers for Language Understanding BERT is a language model that can be fine-tuned for various NLP tasks and at the time of publication achieved several state-of-the-art results. Finally, the impact of the paper and applications of BERT are evaluated from today’s perspective. 1 Impact V.2

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Google Research, 2022 & Beyond Series

Bugra Akyildiz

We are heavy Google Research posts this week, enjoy specifically the 2022 & Beyond series! Bert paper has demos from HF spaces and Replicate. While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers.

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Google Research, 2022 & beyond: Algorithmic advances

Google Research AI blog

In 2022, we continued this journey, and advanced the state-of-the-art in several related areas. We also had a number of interesting results on graph neural networks (GNN) in 2022. We provided a model-based taxonomy that unified many graph learning methods. We also quantified the degree to which LLMs emit memorized training data.

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Google Research, 2022 & beyond: Research community engagement

Google Research AI blog

In 2022, we expanded our research interactions and programs to faculty and students across Latin America , which included grants to women in computer science in Ecuador. We also help make global conferences accessible to more researchers around the world, for example, by funding 24 students this year to attend Deep Learning Indaba in Tunisia.

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Unlock the Power of BERT-based Models for Advanced Text Classification in Python

John Snow Labs

Text classification with transformers refers to the application of deep learning models based on the transformer architecture to classify sequences of text into predefined categories or labels. BERT (Bidirectional Encoder Representations from Transformers) is a language model that was introduced by Google in 2018.

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GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

AWS Machine Learning Blog

of nodes with text-features MAG 484,511,504 7,520,311,838 4/4 28,679,392 1,313,781,772 240,955,156 We benchmark two main LM-GNN methods in GraphStorm: pre-trained BERT+GNN, a baseline method that is widely adopted, and fine-tuned BERT+GNN, introduced by GraphStorm developers in 2022. Dataset Num. of nodes Num. of edges Num.

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