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

Google Research AI blog

In 2022, we focused on new techniques for infusing external knowledge by augmenting models via retrieved context; mixture of experts; and making transformers (which lie at the heart of most large ML models) more efficient. This motivates new techniques to more efficiently and effectively optimize modern neural network models.

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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. First of all, we have made a variety of algorithmic advances to address the problem of training large neural networks with DP.

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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 Architecture III.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. Libraries MLCommons Algorithmic Efficiency is a benchmark and competition measuring neural network training speedups due to algorithmic improvements in both training algorithms and models.

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AI News Weekly - Issue #343: Summer Fiction Reads about AI - Jul 27th 2023

AI Weekly

techcrunch.com The Essential Artificial Intelligence Glossary for Marketers (90+ Terms) BERT - Bidirectional Encoder Representations from Transformers (BERT) is Google’s deep learning model designed explicitly for natural language processing tasks like answering questions, analyzing sentiment, and translation.

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

AWS Machine Learning Blog

We also released a comprehensive study of co-training language models (LM) and graph neural networks (GNN) for large graphs with rich text features using the Microsoft Academic Graph (MAG) dataset from our KDD 2024 paper. GraphStorm provides different ways to fine-tune the BERT models, depending on the task types. Dataset Num.

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Google at EMNLP 2022

Google Research AI blog

Posted by Malaya Jules, Program Manager, Google This week, the premier conference on Empirical Methods in Natural Language Processing (EMNLP 2022) is being held in Abu Dhabi, United Arab Emirates. We are proud to be a Diamond Sponsor of EMNLP 2022, with Google researchers contributing at all levels.