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In this work, we demonstrate Sentence Transformer Fine-tuning (SetFit), a simple and efficient alternative for few-shot text classification. The method is based on fine-tuning a Sentence Transformer with task-specific data and can easily be implemented with the sentence-transformers library.

Research finds that reversible Graph Neural Networks (RevGNNs) significantly outperform existing methods on multiple datasets and improve large models’ memory efficiency for AI applications.

Published October 26, 2021  Image adapted from Elentaris Photo / Shutterstock.com Gadi Singer is Vice President and Director of Emergent AI Research at Intel Labs leading the development of the third wave of AI capabilities. Cognitive AI, hierarchy of knowledge, … Continuer la lecture →

In this post, I present Tango architecture, a simple cascade student-teacher model, and exploit the simplicity of task instances to gain maximum throughput for text classification.  

To pursue this path to better AI, it is essential to understand what “understanding” really means for the human brain.

Intel and Google* have been collaborating to deliver optimized implementations of some of the most compute-intensive TensorFlow operations.

In his keynote presentation from the oneAPI DevSummit for AI 2022, Wei Li, VP/GM AI & Analytics at Intel, is joined by Jean-Luc Chatelain, CTO of Accenture Applied Intelligence to launch new AI reference kits for industry-specific use cases.

Azure Arc-Enabled Kubernetes enables centralized management of heterogenous and geographically separate Kubernetes clusters from Azure public cloud.

Learn about how Intel has been working to improve performance of popular Python* libraries

A collaboration between Intel Labs and Deci, a start-up company focused on deep learning development, is accelerating the journey toward more scalable AI.