Code and Datasets

Bort

Adrian de Wynter, Daniel J. Perry

2020

Last updated October 9, 2023

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Bort is an optimal subset of architectural parameters for the BERT architecture, extracted by applying a fully polynomial-time approximation scheme (FPTAS) for neural architecture search. Bort has an effective (that is, not counting the embedding layer) size of 5.5% the original BERT-large architecture, and 16% of the net size. It is also able to be pretrained in 288 GPU hours, which is 1.2% of the time required to pretrain the highest-performing BERT parametric architectural variant, RoBERTa-large. It is also 7.9x faster than BERT-base (20x faster than BERT/RoBERTa-large) on a CPU, and performs better than other compressed variants of the architecture, and some of the non-compressed variants; it obtains an average performance improvement of between 0.3% and 31%, relative, with respect to BERT-large on multiple public natural language understanding (NLU) benchmarks.

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Conversational AI

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Applied scientist, Agentic AI, AWS Agentic AI

IL, Haifa

Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making.

Applied Scientist II, AWS AI Training and Resilience

US, NY, New York

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Applied Scientist, Prime Video - Content Understanding and Enrichment

US, WA, Seattle

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