Search Results for author: David R. So

Found 7 papers, 6 papers with code

Primer: Searching for Efficient Transformers for Language Modeling

3 code implementations17 Sep 2021 David R. So, Wojciech Mańke, Hanxiao Liu, Zihang Dai, Noam Shazeer, Quoc V. Le

For example, at a 500M parameter size, Primer improves the original T5 architecture on C4 auto-regressive language modeling, reducing the training cost by 4X.

Language Modelling

Pay Attention to MLPs

20 code implementations NeurIPS 2021 Hanxiao Liu, Zihang Dai, David R. So, Quoc V. Le

Transformers have become one of the most important architectural innovations in deep learning and have enabled many breakthroughs over the past few years.

Image Classification Natural Language Inference +2

MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records

no code implementations3 Feb 2021 Zhen Xu, David R. So, Andrew M. Dai

One important challenge of applying deep learning to electronic health records (EHR) is the complexity of their multimodal structure.

Neural Architecture Search

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

2 code implementations6 Mar 2020 Esteban Real, Chen Liang, David R. So, Quoc V. Le

However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks---or similarly restrictive search spaces.

AutoML

Towards a Human-like Open-Domain Chatbot

2 code implementations27 Jan 2020 Daniel Adiwardana, Minh-Thang Luong, David R. So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, Quoc V. Le

We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations.

Chatbot

The Evolved Transformer

2 code implementations30 Jan 2019 David R. So, Chen Liang, Quoc V. Le

Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models.

Machine Translation Neural Architecture Search

Classification of crystallization outcomes using deep convolutional neural networks

2 code implementations27 Mar 2018 Andrew E. Bruno, Patrick Charbonneau, Janet Newman, Edward H. Snell, David R. So, Vincent Vanhoucke, Christopher J. Watkins, Shawn Williams, Julie Wilson

The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups.

Classification General Classification

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