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Currently, there is an astonishing amount of toil and guesswork involved in actually getting deep neural networks to work well in practice. Even worse, the actual recipes people use to get good results with deep learning are rarely documented. Papers gloss over the process that led to their final results in order to present a cleaner story, and machine learning engineers working on commercial prob
ScaNN (Scalable Nearest Neighbors) is a method for efficient vector similarity search at scale. This code implements [1, 2], which includes search space pruning and quantization for Maximum Inner Product Search and also supports other distance functions such as Euclidean distance. The implementation is designed for x86 processors with AVX2 support. ScaNN achieves state-of-the-art performance on an
AutoML-Zero aims to automatically discover computer programs that can solve machine learning tasks, starting from empty or random programs and using only basic math operations. The goal is to simultaneously search for all aspects of an ML algorithm—including the model structure and the learning strategy—while employing minimal human bias. Despite AutoML-Zero's challenging search space, evolutionar
The comparison shows that for ALBERT-base, ALBERT-large, and ALBERT-xlarge, v2 is much better than v1, indicating the importance of applying the above three strategies. On average, ALBERT-xxlarge is slightly worse than the v1, because of the following two reasons: 1) Training additional 1.5 M steps (the only difference between these two models is training for 1.5M steps and 3M steps) did not lead
Dex (named for "index") is a research language for typed, functional array processing. The goal of the project is to explore: Type systems for array programming Mathematical program transformations like differentiation and integration User-directed compilation to parallel hardware Interactive and incremental numerical programming and visualization To learn more, check out our paper, our tutorial o
This repository contains code released by Google Research. All datasets in this repository are released under the CC BY 4.0 International license, which can be found here: https://creativecommons.org/licenses/by/4.0/legalcode. All source files in this repository are released under the Apache 2.0 license, the text of which can be found in the LICENSE file. Because the repo is large, we recommend yo
There are two multilingual models currently available. We do not plan to release more single-language models, but we may release BERT-Large versions of these two in the future: BERT-Base, Multilingual Cased (New, recommended): 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters BERT-Base, Multilingual Uncased (Orig, not recommended): 102 languages, 12-layer, 768-hidden, 12-heads, 110M p
***** New March 11th, 2020: Smaller BERT Models ***** This is a release of 24 smaller BERT models (English only, uncased, trained with WordPiece masking) referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond
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