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TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. Paper: A decoder-only foundation model for time-series forecasting, ICML 2024. All checkpoints: TimesFM Hugging Face Collection. Google Research blog. TimesFM in BigQuery: an official Google product. This open version is not an officially supported Google pr
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
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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
Tensor2Robot (T2R) is a library for training, evaluation, and inference of large-scale deep neural networks, tailored specifically for neural networks relating to robotic perception and control. It is based on the TensorFlow deep learning framework. A common task in robotics research involves adding a new sensor modality or new label tensor to a neural network graph. This involves 1) changing what
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