PyTorch demonstrates compatibility of imperative Pythonic usability with high performance and accelerator support through its runtime architecture and benchmark results.
Theano: A Python framework for fast computation of mathematical expressions
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.
representative citing papers
JWST transit observations refine ephemerides of TOI 700 d and e by an order of magnitude in period precision but yield only upper limits on exomoons larger than Ganymede due to 46 ppm correlated noise attributed to stellar granulation.
An unsupervised character-level CNN encoder with attention-based RNN decoder, trained on Clueweb09 anchor phrases, generates query reformulations that improve retrieval on TREC collections.
citing papers explorer
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch demonstrates compatibility of imperative Pythonic usability with high performance and accelerator support through its runtime architecture and benchmark results.
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The JWST Search for Earth-Luna Analogs: Upper Limits on Exomoons and Refined Ephemerides for TOI 700 d and e
JWST transit observations refine ephemerides of TOI 700 d and e by an order of magnitude in period precision but yield only upper limits on exomoons larger than Ganymede due to 46 ppm correlated noise attributed to stellar granulation.
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Learning to Reformulate the Queries on the WEB
An unsupervised character-level CNN encoder with attention-based RNN decoder, trained on Clueweb09 anchor phrases, generates query reformulations that improve retrieval on TREC collections.