arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv
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Repositórios
Repositórios de google-research
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Efficient Batched Reinforcement Learning in TensorFlow
(963 stars) (154 forks) (1 issue indexada) (1 good first issue aberta)
google-research/bertPython
TensorFlow code and pre-trained models for BERT
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google-research/bigbirdPython
Transformers for Longer Sequences
(522 stars) (96 forks) (0 issues indexadas) (0 good first issues abertas)
(126 stars) (7 forks) (0 issues indexadas) (0 good first issues abertas)
google-research/dex-langHaskell
Research language for array processing in the Haskell/ML family
(1.681 stars) (116 forks) (4 issues indexadas) (4 good first issues abertas)
google-research/fooling-feature-visualizationsJupyter Notebook
Code for "Don't trust your eyes: on the (un)reliability of feature visualizations" (ICML 2024)
(34 stars) (4 forks) (0 issues indexadas) (0 good first issues abertas)
google-research/kubricJupyter Notebook
A data generation pipeline for creating semi-realistic synthetic multi-object videos with rich annotations such as instance segmentation masks, depth maps, and optical flow.
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google-research/languagePython
Shared repository for open-sourced projects from the Google AI Language team.
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(305 stars) (20 forks) (0 issues indexadas) (0 good first issues abertas)
A reimplementation of "The Lottery Ticket Hypothesis" (Frankle and Carbin) on MNIST.
(701 stars) (141 forks) (0 issues indexadas) (0 good first issues abertas)
Fast & Simple Resource-Constrained Learning of Deep Network Structure
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google-research/perchPython
(363 stars) (69 forks) (0 issues indexadas) (0 good first issues abertas)
Tooling for agile modeling on large machine perception embedding databases.
(115 stars) (31 forks) (0 issues indexadas) (0 good first issues abertas)
(684 stars) (60 forks) (0 issues indexadas) (0 good first issues abertas)
google-research/planetPython
Learning Latent Dynamics for Planning from Pixels
(1.143 stars) (205 forks) (0 issues indexadas) (0 good first issues abertas)
google-research/seed_rlPython
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
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Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"
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