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Learning to Execute
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/deepmind/clrs
공식 코드https://github.com/google-research/google-research/tree/master/ipagnn
공식 코드Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
https://github.com/google-research/runtime-error-prediction
공식 코드https://github.com/ischubert/l2e
공식 코드Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics
https://github.com/mirjanic/nar-latent-spaces
공식 코드Latent Space Representations of Neural Algorithmic Reasoners
https://github.com/rajasekharmekala/query-rephrasing-subtask-cot
공식 코드EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning
https://github.com/sjtuytc/Neurips21-ProTo-Program-guided-Transformers-for-Program-guided-Tasks
공식 코드https://github.com/tensorflow/tensor2tensor
공식 코드https://github.com/wojciechz/learning_to_execute
공식 코드https://github.com/yizhangzzz/transformers-lego
공식 코드Unveiling Transformers with LEGO: a synthetic reasoning task
https://github.com/zhengxiangshi/learntoask
공식 코드https://github.com/MostafaDehghani/bAbI-T2T
https://github.com/andreamad8/Universal-Transformer-Pytorch
https://github.com/btc-room101/bitcoin-rnn
https://github.com/cfiken/universal_transformer
https://github.com/dsmic/LearnMultiplyByHand
https://github.com/google-deepmind/clrs
https://github.com/kpot/keras-transformer
https://github.com/lcary/keras-program-induction
https://github.com/manyunya/bitcoin-rnn
https://github.com/mdjunior/coc891
https://github.com/moon23k/Transformer_Archs
https://github.com/sidney1505/arc_maml_transformer
https://github.com/ypeleg/MinimalIsAllYouNeed
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.