https://github.com/wasiahmad/PLBART
공식 코드Unified Pre-training for Program Understanding and Generation
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wasiahmad/avatar
공식 코드AVATAR: A Parallel Corpus for Java-Python Program Translation
https://github.com/wasiahmad/sumgentobt
공식 코드Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages
https://github.com/wchen342/SketchyGAN
공식 코드SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis
https://github.com/wcshin-git/te-vqgan
공식 코드https://github.com/weaidb/cracksql
공식 코드CrackSQL: A Hybrid SQL Dialect Translation System Powered by Large Language Models
https://github.com/weepiess/StyleFlow-Content-Fixed-I2I
공식 코드https://github.com/weichenfan/hierarchyflow
공식 코드https://github.com/weichiyao/scalaremlp
공식 코드Scalars are universal: Equivariant machine learning, structured like classical physics
https://github.com/weichuanw/llm-based-mt-via-model-editing
공식 코드https://github.com/weihaox/DCMIT
공식 코드https://github.com/weijia-xu/hallucinations-in-nmt
공식 코드Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection
https://github.com/weixiangyan/codetransocean
공식 코드CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation
https://github.com/weiyali126/TACTIC
공식 코드TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration
https://github.com/wengong-jin/iclr19-graph2graph
공식 코드Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
https://github.com/wengsyx/low-resource-text-translation
공식 코드Towards Better Chinese-centric Neural Machine Translation for Low-resource Languages
https://github.com/wesselb/NeuralProcesses.jl
공식 코드Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
https://github.com/westenfelder/NL2SH
공식 코드https://github.com/wgantt/multimuc
공식 코드https://github.com/whaleloops/interpoetry
공식 코드https://github.com/whher/nmt-de-bar
공식 코드Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Study
https://github.com/williamyang1991/fresco
공식 코드FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation
https://github.com/williamyang1991/gp-unit
공식 코드Unsupervised Image-to-Image Translation with Generative Prior
https://github.com/wingseeye/transeval-lrm
공식 코드https://github.com/wiseman/py-webrtcvad
공식 코드https://github.com/wjko2/NMT-Adapt
공식 코드Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data
https://github.com/wjmacro/continualmt
공식 코드F-MALLOC: Feed-forward Memory Allocation for Continual Learning in Neural Machine Translation
https://github.com/wjrzm/VP-MoCap
공식 코드MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensors
https://github.com/wltjr1007/FDEN
공식 코드A Plug-in Method for Representation Factorization in Connectionist Models
https://github.com/wmt-conference/ErrorSpanAnnotation
공식 코드Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation
https://github.com/wns823/NMT_SSP
공식 코드https://github.com/wojzaremba/lstm
공식 코드https://github.com/world1tree/SpeechTranslationRefinement
공식 코드https://github.com/wouterkool/stochastic-beam-search
공식 코드https://github.com/wrld/PRoGAN
공식 코드PREGAN: Pose Randomization and Estimation for Weakly Paired Image Style Translation
https://github.com/wssun/trancs
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.