Papers with Code 아카이브
Multi-Task Learning
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Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.
<span style="color:grey; opacity: 0.6">( Image credit: Cross-stitch Networks for Multi-task Learning )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/opennlplab/transnormerllm
https://github.com/oxcsaml2019/multitask-learning
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/pasrichashivam/hyperface_deep_multi-task-learning_keras_implementation
https://github.com/peterfws/structure-plp-slam
https://github.com/peteryuX/retinaface-tf2
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/petezh/ChatGPT-Advice
https://github.com/phoebussi/alpaca-cot
https://github.com/piaohe111/gittest
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/pisalore/roberta_results
https://github.com/plkmo/BERT-Relation-Extraction
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/polifack/codelin
Better, Faster, Stronger Sequence Tagging Constituent Parsers
https://github.com/postech-ami/smile-dataset
https://github.com/prajinkhadka/face_det_check
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/predictiveintelligencelab/jaxpi
Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective
https://github.com/pumpkinnan/BAN
Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds
https://github.com/pwc-1/Paper-10/tree/main/speech_to_text
https://github.com/pwc-1/Paper-10/tree/main/table_transformer
https://github.com/pwc-1/Paper-8/tree/main/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/pwc-1/Paper-9/tree/main/2/mistral
https://github.com/pwc-1/Paper-9/tree/main/2/mixtral
https://github.com/pwc-1/Paper-9/tree/main/4/PCGrad-mindspore-example
https://github.com/pwc-1/Paper-9/tree/main/5/xlm_roberta
https://github.com/pwc-1/Paper-9/tree/main/7/PCGrad-mindspore-example
https://github.com/pwc-1/Paper-9/tree/main/gpt_neox_japanese
https://github.com/qiaoxiu/facenetRetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/qizhipei/fabind
https://github.com/qmeng99/Multi-task-Representation-Disentanglement
Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound
https://github.com/qwopqwop200/GPTQ-for-LLaMa
https://github.com/raghavmalawat/presentationmastery
https://github.com/rajatgermany/qa-nlp
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/ramanakshay/nanogpt
https://github.com/ranandalon/mtl
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/rangwani-harsh/PC_Grad_Pytorch
https://github.com/raulsteleac/Speech_Emotion_Recognition
Attention-Augmented End-to-End Multi-Task Learning for Emotion Prediction from Speech
https://github.com/realdingke/mscai_softwareengineering_project
FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.