https://github.com/maxuewei2/RiWalk
공식 코드RiWalk: Fast Structural Node Embedding via Role Identification
Papers with Code 아카이브
Transfer Learning
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Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.
<span style="color:grey; opacity: 0.6">( Image credit: Subodh Malgonde )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mbaityje/plankifier
공식 코드https://github.com/mbosc/twf
공식 코드https://github.com/mbzuai-nlp/arabicmmlu
공식 코드ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic
https://github.com/mbzuai-nlp/sttatts
공식 코드https://github.com/mc-cv/cigf
공식 코드Compressed Interaction Graph based Framework for Multi-behavior Recommendation
https://github.com/mc-cv/pkef
공식 코드Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation
https://github.com/mcanyesilli/WPT_EEMD_ML_Machining
공식 코드https://github.com/mcd4874/neurips_competition
공식 코드https://github.com/mcjacktang/fusionvitals
공식 코드Non-Contact Health Monitoring During Daily Personal Care Routines
https://github.com/mdelhoneux/machamp-worst_case_acl
공식 코드Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning
https://github.com/mdimiccoli/gcrl-for-hms
공식 코드Graph Constrained Data Representation Learning for Human Motion Segmentation
https://github.com/mdswyz/dmd
공식 코드https://github.com/mechrev0/mudpt
공식 코드MuDPT: Multi-modal Deep-symphysis Prompt Tuning for Large Pre-trained Vision-Language Models
https://github.com/med-air/endo-fm
공식 코드Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-train
https://github.com/mediabrain-sjtu/ming
공식 코드https://github.com/median-research-group/libmtl
공식 코드https://github.com/medicl-vu/promise
공식 코드Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models
https://github.com/megagonlabs/doduo
공식 코드https://github.com/meghdadk/DDUp
공식 코드Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data
https://github.com/megvii-research/petr
공식 코드PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
https://github.com/mehdi-mirzapour/emotional_speech_recognition
공식 코드Emotional Speech Recognition with Pre-trained Deep Visual Models
https://github.com/mehmetaaygul/hardware-problem-identification
공식 코드Identification of Distorted RF Components via Deep Multi-Task Learning
https://github.com/meiyan88/mcnet
공식 코드https://github.com/melsunny/keras-fcn
공식 코드Rethinking the transfer learning for FCN based polyp segmentation in colonoscopy
https://github.com/mendicant04/omega
공식 코드Imbalanced Open Set Domain Adaptation via Moving-threshold Estimation and Gradual Alignment
https://github.com/menelaoskanakis/RCM
공식 코드Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference
https://github.com/menggedu/autoke
공식 코드AutoKE: An automatic knowledge embedding framework for scientific machine learning
https://github.com/mengjieshi/learning-based-sos-correction-us-pa
공식 코드https://github.com/menoRashid/animal_human_kp
공식 코드Interspecies Knowledge Transfer for Facial Keypoint Detection
https://github.com/mercari/mercari-ml-merrec-pub-us
공식 코드MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems
https://github.com/meryemmhamdi1/meta_cross_nlu_qa
공식 코드https://github.com/meryemmhamdi1/x-continuous-learning
공식 코드https://github.com/metabo-iclr2020/MetaBO
공식 코드Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
https://github.com/metagol/metagol
공식 코드https://github.com/meteahishali/ada-net
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.