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Meta-Learning
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Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.
<span style="color:grey; opacity: 0.6">( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/moonsh/hm-hybrid-masking
공식 코드https://github.com/moonsh/msi-maximize-support-set-information
공식 코드MSI: Maximize Support-Set Information for Few-Shot Segmentation
https://github.com/morningstarwang/MetaTTE
공식 코드https://github.com/moskomule/hypergrad
공식 코드Nystrom Method for Accurate and Scalable Implicit Differentiation
https://github.com/moukamisama/f2m
공식 코드Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima
https://github.com/movinghoon/ESFR
공식 코드Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification
https://github.com/mpatacchiola/contextual-squeeze-and-excitation
공식 코드Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
https://github.com/mpatacchiola/imujoco
공식 코드Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
https://github.com/mprompting/xlmrprompt
공식 코드https://github.com/mrkshllr/FewTURE
공식 코드https://github.com/mrusci/ondevice-fewshot-kws
공식 코드Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems
https://github.com/mrvoh/meta_learning_multilingual_doc_classification
공식 코드Multilingual and cross-lingual document classification: A meta-learning approach
https://github.com/msiam/tti_fsvos
공식 코드Temporal Transductive Inference for Few-Shot Video Object Segmentation
https://github.com/mtensor/rulesynthesis
공식 코드https://github.com/muraliadithya/vdp
공식 코드Composing Neural Learning and Symbolic Reasoning with an Application to Visual Discrimination
https://github.com/murphyzc/metakan
공식 코드Improving Memory Efficiency for Training KANs via Meta Learning
https://github.com/mustafa1728/wlsegnet
공식 코드A Language-Guided Benchmark for Weakly Supervised Open Vocabulary Semantic Segmentation
https://github.com/mxhofer/Named-Entity-Recognition-BidirectionalLSTM-CNN-CoNLL
공식 코드Few-shot Learning for Named Entity Recognition in Medical Text
https://github.com/mxu34/mbrl-gpmm
공식 코드Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
https://github.com/myhakureimu/prerec
공식 코드Pre-trained Recommender Systems: A Causal Debiasing Perspective
https://github.com/myungsub/meta-interpolation
공식 코드https://github.com/n3il666/meta-dmoe
공식 코드Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
https://github.com/nabenabe0928/meta-learn-tpe
공식 코드https://github.com/nacharki/multiplet-metalearners
공식 코드Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects
https://github.com/nadavlab/MedConceptsQA
공식 코드https://github.com/naeem-paeedeh/adapter
공식 코드Cross-Domain Few-Shot Learning via Adaptive Transformer Networks
https://github.com/naiveneuron/wikigoldsk
공식 코드https://github.com/namednil/sip
공식 코드SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation
https://github.com/namednil/step
공식 코드Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations
https://github.com/namespace-pt/ultragist
공식 코드https://github.com/namkoong-lab/PersonalLLM
공식 코드https://github.com/namyongpark/metagl
공식 코드MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning
https://github.com/naruya/vaxnerf
공식 코드VaxNeRF: Revisiting the Classic for Voxel-Accelerated Neural Radiance Field
https://github.com/nasaharvest/timl
공식 코드https://github.com/naszilla/reczilla
공식 코드On the Generalizability and Predictability of Recommender Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.