https://github.com/luck1226/multisource_deepGaussianProcess
공식 코드Conditional Deep Gaussian Processes: multi-fidelity kernel learning
Papers with Code 아카이브
Meta-Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/luckeciano/bumps
공식 코드https://github.com/luckybian/isy5001
공식 코드https://github.com/lueckem/quadrature-ML
공식 코드Efficient time stepping for numerical integration using reinforcement learning
https://github.com/luhc666/asc-cdfsl
공식 코드Adaptive Semantic Consistency for Cross-domain Few-shot Classification
https://github.com/luizgh/sigver
공식 코드Meta-learning for fast classifier adaptation to new users of Signature Verification systems
https://github.com/lukashedegaard/dage
공식 코드Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol
https://github.com/lukastrm/metakkl
공식 코드Adaptive Meta-Learning-Based KKL Observer Design for Nonlinear Dynamical Systems
https://github.com/luoxiaoliu/pfenet2plus
공식 코드PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Mask
https://github.com/luoyuanlab/clinical-longformer
공식 코드Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences
https://github.com/lupalab/Meta-Neighborhoods
공식 코드https://github.com/lvyiqin/DRMAML
공식 코드https://github.com/lyan62/data-curation
공식 코드https://github.com/lyymuwu/timo
공식 코드Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP
https://github.com/lzw108/raemollm
공식 코드https://github.com/lzzcd001/OSLSM
공식 코드https://github.com/m-3lab/im-iad
공식 코드IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
https://github.com/m-3lab/open-iad
공식 코드IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
https://github.com/machanic/MetaAdvDet
공식 코드MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks
https://github.com/machanic/SimulatorAttack
공식 코드Simulating Unknown Target Models for Query-Efficient Black-box Attacks
https://github.com/machine-listeners-valencia/fsl_osr_dataset_baseline
공식 코드https://github.com/madhavlab/2021KalpitBTMT
공식 코드Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning
https://github.com/maheshkkumar/fscc
공식 코드https://github.com/mainlp/SYGAR
공식 코드https://github.com/manasgg44/improved_proto
공식 코드https://github.com/manasgg44/meta_overfit
공식 코드https://github.com/marcdemers/FIGR-8
공식 코드https://github.com/marcelbinz/meta-learned-models
공식 코드https://github.com/marcelbinz/resource-rational-reinforcement-learning
공식 코드Modeling Human Exploration Through Resource-Rational Reinforcement Learning
https://github.com/marcovirgolin/pyNSGP
공식 코드Learning a Formula of Interpretability to Learn Interpretable Formulas
https://github.com/mario-sanz/cicl
공식 코드Corrective In-Context Learning: Evaluating Self-Correction in Large Language Models
https://github.com/mark-sky/kcl
공식 코드The Devil is in the Few Shots: Iterative Visual Knowledge Completion for Few-shot Learning
https://github.com/markpwoodward/active_osl
공식 코드https://github.com/markustyj/fsponer_ecai2024
공식 코드FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios
https://github.com/martayang/sega
공식 코드SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning
https://github.com/masakhane-io/masakhane-news
공식 코드MasakhaNEWS: News Topic Classification for African languages
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.