https://github.com/dylan-slack/fairness-warnings-fair-maml
공식 코드Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data
Papers with Code 아카이브
Meta-Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 4
- 보존된 평가 기록
- 13
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/e-bug/iglue
공식 코드IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/e-bug/volta
공식 코드IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/eaoltu/hyperseed
공식 코드Hyperseed: Unsupervised Learning with Vector Symbolic Architectures
https://github.com/easezyc/MetaHeac
공식 코드Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
https://github.com/ebennequin/FewShotVision
공식 코드https://github.com/ebennequin/few-shot-open-set
공식 코드https://github.com/ebennequin/meta-domain-shift
공식 코드Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift
https://github.com/ec6dde01667145e58de60f864e05a4/CausalOptimizationAnon
공식 코드A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
https://github.com/eccv22-ood-workshop/eccv22-ood-workshop.github.io
공식 코드Advancing Image Retrieval with Few-Shot Learning and Relevance Feedback
https://github.com/edchengg/model_selection
공식 코드https://github.com/edgarschnfld/CADA-VAE-PyTorch
공식 코드Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
https://github.com/edi-meta-learning/meta-omnium
공식 코드Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn
https://github.com/edwardjhu/TP4
공식 코드https://github.com/edwin-pan/MetaHDR
공식 코드MetaHDR: Model-Agnostic Meta-Learning for HDR Image Reconstruction
https://github.com/egetaga/timepfn
공식 코드TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data
https://github.com/ekinakyurek/marc
공식 코드The Surprising Effectiveness of Test-Time Training for Few-Shot Learning
https://github.com/elvisnava/hyperclip
공식 코드https://github.com/enegrini/MBO_network
공식 코드https://github.com/ennengyang/awesome-model-merging-methods-theories-applications
공식 코드Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
https://github.com/epfl-nlp/scesc-llm-skill-extraction
공식 코드Rethinking Skill Extraction in the Job Market Domain using Large Language Models
https://github.com/epfllm/meditron
공식 코드MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
https://github.com/epignatelli/navix
공식 코드https://github.com/eric-ai-lab/pectvlm
공식 코드https://github.com/eric-mitchell/macaw
공식 코드Offline Meta-Reinforcement Learning with Advantage Weighting
https://github.com/estelyang/qcircuitnet_dataset
공식 코드QCircuitNet: A Large-Scale Hierarchical Dataset for Quantum Algorithm Design
https://github.com/esw0116/DynaVSR
공식 코드https://github.com/eternityyw/tram-benchmark
공식 코드TRAM: Benchmarking Temporal Reasoning for Large Language Models
https://github.com/eth-lre/llm_icl
공식 코드What Do Language Models Learn in Context? The Structured Task Hypothesis
https://github.com/eth-sri/finetuning-activated-backdoors
공식 코드https://github.com/ethanjperez/true_few_shot
공식 코드https://github.com/etiennelnr/timeflow
공식 코드Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations
https://github.com/evanzhuang/metatree
공식 코드https://github.com/explainableml/avdiff-gfsl
공식 코드Text-to-feature diffusion for audio-visual few-shot learning
https://github.com/explainableml/fomo_in_flux
공식 코드https://github.com/extensityai/benchmark
공식 코드SymbolicAI: A framework for logic-based approaches combining generative models and solvers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.