Papers with Code 아카이브

Meta-Learning

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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/kclip/channel-prediction-meta-learning

공식 코드

Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition

https://github.com/kclip/icl-equalization

공식 코드

In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models

https://github.com/kclip/meta-autoencoder

공식 코드

Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels

https://github.com/kclip/meta-autoencoder-without-channel-model

공식 코드

End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning

https://github.com/kclip/meta-demodulator

공식 코드

Learning How to Demodulate from Few Pilots via Meta-Learning

https://github.com/kclip/meta-xb

공식 코드

Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Prediction

https://github.com/kclip/scalar-channel-meta-prediction

공식 코드

Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation

https://github.com/kddubey/pretrain-on-test

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Evaluating the fairness of task-adaptive pretraining on unlabeled test data before few-shot text classification

https://github.com/kding1225/softcpt

공식 코드

Prompt Tuning with Soft Context Sharing for Vision-Language Models

https://github.com/keaml-jlu/star

공식 코드

Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport

https://github.com/keanson/md-bsfc

공식 코드

Accelerating Convergence in Bayesian Few-Shot Classification

https://github.com/keanson/revisit-logistic-softmax

공식 코드

Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification

https://github.com/keeplearning-again/matchseg

공식 코드

MatchSeg: Towards Better Segmentation via Reference Image Matching

https://github.com/keirp/automatic_prompt_engineer

공식 코드

Large Language Models Are Human-Level Prompt Engineers

https://github.com/kelichiu/GPT3-hate-speech-detection

공식 코드

Detecting Hate Speech with GPT-3

https://github.com/kent0n-li/medical-image-segmentation

공식 코드

nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance

https://github.com/keraJLi/synthetic-gymnax

공식 코드

Discovering Minimal Reinforcement Learning Environments

https://github.com/kevinma1515/gpt_idetc

공식 코드

Conceptual Design Generation Using Large Language Models

https://github.com/kevinsrr/repal

공식 코드

Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction

https://github.com/kexin-kxzhang/optfusion

공식 코드

Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models

https://github.com/keyu07/DADG

공식 코드

Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation

https://github.com/kfu02/jaxmarl

공식 코드

Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot Coordination

https://github.com/khalil-research/arga-aaai23

공식 코드

Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus

https://github.com/khurramjaved96/columnar_networks

공식 코드

Scalable Online Recurrent Learning Using Columnar Neural Networks

https://github.com/khy0809/fewshot-egnn

공식 코드

Edge-labeling Graph Neural Network for Few-shot Learning

https://github.com/kieranjwood/x-trend

공식 코드

Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies

https://github.com/kikimormay/fsl-tcbr

공식 코드

Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid

https://github.com/kikumaru818/meta_math_scoring

공식 코드

Automatic Short Math Answer Grading via In-context Meta-learning

https://github.com/kilimanj4r0/code-summarization-beyond-function-level

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Code Summarization Beyond Function Level

https://github.com/killingbear999/chemical-reactor-foundation-model

공식 코드

Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation

https://github.com/kinit-sk/l3d-sensitivity-investigation

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On Sensitivity of Learning with Limited Labelled Data to the Effects of Randomness: Impact of Interactions and Systematic Choices

https://github.com/kinit-sk/selec-strats-for-aug

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Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classification

https://github.com/kiryteo/RG_Classification_code

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Data-Efficient Classification of Radio Galaxies

https://github.com/kjunelee/MetaOptNet

공식 코드

Meta-Learning with Differentiable Convex Optimization

https://github.com/kkalais/stochlwta-ml

공식 코드

Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning

https://github.com/kksinghal/concept-search

공식 코드

ConceptSearch: Towards Efficient Program Search Using LLMs for Abstraction and Reasoning Corpus (ARC)

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.