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/oughtinc/raft-baselines

공식 코드

RAFT: A Real-World Few-Shot Text Classification Benchmark

https://github.com/ouhenio/llms-overstimate-profoundness

공식 코드

Large Language Models are biased to overestimate profoundness

https://github.com/ouyangtianran/plml

공식 코드

Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning

https://github.com/oveilleux/realistic_transductive_few_shot

공식 코드

Realistic Evaluation of Transductive Few-Shot Learning

https://github.com/oywtece/gme

공식 코드

Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction

https://github.com/ozzzp/MLHF

공식 코드

Meta-Learning with Hessian-Free Approach in Deep Neural Nets Training

https://github.com/p-lambda/incontext-learning

공식 코드

An Explanation of In-context Learning as Implicit Bayesian Inference

https://github.com/paint4poem/paint4poem

공식 코드

Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poems

https://github.com/pancakewaffles/metasrgan-tutorial

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Arbitrary Scale Super-Resolution for Brain MRI Images

https://github.com/pandeydeep9/units-ml-cvpr-22

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Multidimensional Belief Quantification for Label-Efficient Meta-Learning

https://github.com/papart/res-recsyst

공식 코드

Meta-Learning for Resampling Recommendation Systems

https://github.com/parksandrecfan/VIRL

공식 코드

VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability Estimation

https://github.com/parkseobin/MLSR

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Fast Adaptation to Super-Resolution Networks via Meta-Learning

https://github.com/pasqualedem/LabelAnything

공식 코드

Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts

https://github.com/patricia-a-apellaniz/low_sample_data_generator

공식 코드

Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios

https://github.com/pauchingyap/boml

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Addressing Catastrophic Forgetting in Few-Shot Problems

https://github.com/pavanbaswani/fincausal_sharedtask-2023

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Fine-grained Contract NER using instruction based model

https://github.com/pawn-sxy/dcama

공식 코드

Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation

https://github.com/pbihao/hdmnet

공식 코드

Hierarchical Dense Correlation Distillation for Few-Shot Segmentation

https://github.com/pedrohtg/weasel

공식 코드

Learning to Segment Medical Images from Few-Shot Sparse Labels

https://github.com/peiman-m/ACQNP

공식 코드

Adaptive Conditional Quantile Neural Processes

https://github.com/peiman-m/specconvcnp

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Spectral Convolutional Conditional Neural Processes

https://github.com/pengbaolin/SC-GPT

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Few-shot Natural Language Generation for Task-Oriented Dialog

https://github.com/peustr/nfts-public

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Neural Fine-Tuning Search for Few-Shot Learning

https://github.com/peweetheman/reinforcement_learning_in_two_player_simultaneous_action_games

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Reinforcement Learning In Two Player Zero Sum Simultaneous Action Games

https://github.com/pfnet-research/meta-tasnet

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Meta-learning Extractors for Music Source Separation

https://github.com/pfriedri/medfuncta

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MedFuncta: Modality-Agnostic Representations Based on Efficient Neural Fields

https://github.com/pgao-lab/amga

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Towards Adaptive Meta-Gradient Adversarial Examples for Visual Tracking

https://github.com/pharmolix/openbiomed

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BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine

https://github.com/philipmit/thread

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THREAD: Thinking Deeper with Recursive Spawning

https://github.com/philippdahlinger/ltsgns_ai4science

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Latent Task-Specific Graph Network Simulators

https://github.com/picsart-ai-research/mask-matching-transformer

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Mask Matching Transformer for Few-Shot Segmentation

https://github.com/pinakinathc/fscoco

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FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context

https://github.com/pittnail/llms-vote

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Large Language Models Vote: Prompting for Rare Disease Identification

https://github.com/pku-marl/dexteroushands

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Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning

https://github.com/pku-ml/beyond_interpretability

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Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness

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