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/tensorflow/models

공식 코드

Learning to Remember Rare Events

https://github.com/tensorflow/quantum

공식 코드

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

https://github.com/thaihungle/NSM

공식 코드

Neural Stored-program Memory

https://github.com/theo-wu/metaras

공식 코드

Learning to Rasterize Differentiably

https://github.com/theoceancleanup/rivertrashsegmentation

공식 코드

Foundation Model or Finetune? Evaluation of few-shot semantic segmentation for river pollution

https://github.com/thinklab-sjtu/awesome-llm4ad

공식 코드

LLM4Drive: A Survey of Large Language Models for Autonomous Driving

https://github.com/thomasmiconi/meta-task-generator

공식 코드

Procedural generation of meta-reinforcement learning tasks

https://github.com/thomasmiconi/metametalearning

공식 코드

Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning

https://github.com/thu-bpm/gradlre

공식 코드

Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction

https://github.com/thu-bpm/rapl

공식 코드

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

https://github.com/thu-coai/ppt

공식 코드

PPT: Pre-trained Prompt Tuning for Few-shot Learning

https://github.com/thudm/longbench

공식 코드

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

https://github.com/thudm/msagpt

공식 코드

MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training

https://github.com/thunlp/OpenMatch

공식 코드

CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search

https://github.com/thunlp/OpenPrompt

공식 코드

Prototypical Verbalizer for Prompt-based Few-shot Tuning

https://github.com/thunlp/fewrel

공식 코드

FewRel 2.0: Towards More Challenging Few-Shot Relation Classification

https://github.com/thunlp/seq2seq-prompt

공식 코드

Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models

https://github.com/tian1327/swat

공식 코드

Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning

https://github.com/tiangeluo/fsl-global

공식 코드

Few-Shot Learning with Global Class Representations

https://github.com/tianjunz/tempera

공식 코드

TEMPERA: Test-Time Prompting via Reinforcement Learning

https://github.com/tibhannover/patent-figure-classification

공식 코드

Patent Figure Classification using Large Vision-language Models

https://github.com/tiehangd/MUPS

공식 코드

Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification

https://github.com/tinkez/fap_cdfsc

공식 코드

Exploring Cross-Domain Few-Shot Classification via Frequency-Aware Prompting

https://github.com/tinyaway/manet

공식 코드

Few-shot semantic segmentation via mask aggregation

https://github.com/tju-sjyj/amu-tuning

공식 코드

AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning

https://github.com/tmcarvalho/AUTOPRIV

공식 코드

Automated Privacy-Preserving Techniques via Meta-Learning

https://github.com/tmiu/itfa

공식 코드

Incremental Few-Shot Object Detection via Simple Fine-Tuning Approach

https://github.com/tmlr-group/CoPA

공식 코드

Mind the Gap Between Prototypes and Images in Cross-domain Finetuning

https://github.com/tmlr-group/mokd

공식 코드

MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence

https://github.com/tmlr-group/wca

공식 코드

Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models

https://github.com/tntek/dspnet

공식 코드

Few-Shot Medical Image Segmentation with High-Fidelity Prototypes

https://github.com/tobiasvanderwerff/master-thesis

공식 코드

Writer adaptation for offline text recognition: An exploration of neural network-based methods

https://github.com/tobyperrett/context_splits

공식 코드

Meta-Learning with Context-Agnostic Initialisations

https://github.com/tom-andersson/eds2022-convgnp-sensor-placement

공식 코드

Environmental Sensor Placement with Convolutional Gaussian Neural Processes

https://github.com/tom-andersson/iclr2023-antarctic-arconvcnp

공식 코드

Autoregressive Conditional Neural Processes

https://github.com/tommccoy1/meta-learning-linguistic-biases

공식 코드

Universal linguistic inductive biases via meta-learning

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