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/damo-nlp-sg/llm-sentiment

공식 코드

Sentiment Analysis in the Era of Large Language Models: A Reality Check

https://github.com/danhntd/FS-CDIS

공식 코드

The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation

https://github.com/danielzuegner/gnn-meta-attack

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Adversarial Attacks on Graph Neural Networks via Meta Learning

https://github.com/dannyallover/overthinking_the_truth

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Overthinking the Truth: Understanding how Language Models Process False Demonstrations

https://github.com/dasepli/codeie

공식 코드

CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors

https://github.com/data-designer/maprec

공식 코드

Cross-domain Transfer of Valence Preferences via a Meta-optimization Approach

https://github.com/daviswer/fewshotlocal

공식 코드

Few-Shot Learning with Localization in Realistic Settings

https://github.com/davor10105/laat

공식 코드

Large Language Models as Attribution Regularizers for Efficient Model Training

https://github.com/db-lee/selfsup_dd

공식 코드

Self-Supervised Dataset Distillation for Transfer Learning

https://github.com/dchiji-ntt/meta-ticket

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Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks

https://github.com/dddavid4real/focus

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FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification

https://github.com/ddrous/ncflow

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Neural Context Flows for Meta-Learning of Dynamical Systems

https://github.com/ddrous/self-mod

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Reevaluating Meta-Learning Optimization Algorithms Through Contextual Self-Modulation

https://github.com/debabratapal7/dafosnet

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Domain Adaptive Few-Shot Open-Set Learning

https://github.com/debarshigit/promptmargin

공식 코드

Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts

https://github.com/decadz/evolved-model-agnostic-loss

공식 코드

Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning

https://github.com/decadz/sparse-label-smoothing-regularization

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Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning

https://github.com/deep-symbolic-mathematics/Multimodal-Math-Pretraining

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SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training

https://github.com/deep-symbolic-mathematics/Multimodal-Symbolic-Regression

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SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training

https://github.com/deepmind/deepmind-research

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Towards mental time travel: a hierarchical memory for reinforcement learning agents

https://github.com/deepmind/deepmind-research/tree/master/cs_gan

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Deep Compressed Sensing

https://github.com/deepmind/dm_alchemy

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Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents

https://github.com/deepmind/dm_fast_mapping

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Towards mental time travel: a hierarchical memory for reinforcement learning agents

https://github.com/deepmind/dm_nevis

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NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research

https://github.com/deepmind/emergent_in_context_learning

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Data Distributional Properties Drive Emergent In-Context Learning in Transformers

https://github.com/deepmind/lab/tree/master/game_scripts/levels/contributed/fast_mapping

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Grounded Language Learning Fast and Slow

https://github.com/deepmind/learning-to-learn

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Learning to learn by gradient descent by gradient descent

https://github.com/deepmind/leo

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Meta-Learning with Latent Embedding Optimization

https://github.com/deepmind/nonstationary_mbml

공식 코드

Memory-Based Meta-Learning on Non-Stationary Distributions

https://github.com/deepseek-ai/DeepEP

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A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications

https://github.com/deezer/net-ner-probing

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Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition

https://github.com/deku-0621/tide

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TIDE: Test Time Few Shot Object Detection

https://github.com/dekura/llana

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LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation

https://github.com/densechen/AReLU

공식 코드

AReLU: Attention-based Rectified Linear Unit

https://github.com/derekchen14/mixture_soft_prompts

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Mixture of Soft Prompts for Controllable Data Generation

https://github.com/derenlei/CS_Net

공식 코드

Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person Shooters

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