Papers with Code 아카이브

Memorization

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

1

보존된 평가 기록

3

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/getao/icae

공식 코드

In-context Autoencoder for Context Compression in a Large Language Model

https://github.com/ggendro/causal-cartographer

공식 코드

Causal Cartographer: From Mapping to Reasoning Over Counterfactual Worlds

https://github.com/ghostq99/robusttraininggnn

공식 코드

Robust Training of Graph Neural Networks via Noise Governance

https://github.com/giannisdaras/ambient-tweedie

공식 코드

Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data

https://github.com/gibipara92/learning-explanations-hard-to-vary

공식 코드

Learning explanations that are hard to vary

https://github.com/gmum/PALATE

공식 코드

PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models

https://github.com/google-deepmind/searchless_chess

공식 코드

Amortized Planning with Large-Scale Transformers: A Case Study on Chess

https://github.com/google-research/google-research/tree/master/extreme_memorization

공식 코드

Extreme Memorization via Scale of Initialization

https://github.com/google-research/google-research/tree/master/meta_learning_without_memorization

공식 코드

Meta-Learning without Memorization

https://github.com/google/BIG-bench

공식 코드

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

https://github.com/googleinterns/localizing-paragraph-memorization

공식 코드

Localizing Paragraph Memorization in Language Models

https://github.com/gouki510/grokking-tickets

공식 코드

Bridging Lottery Ticket and Grokking: Understanding Grokking from Inner Structure of Networks

https://github.com/hazyresearch/state-spaces

공식 코드

Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers

https://github.com/hcyang-null/mild

공식 코드

MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels

https://github.com/hiroki11x/timm_ood_calibration

공식 코드

An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration

https://github.com/hodeld/emergent_analogies_llm_fork

공식 코드

Response: Emergent analogical reasoning in large language models

https://github.com/hongwang600/flair

공식 코드

Bot or Human? Detecting ChatGPT Imposters with A Single Question

https://github.com/hrayrhar/limit-label-memorization

공식 코드

Improving Generalization by Controlling Label-Noise Information in Neural Network Weights

https://github.com/htt210/GeneralizationMetricGAN

공식 코드

Toward a Generalization Metric for Deep Generative Models

https://github.com/huihanlhh/CultureGenAttr

공식 코드

Attributing Culture-Conditioned Generations to Pretraining Corpora

https://github.com/hushes-minutes/contrasting_attention

공식 코드

Enhancing elusive clues in knowledge learning by contrasting attention of language models

https://github.com/iaar-shanghai/fastmem

공식 코드

FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language Models

https://github.com/ican-szu/lond-drs

공식 코드

Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space

https://github.com/illusionhi/probingprivacy

공식 코드

Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Models

https://github.com/interpretml/llm-tabular-memorization-checker

공식 코드

Elephants Never Forget: Testing Language Models for Memorization of Tabular Data

https://github.com/jaydeepborkar/Assisted-Memorization

공식 코드

Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training

https://github.com/jeffhj/lm_personalinfoleak

공식 코드

Are Large Pre-Trained Language Models Leaking Your Personal Information?

https://github.com/jiacheng-xu/sum-interpret

공식 코드

Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution

https://github.com/jiaran-ye/implicitreasoning

공식 코드

How does Transformer Learn Implicit Reasoning?

https://github.com/jingli9111/RUM

공식 코드

Rotational Unit of Memory

https://github.com/jingli9111/RUM-Tensorflow

공식 코드

Rotational Unit of Memory

https://github.com/jingwu321/erasediff

공식 코드

Erasing Undesirable Influence in Diffusion Models

https://github.com/jingyi0000/bimem

공식 코드

Black-box Unsupervised Domain Adaptation with Bi-directional Atkinson-Shiffrin Memory

https://github.com/jonathanbrok/identifying-memorization-of-diffusion-models-through-p-laplace-analysis

공식 코드

Identifying Memorization of Diffusion Models through p-Laplace Analysis

https://github.com/josh1108/eptest

공식 코드

Implications of Annotation Artifacts in Edge Probing Test Datasets

https://github.com/jshwang0311/odim

공식 코드

ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models

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