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/bpcarlos/understanding_qml_rethinking_gen

공식 코드

Understanding quantum machine learning also requires rethinking generalization

https://github.com/brantondemoss/GrokkingComplexity

공식 코드

The Complexity Dynamics of Grokking

https://github.com/brit7777/lm-plagiarism

공식 코드

Do Language Models Plagiarize?

https://github.com/caotians1/BabyAIPlusPlus

공식 코드

Zero-Shot Compositional Policy Learning via Language Grounding

https://github.com/carol-gutianle/meow

공식 코드

MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts

https://github.com/chenxi52/upa

공식 코드

Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain Adaptation

https://github.com/chuck-arvin/casehold2025

공식 코드

Identifying Legal Holdings with LLMs: A Systematic Study of Performance, Scale, and Memorization

https://github.com/chunsanHong/MemBench_code

공식 코드

MemBench: Memorized Image Trigger Prompt Dataset for Diffusion Models

https://github.com/cjbarrie/ai-norms-prompting

공식 코드

Emergent LLM behaviors are observationally equivalent to data leakage

https://github.com/claim-berlin/relict

공식 코드

RELICT: A Replica Detection Framework for Medical Image Generation

https://github.com/claserken/SISS

공식 코드

Data Unlearning in Diffusion Models

https://github.com/coastalcph/copyrightllms

공식 코드

Copyright Violations and Large Language Models

https://github.com/codellm-research/codejudge-eval

공식 코드

CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?

https://github.com/computationalprivacy/copyright-traps

공식 코드

Copyright Traps for Large Language Models

https://github.com/computationalprivacy/mia_llms_benchmark

공식 코드

SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)

https://github.com/computationalprivacy/mosaic_memory

공식 코드

The Mosaic Memory of Large Language Models

https://github.com/cornell-zhang/heurigym

공식 코드

HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial Optimization

https://github.com/csm9493/efficient-llm-unlearning

공식 코드

Towards Robust and Parameter-Efficient Knowledge Unlearning for LLMs

https://github.com/cspartalis/lotus

공식 코드

LoTUS: Large-Scale Machine Unlearning with a Taste of Uncertainty

https://github.com/curtfox/decoder-memory-capacity

공식 코드

Next-token prediction capacity: general upper bounds and a lower bound for transformers

https://github.com/cycucas/gcniii

공식 코드

Wide & Deep Learning for Node Classification

https://github.com/d-doshi/Grokking

공식 코드

To grok or not to grok: Disentangling generalization and memorization on corrupted algorithmic datasets

https://github.com/dahyun-kang/mml

공식 코드

Memory-Modular Classification: Learning to Generalize with Memory Replacement

https://github.com/dbarzilai7/kernel_reconstruction

공식 코드

Querying Kernel Methods Suffices for Reconstructing their Training Data

https://github.com/dbp1994/bare-wacv-2023

공식 코드

Adaptive Sample Selection for Robust Learning under Label Noise

https://github.com/dbp1994/masters_thesis_codes/tree/main/memorization_and_overparam

공식 코드

Memorization in Deep Neural Networks: Does the Loss Function matter?

https://github.com/deep-symbolic-mathematics/llm-sr

공식 코드

LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models

https://github.com/deep-symbolic-mathematics/llm-srbench

공식 코드

LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models

https://github.com/deepmind/informed_adversary_mnist_reconstruction

공식 코드

Reconstructing Training Data with Informed Adversaries

https://github.com/deezer/llms_quotation_attribution

공식 코드

Evaluating LLMs for Quotation Attribution in Literary Texts: A Case Study of LLaMa3

https://github.com/dheeraj7596/lops

공식 코드

LOPS: Learning Order Inspired Pseudo-Label Selection for Weakly Supervised Text Classification

https://github.com/dmis-lab/bioner-generalization

공식 코드

How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?

https://github.com/dongyp13/memorization-AT

공식 코드

Exploring Memorization in Adversarial Training

https://github.com/doujiang-zheng/tip-gnn

공식 코드

Transition Propagation Graph Neural Networks for Temporal Networks

https://github.com/drndr/bert_ent_attack

공식 코드

Memorization of Named Entities in Fine-tuned BERT Models

https://github.com/dtuzi123/ovae

공식 코드

Continual Variational Autoencoder Learning via Online Cooperative Memorization

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