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Memorization

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

https://github.com/machine-learning-security-lab/mia_prune

공식 코드

Membership Inference Attacks and Defenses in Neural Network Pruning

https://github.com/maclll/selc

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SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels

https://github.com/magnetic2014/roleeval

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RoleEval: A Bilingual Role Evaluation Benchmark for Large Language Models

https://github.com/mahf93/tracking-memorization

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Leveraging Unlabeled Data to Track Memorization

https://github.com/mainlp/explaind

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Grokking ExPLAIND: Unifying Model, Data, and Training Attribution to Study Model Behavior

https://github.com/marcelomatheusgauy/optimal_kronecker_approximation

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Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning

https://github.com/marcobombieri/do-LLM-dream-of-ontologies

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Do LLMs Dream of Ontologies?

https://github.com/maria-antoniak/poetry-eval

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Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets

https://github.com/matanabudy/mdl-hn

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Minimum Description Length Hopfield Networks

https://github.com/matejsprogar/agitb

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AGITB: A Signal-Level Benchmark for Evaluating Artificial General Intelligence

https://github.com/matthewyzhang/nlgift

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Can LLM Graph Reasoning Generalize beyond Pattern Memorization?

https://github.com/mdrafiqulrabin/CI-Memorization

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Memorization and Generalization in Neural Code Intelligence Models

https://github.com/mechanistic-interpretability-grokking/progress-measures-paper

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Progress measures for grokking via mechanistic interpretability

https://github.com/megagonlabs/witqa

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Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models

https://github.com/meng-wenlong/rr

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R.R.: Unveiling LLM Training Privacy through Recollection and Ranking

https://github.com/microsoft/planting-and-mitigating-memorization

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Planting and Mitigating Memorized Content in Predictive-Text Language Models

https://github.com/mingxue-xu/ori

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Data Origin Inference in Machine Learning

https://github.com/mireshghallah/ft-memorization

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Memorization in NLP Fine-tuning Methods

https://github.com/mjamroz90/dnn-class-fitting

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Neural Representations Reveal Distinct Modes of Class Fitting in Residual Convolutional Networks

https://github.com/mkuchnik/relm

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Validating Large Language Models with ReLM

https://github.com/ml-research/localizing_memorization_in_diffusion_models

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Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models

https://github.com/mohaes/vl4ml

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Affective Medical Estimation and Decision Making via Visualized Learning and Deep Learning

https://github.com/msakarvadia/memorization

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Mitigating Memorization In Language Models

https://github.com/mtensor/neural_sketch

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Learning to Infer Program Sketches

https://github.com/muhamadusman/assist

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Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models

https://github.com/nardien/kard

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Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks

https://github.com/narenmanoj/mnist-adv-training

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Excess Capacity and Backdoor Poisoning

https://github.com/nazmul-karim170/C-SFDA_Source-Free-Domain-Adaptation

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C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation

https://github.com/nazmul-karim170/unicon-noisy-label

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UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning

https://github.com/nbalepur/mcqa-artifacts

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Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?

https://github.com/neurai-lab/cure

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Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training

https://github.com/ngaggion/chest-xray-landmark-dataset

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Multi-center anatomical segmentation with heterogeneous labels via landmark-based models

https://github.com/nickydcfp/solidmark

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SolidMark: Evaluating Image Memorization in Generative Models

https://github.com/nkushaw/psychoinformatics

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Post-hoc and manifold explanations analysis of facial expression data based on deep learning

https://github.com/nla-group/slearn

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A comparison of LSTM and GRU networks for learning symbolic sequences

https://github.com/nurikorhan/negotiated-representations

공식 코드

Negotiated Representations for Machine Mearning Application

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