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Memorization

Papers

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Showing 601–650 of 1088 papers

TitleStatusHype
Understanding Unintended Memorization in Federated Learning—0
Understanding Unintended Memorization in Language Models Under Federated Learning—0
Undesirable Memorization in Large Language Models: A Survey—0
Unified Regularity Measures for Sample-wise Learning and Generalization—0
Unified View of Grokking, Double Descent and Emergent Abilities: A Perspective from Circuits Competition—0
Unintended Memorization and Timing Attacks in Named Entity Recognition Models—0
Unintended Memorization in Large ASR Models, and How to Mitigate It—0
Universal Online Learning: an Optimistically Universal Learning Rule—0
Universal Online Learning with Unbounded Losses: Memory Is All You Need—0
Unlearning in- vs. out-of-distribution data in LLMs under gradient-based method—0
Unleash the Potential of Adaptation Models via Dynamic Domain Labels—0
Unlocking Memorization in Large Language Models with Dynamic Soft Prompting—0
UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization—0
Unsupervised Meta-Learning via In-Context Learning—0
Unveiling Privacy, Memorization, and Input Curvature Links—0
Unveiling Structural Memorization: Structural Membership Inference Attack for Text-to-Image Diffusion Models—0
Using Early-Learning Regularization to Classify Real-World Noisy Data—0
U-Turn Diffusion—0
Validation of machine learning based scenario generators—0
Variance-Based Membership Inference Attacks Against Large-Scale Image Captioning Models—0
Variance Reduced Stochastic Gradient Descent with Neighbors—0
Vertical Validation: Evaluating Implicit Generative Models for Graphs on Thin Support Regions—0
VideoMatch: Matching based Video Object Segmentation—0
VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability—0
Volumization as a Natural Generalization of Weight Decay—0
Wasserstein proximal operators describe score-based generative models and resolve memorization—0
What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks—0
What can we learn from Data Leakage and Unlearning for Law?—0
What do larger image classifiers memorise?—0
What Do Neural Networks Learn When Trained With Random Labels?—0
What is Adversarial Training for Diffusion Models?—0
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions—0
What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests—0
When Can Memorization Improve Fairness?—0
When do you need Chain-of-Thought Prompting for ChatGPT?—0
When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale—0
When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?—0
When Reasoning Meets Compression: Benchmarking Compressed Large Reasoning Models on Complex Reasoning Tasks—0
Where to find Grokking in LLM Pretraining? Monitor Memorization-to-Generalization without Test—0
Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes—0
White-box Membership Attack Against Machine Learning Based Retinopathy Classification—0
Why are state-space models more expressive than n-gram models?—0
Towards Understanding Clean Generalization and Robust Overfitting in Adversarial Training—0
Why Diffusion Models Don't Memorize: The Role of Implicit Dynamical Regularization in Training—0
Why Does ChatGPT Fall Short in Providing Truthful Answers?—0
Why Train More? Effective and Efficient Membership Inference via Memorization—0
Wide and Deep Learning for Peer-to-Peer Lending—0
WILT: A Multi-Turn, Memorization-Robust Inductive Logic Benchmark for LLMs—0
WinoWhat: A Parallel Corpus of Paraphrased WinoGrande Sentences with Common Sense Categorization—0
Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PaLM-540B (few-shot, k=5)Accuracy95.4—Unverified
2Gopher-280B (few-shot, k=5)Accuracy80—Unverified
3PaLM-62B (few-shot, k=5)Accuracy77.7—Unverified