:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Kang, Hee-Geon, Kim, Sunggon
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Computation
Acceso en línea:	https://arxiv.org/abs/2408.07372
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling
por: Grangier, David, et al.
Publicado: (2024)

DreamSampler: Unifying Diffusion Sampling and Score Distillation for Image Manipulation
por: Kim, Jeongsol, et al.
Publicado: (2024)

Entropy Meets Importance: A Unified Head Importance-Entropy Score for Stable and Efficient Transformer Pruning
por: Choi, Minsik, et al.
Publicado: (2025)

Federated Graph Learning with Adaptive Importance-based Sampling
por: Li, Anran, et al.
Publicado: (2024)

Adaptively Optimised Adaptive Importance Samplers
por: Perello, Carlos A. C. C., et al.
Publicado: (2023)

Pareto Smoothed Importance Sampling
por: Vehtari, Aki, et al.
Publicado: (2015)

Conditional Local Importance by Quantile Expectations
por: Bladen, Kelvyn K., et al.
Publicado: (2024)

Forward $χ^2$ Divergence Based Variational Importance Sampling
por: Li, Chengrui, et al.
Publicado: (2023)

GRASS: Gradient-based Adaptive Layer-wise Importance Sampling for Memory-efficient Large Language Model Fine-tuning
por: Tian, Kaiyuan, et al.
Publicado: (2026)

AIS: Adaptive Importance Sampling for Quantized RL
por: Zhou, Jiajun, et al.
Publicado: (2026)

DISA: Offline Importance Sampling for Distribution-Matching LLM-RL
por: Wang, Shaobo, et al.
Publicado: (2026)

A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel
por: Chen, Yindong, et al.
Publicado: (2021)

DQE-CIR: Distinctive Query Embeddings through Learnable Attribute Weights and Target Relative Negative Sampling in Composed Image Retrieval
por: Park, Geon, et al.
Publicado: (2026)

Importance is Important: Generalized Markov Chain Importance Sampling Methods
por: Li, Guanxun, et al.
Publicado: (2023)

Importance-Aware Adaptive Dataset Distillation
por: Li, Guang, et al.
Publicado: (2024)

ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling
por: Zhu, Zheqi, et al.
Publicado: (2022)

Importance Sampling for Multi-Negative Multimodal Direct Preference Optimization
por: Li, Xintong, et al.
Publicado: (2025)

Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning
por: Kim, Junseok, et al.
Publicado: (2026)

DOO-RE: A dataset of ambient sensors in a meeting room for activity recognition
por: Kim, Hyunju, et al.
Publicado: (2024)

On the Importance of a Multi-Scale Calibration for Quantization
por: Son, Seungwoo, et al.
Publicado: (2026)

Adaptive Pruning for Large Language Models with Structural Importance Awareness
por: Zheng, Haotian, et al.
Publicado: (2024)

VEXA: Evidence-Grounded and Persona-Adaptive Explanations for Scam Risk Sensemaking
por: An, Heajun, et al.
Publicado: (2026)

From Minimax Optimal Importance Sampling to Uniformly Ergodic Importance-tempered MCMC
por: Zhou, Quan
Publicado: (2025)

AdaSTaR: Adaptive Data Sampling for Training Self-Taught Reasoners
por: Koh, Woosung, et al.
Publicado: (2025)

Adaptive Point Transformer
por: Baiocchi, Alessandro, et al.
Publicado: (2024)

Adaptive Task Vectors for Large Language Models
por: Kang, Joonseong, et al.
Publicado: (2025)

InfiniteHiP: Extending Language Model Context Up to 3 Million Tokens on a Single GPU
por: Lee, Heejun, et al.
Publicado: (2025)

Score-Regularized Joint Sampling with Importance Weights for Flow Matching
por: Liu, Xinshuang, et al.
Publicado: (2025)

Stochastic Localization via Iterative Posterior Sampling
por: Grenioux, Louis, et al.
Publicado: (2024)

Speculative Sampling for Parametric Temporal Point Processes
por: Biloš, Marin, et al.
Publicado: (2025)

Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond
por: Guo, Wei, et al.
Publicado: (2025)

VideoGuide: Improving Video Diffusion Models without Training Through a Teacher's Guide
por: Lee, Dohun, et al.
Publicado: (2024)

Regularization by Texts for Latent Diffusion Inverse Solvers
por: Kim, Jeongsol, et al.
Publicado: (2023)

Learning from Random Demonstrations: Offline Reinforcement Learning with Importance-Sampled Diffusion Models
por: Fang, Zeyu, et al.
Publicado: (2024)

Adaptive Non-uniform Timestep Sampling for Accelerating Diffusion Model Training
por: Kim, Myunsoo, et al.
Publicado: (2024)

OMEGA: A Low-Latency GNN Serving System for Large Graphs
por: Kim, Geon-Woo, et al.
Publicado: (2025)

Learning to Importance Sample in Primary Sample Space
por: Zheng, Quan, et al.
Publicado: (2018)

Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
por: Ahn, Donghoon, et al.
Publicado: (2024)

Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design
por: Cai, Ruisi, et al.
Publicado: (2024)

Adaptive-lambda Subtracted Importance Sampled Scores in Machine Unlearning for DDPMs and VAEs
por: Dini, MohammadParsa, et al.
Publicado: (2025)