Copertina

Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hu, Jie, Chen, Lingyun, Kim, Geeho, Choi, Jinyoung, Han, Bohyung, Eun, Do Young
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Computation
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Communication-Efficient Federated Learning with Accelerated Client Gradient
di: Kim, Geeho, et al.
Pubblicazione: (2022)

Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
di: Hu, Jie, et al.
Pubblicazione: (2025)

Relaxed Contrastive Learning for Federated Learning
di: Seo, Seonguk, et al.
Pubblicazione: (2024)

MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators
di: Choi, Jinyoung, et al.
Pubblicazione: (2021)

Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
di: Hu, Jie, et al.
Pubblicazione: (2024)

Self-Repellent Random Walks on General Graphs -- Achieving Minimal Sampling Variance via Nonlinear Markov Chains
di: Doshi, Vishwaraj, et al.
Pubblicazione: (2023)

Enhanced Diffusion Sampling via Extrapolation with Multiple ODE Solutions
di: Choi, Jinyoung, et al.
Pubblicazione: (2025)

A Training-Free Defense Framework for Robust Learned Image Compression
di: Song, Myungseo, et al.
Pubblicazione: (2024)

FIFO-Diffusion: Generating Infinite Videos from Text without Training
di: Kim, Jihwan, et al.
Pubblicazione: (2024)

Observation-Guided Diffusion Probabilistic Models
di: Kang, Junoh, et al.
Pubblicazione: (2023)

Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications
di: Hu, Jie, et al.
Pubblicazione: (2024)

Towards Efficient Vision State Space Models via Token Merging
di: Park, Jinyoung, et al.
Pubblicazione: (2025)

Repelling-Attracting Hamiltonian Monte Carlo
di: Vishwanath, Siddharth, et al.
Pubblicazione: (2024)

Reverse Diffusion Sequential Monte Carlo Samplers
di: Wu, Luhuan, et al.
Pubblicazione: (2025)

Diffusion Path Samplers via Sequential Monte Carlo
di: Young, James Matthew, et al.
Pubblicazione: (2026)

War and Peace: Ogawa Takemitsu's Theological Engagement with State and Religion
di: Eun‐Young Park, et al.
Pubblicazione: (2025)

Optimised Annealed Sequential Monte Carlo Samplers
di: Syed, Saifuddin, et al.
Pubblicazione: (2024)

STR-Match: Matching SpatioTemporal Relevance Score for Training-Free Video Editing
di: Lee, Junsung, et al.
Pubblicazione: (2025)

Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers
di: Fearnhead, Paul, et al.
Pubblicazione: (2024)

Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization
di: Kim, Kyurae, et al.
Pubblicazione: (2025)

Water‐Repellent, Surface‐Adaptive, and Switchable Adhesive Patch Using Microstructured Shape Memory Polymers
di: Jaeil Kim, et al.
Pubblicazione: (2026)

Hierarchical Visual Feature Aggregation for OCR-Free Document Understanding
di: Park, Jaeyoo, et al.
Pubblicazione: (2024)

Monte Carlo Expected Threat (MOCET) Scoring
di: Kim, Joseph, et al.
Pubblicazione: (2025)

Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers
di: Millard, Andrew, et al.
Pubblicazione: (2025)

Generalized Posterior Calibration via Sequential Monte Carlo Sampler
di: Tanaka, Masahiro
Pubblicazione: (2024)

Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD
di: Hu, Jie, et al.
Pubblicazione: (2024)

AGDC: Autoregressive Generation of Variable-Length Sequences with Joint Discrete and Continuous Spaces
di: Shin, Yeonsang, et al.
Pubblicazione: (2026)

GP-4DGS: Probabilistic 4D Gaussian Splatting from Monocular Video via Variational Gaussian Processes
di: Kim, Mijeong, et al.
Pubblicazione: (2026)

Map the Flow: Revealing Hidden Pathways of Information in VideoLLMs
di: Kim, Minji, et al.
Pubblicazione: (2025)

On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers
di: Durmus, Alain, et al.
Pubblicazione: (2023)

Contraction of Markovian Operators in Orlicz Spaces and Error Bounds for Markov Chain Monte Carlo
di: Esposito, Amedeo Roberto, et al.
Pubblicazione: (2024)

PhysGaia: A Physics-Aware Benchmark with Multi-Body Interactions for Dynamic Novel View Synthesis
di: Kim, Mijeong, et al.
Pubblicazione: (2025)

Long‐Term Immersion Study for Durability of Interconnected Micropatterned Surfaces with Sustained Water Repellency (Adv. Mater. Interfaces 22/2024)
di: Seo Rim Park, et al.
Pubblicazione: (2024)

Non-equilibrium Annealed Adjoint Sampler
di: Choi, Jaemoo, et al.
Pubblicazione: (2025)

Merge and Bound: Direct Manipulations on Weights for Class Incremental Learning
di: Kim, Taehoon, et al.
Pubblicazione: (2025)

Revisiting Machine Unlearning with Dimensional Alignment
di: Seo, Seonguk, et al.
Pubblicazione: (2024)

4D Gaussian Splatting in the Wild with Uncertainty-Aware Regularization
di: Kim, Mijeong, et al.
Pubblicazione: (2024)

Cross-Class Feature Augmentation for Class Incremental Learning
di: Kim, Taehoon, et al.
Pubblicazione: (2023)

Leveraging Temporal Contextualization for Video Action Recognition
di: Kim, Minji, et al.
Pubblicazione: (2024)

The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler with a Near-Optimal L-Kernel
di: Devlin, Lee, et al.
Pubblicazione: (2021)