:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Lu, Cheng, Song, Yang
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2410.11081
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency
by: Zheng, Kaiwen, et al.
Published: (2025)

Model Sparsity Can Simplify Machine Unlearning
by: Jia, Jinghan, et al.
Published: (2023)

Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
by: Hu, Jiaxi, et al.
Published: (2024)

Simplifying Graph Convolutional Networks with Redundancy-Free Neighbors
by: Lu, Jielong, et al.
Published: (2025)

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers
by: Liu, Hongbo
Published: (2024)

Stabilized Neural Prediction of Potential Outcomes in Continuous Time
by: Hess, Konstantin, et al.
Published: (2024)

Simplified PCNet with Robustness
by: Li, Bingheng, et al.
Published: (2024)

Simplifying Transformer Blocks
by: He, Bobby, et al.
Published: (2023)

Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training
by: Chang, Che-Chia, et al.
Published: (2026)

SDE: A Simplified and Disentangled Dependency Encoding Framework for State Space Models in Time Series Forecasting
by: Weng, Zixuan, et al.
Published: (2024)

Interpretability Illusions in the Generalization of Simplified Models
by: Friedman, Dan, et al.
Published: (2023)

Riemannian Consistency Model
by: Cheng, Chaoran, et al.
Published: (2025)

Beyond State Consistency: Behavior Consistency in Text-Based World Models
by: Huang, Youling, et al.
Published: (2026)

On the Power of (Approximate) Reward Models for Inference-Time Scaling
by: Zhu, Youheng, et al.
Published: (2026)

Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling
by: Yuan, Haochen, et al.
Published: (2026)

Dynamic Load Model for Data Centers with Pattern-Consistent Calibration
by: Lu, Siyu, et al.
Published: (2026)

On Simplifying Large-Scale Spatial Vectors: Fast, Memory-Efficient, and Cost-Predictable k-means
by: Ji, Yushuai, et al.
Published: (2024)

Simplifying the Theory on Over-Smoothing
by: Roth, Andreas
Published: (2024)

Simplifying Graph Kernels for Efficient
by: Wang, Lin, et al.
Published: (2025)

Towards Non-Stationary Time Series Forecasting with Temporal Stabilization and Frequency Differencing
by: Lu, Junkai, et al.
Published: (2025)

Consistent Diffusion Language Models
by: Amin, Hasan, et al.
Published: (2026)

FoCTTA: Low-Memory Continual Test-Time Adaptation with Focus
by: Hu, Youbing, et al.
Published: (2025)

Score $\times$ Decoder: A Unified View of Unsupervised Inference-Time Scaling for Hallucination Mitigation
by: Cheng, Yun-Chen, et al.
Published: (2026)

Simplifying Latent Dynamics with Softly State-Invariant World Models
by: Saanum, Tankred, et al.
Published: (2024)

Diffusion Models under Alternative Noise: Simplified Analysis and Sensitivity
by: Choi, Juhyeok, et al.
Published: (2025)

Boosting Continuous Control with Consistency Policy
by: Chen, Yuhui, et al.
Published: (2023)

CCD: Continual Consistency Diffusion for Lifelong Generative Modeling
by: Liu, Jingren, et al.
Published: (2025)

Simplifying Knowledge Transfer in Pretrained Models
by: Jain, Siddharth, et al.
Published: (2025)

Small-to-Large Generalization: Data Influences Models Consistently Across Scale
by: Khaddaj, Alaa, et al.
Published: (2025)

Reverse Distillation: Consistently Scaling Protein Language Model Representations
by: Catrina, Darius, et al.
Published: (2026)

Simplifying Deep Temporal Difference Learning
by: Gallici, Matteo, et al.
Published: (2024)

Adam Simplified: Bias Correction Debunked
by: Laing, Sam, et al.
Published: (2025)

Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data
by: Kim, Sehwan, et al.
Published: (2025)

Optimize Cardinality Estimation Model Pretraining by Simplifying the Training Datasets
by: Fang, Boyang
Published: (2025)

Continued AI Scaling Requires Repeated Efficiency Doublings
by: Lu, Chien-Ping
Published: (2026)

Understanding the Role of Rehearsal Scale in Continual Learning under Varying Model Capacities
by: He, JinLi, et al.
Published: (2026)

Robust Uncertainty Quantification for Self-Evolving Large Language Models via Continual Domain Pretraining
by: Zhou, Xiaofan, et al.
Published: (2025)

Rod Flow: A Continuous-Time Model for Gradient Descent at the Edge of Stability
by: Regis, Eric, et al.
Published: (2026)

veScale: Consistent and Efficient Tensor Programming with Eager-Mode SPMD
by: Li, Youjie, et al.
Published: (2025)

A Study of Posterior Stability for Time-Series Latent Diffusion
by: Li, Yangming, et al.
Published: (2024)