EVODiff: Entropy-aware Variance Optimized Diffusion Inference, Shigui Li, Wei Chen, Delu Zeng* [Bib]

NeurIPS 2025 | Paper | Code | News🎉

• Introduces an information-theoretic view: successful denoising reduces conditional entropy in reverse transitions.
• Proposes EVODiff, a reference-free diffusion inference framework that optimizes conditional variance to reduce transition and reconstruction errors, improving sample quality and reducing inference cost.

Entropy-informed weighting channel normalizing flow for deep generative models, Wei Chen#, Shian Du#, Shigui Li#, Delu Zeng*, John Paisley [Bib]

Pattern Recognition (PR) 2025 | Paper | Code

• Proposes EIW-Flow, enhancing normalizing flows with channel-wise weights and latent variable shuffling.
• Achieves state-of-the-art density estimation with minimal computational overhead.

ReciprocalLA-LLIE: Low-light image enhancement with luminance-aware reciprocal diffusion process, Zhiqi Lin, Wei Chen, Jian Xu, Delu Zeng*, Min Chen [Bib]

Neurocomputing 2025 | Paper

• Proposes a reciprocal diffusion process within DDPM for low-light image enhancement.
• Introduces Luminance Adjustment Block for robust global luminance control.

To-Flow: Efficient Continuous Normalizing Flows with Temporal Optimization Adjoint with Moving Speed, Shian Du#, Yihong Luo#, Wei Chen#, Jian Xu, Delu Zeng* [Bib]

CVPR 2022 | Paper | Code

• CNFs via neural ODEs are costly; To-Flow proposes temporal optimization via coordinate descent.
• Accelerates flow training by about 20% while maintaining performance.

A Minimum Variance Path Principle for Accurate and Stable Score-Based Density Ratio Estimation, Wei Chen, Jiacheng Li, Shigui Li, Zhiqi Lin, Junmei Yang, John Paisley, Delu Zeng* [Bib]

ICLR 2026 | Paper | Code

• Resolves the path schedule paradox in score-based DRE by identifying the overlooked path variance term.
• Proposes MVP Principle with closed-form variance expression for tractable optimization.
• Achieves state-of-the-art results on challenging DRE benchmarks.

Dequantified Diffusion-Schrödinger Bridge for Density Ratio Estimation, Wei Chen, Shigui Li, Jiacheng Li, Junmei Yang, John Paisley, Delu Zeng* [Bib]

ICML 2025 | Paper | Code

• Proposes D3RE, a unified framework for robust DRE under distribution mismatch.
• Introduces dequantified diffusion/SCHRödinger bridge interpolants for support expansion and stabilized scores.

DeepAR-Attention probabilistic prediction for stock price series, Jiacheng Li, Wei Chen, Zhiheng Zhou, Junmei Yang, Delu Zeng* [Bib]

Neural Computing and Applications 2024 | Paper

• Proposes DeepAR-Attention for probabilistic stock price forecasting.

Neural ordinary differential equation networks for fintech applications using IoT, Jiacheng Li, Wei Chen, Yican Liu, Junmei Yang, Delu Zeng*, Zhiheng Zhou [Bib]

IEEE Internet of Things Journal (IoTJ) 2024 | Paper

• Develops neural ODE network approaches for fintech applications in IoT settings.

Integrating Ordinary Differential Equations with Sparse Attention for Power Load Forecasting, Jiacheng Li, Wei Chen, Yican Liu, Junmei Yang, Zhiheng Zhou, Delu Zeng* [Bib]

IEEE Trans on Instrumentation and Measurement (T-IM) 2025 | Paper

• Proposes EvolvInformer: integrates ODE solver with ProbSparse attention for long-sequence load forecasting.
• Achieves 29.7% MSE reduction while preserving logarithmic memory complexity.

Generative Self-Supervised Time-Series Forecasting Leveraging Wavelet Diffusion, Jiacheng Li, Wei Chen, Yican Liu, Junmei Yang, Zhiheng Zhou, Delu Zeng* [Bib]

IEEE Trans on Instrumentation and Measurement (T-IM) 2025 | Paper | News🎉

• TimeWaveDiff: a lightweight self-supervised framework that integrates wavelet decomposition and diffusion modeling to capture multiscale periodicities and robustly learn complex noise patterns in measurement signals.
• Achieves superior long-term forecasting accuracy with significantly reduced computational cost.

Diffinformer: Diffusion Informer model for long sequence time-series forecasting, Jiacheng Li, Wei Chen, Yican Liu, Junmei Yang, Zhiheng Zhou, Delu Zeng* [Bib]

Expert Systems with Applications (ESWA) 2025 | Paper

• Proposes Diffinformer: combines conditional diffusion models with Informer’s ProbSparse attention.
• Demonstrates consistent improvements across five large-scale datasets.

RIKEN AIP - Tensor Learning Team

2026.02 - 2026.05