Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Weight Diffusion for Future: Learn to Generalize in Non-Stationary Environments
Authors: Mixue Xie, Shuang Li, Binhui Xie, Chi Liu, Jian Liang, Zixun Sun, Ke Feng, Chengwei Zhu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on both synthetic and real-world datasets show the superior generalization performance of W-Diff on unseen domains in the future. |
| Researcher Affiliation | Collaboration | Mixue Xie Beijing Institute of Technology EMAIL ... Jian Liang Kuaishou Technology EMAIL |
| Pseudocode | Yes | Algorithm 1: Training procedure for W-Diff ... Algorithm 2: Testing procedure for W-Diff |
| Open Source Code | Yes | Code is available at https://github.com/BIT-DA/W-Diff. |
| Open Datasets | Yes | Benchmark Datasets. We evaluate W-Diff on both synthetic and real-world datasets [2, 48], including two text classification datasets (Huffpost, Arxiv), three image classification datasets (Yearbook, RMNIST, f Mo W) and two multivariate classification datasets (2-Moons, ONP). ... For more details on datasets, please refer to Appendix D.1. |
| Dataset Splits | Yes | For each source domain, we randomly divide the data into training and validation sets in the ratio of 9 : 1. |
| Hardware Specification | Yes | All experiments are conducted using the Py Torch packages and run on a single NVIDIA Ge Force RTX 4090 GPU with 24GB memory. |
| Software Dependencies | No | The paper mentions 'Py Torch packages' but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For all datasets, we set the batch size B = 64, the loss tradeoff λ = 10 and the maximum length L = 8 for the reference point queue Qr. To optimize the task model, we adopt the Adam optimizer with momentum 0.9. As for the warm-up hyperparameter ρ, we ρ = 0.6 for Huffpost, f Mo W and ρ = 0.2 for Arxiv, Yearbook, RMNIST, 2-Moons, ONP. For the conditional diffusion model, we set the maximum diffusion step S = 1000 and use the Adam W optimizer with batch size M = 32... Training details on different datasets are given in Table 8. |