Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Tensor Max-Norm Regularization via Stochastic Optimization
Authors: Tong Wu
TMLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 23,464 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,469 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments demonstrate encouraging results for the effectiveness and robustness of our algorithm. The code is available at https://github.com/twugithub/2024-TMLR-OMRTD. ... In this section, we present several experimental results on both synthetic and real data. |
| Researcher Affiliation | Industry | Tong Wu EMAIL Beijing Institute for General Artificial Intelligence |
| Pseudocode | Yes | Algorithm 1 t-SVD for third-order tensors ... Algorithm 2 Online Max-Norm Regularized Tensor Decomposition ... Algorithm 3 Updating tensor columns of M, R and E ... Algorithm 4 Data Projection (Problem (10)) ... Algorithm 5 Bisection Method for Solving Problem (12) ... Algorithm 6 The Update of L |
| Open Source Code | Yes | The code is available at https://github.com/twugithub/2024-TMLR-OMRTD. |
| Open Datasets | Yes | on the CAMO-UOW dataset (Li et al., 2017) for video background subtraction. |
| Dataset Splits | No | The dataset contains 10 real video sequences and we use all these sequences for both qualitative and quantitative analysis. No specific train/test/validation splits are mentioned for model training or evaluation on the dataset. |
| Hardware Specification | Yes | All experiments are conducted on a PC with an AMD Ryzen 9 5950X 3.40GHz CPU and 64GB RAM with Matlab R2023b. |
| Software Dependencies | Yes | All experiments are conducted on a PC with an AMD Ryzen 9 5950X 3.40GHz CPU and 64GB RAM with Matlab R2023b. |
| Experiment Setup | Yes | We set λ1 = λ2 = 1/ n1 for OMRTD/r OMRTD, and we follow the default parameter settings for the baselines. ... (we set ϵ = 0.01 in our experiments). ... Initialize: R(0) = E (0) = J (0) = 0, γ = 1.9, µ(0) = 0.1, µmax = 1010, ε = 10 6, and ζ = 0. |