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 [1].
Online Tensor Max-Norm Regularization via Stochastic Optimization
Authors: Tong Wu
TMLR 2024 | Venue PDF | LLM Run Details
| 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. |