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].
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation
Authors: Kazu Ghalamkari, Mahito Sugiyama
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Numerical Experiments We empirically examined the ef๏ฌciency and the effectiveness of LTR using synthetic and real-world datasets. We compared LTR with two existing non-negative low Tucker-rank approximation methods. |
| Researcher Affiliation | Academia | Kazu Ghalamkari1,2 Mahito Sugiyama1,2 1National Institute of Informatics 2The Graduate University for Advanced Studies, SOKENDAI EMAIL |
| Pseudocode | Yes | Algorithm 1: input :Tensor P, target Tucker rank r = (r1, . . . , rd) output :Rank reduced tensor Q LTR(P,r) ... BESTRANK1(P) ... |
| Open Source Code | No | The paper does not provide any explicit statement about releasing the source code for their proposed method (LTR), nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluated running time and the LS reconstruction error for two real-world datasets. 4DLFD is a (9, 9, 512, 512, 3) tensor [18] and Att Face is a (92, 112, 400) tensor [33]. |
| Dataset Splits | No | The paper describes the generation of synthetic data and the characteristics of real-world datasets (4DLFD and Att Face), along with the target Tucker ranks used for experiments. However, it does not provide details on specific training, validation, or test dataset splits used for evaluation. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., CPU, GPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions comparison methods (NTD_KL, NTD_LS, lra SNTD) and states 'see Supplement for implementation details' for them. However, it does not explicitly list any specific software dependencies or their version numbers for the proposed LTR method or the experimental setup in the main text. |
| Experiment Setup | Yes | For the 4DLFD dataset, we chose the target Tucker rank as (1,1,1,1,1), (2,2,2,2,1), (3,3,4,4,1), (3,3,5,5,1), (3,3,6,6,1), (3,3,7,7,1), (3,3,8,8,1), (3,3,16,16,1), (3,3,20,20,1), (3,3,40,40,1), (3,3,60,60,1), and (3,3,80,80,1). For the Att Face dataset, we chose (1,1,1), (3,3,3), (5,5,5), (10,10,10), (15,15,15), (20,20,20), (30,30,30), (40,40,40), (50,50,50), (60,60,60), (70,70,70), and (80,80,80). Also, in Algorithm 1, it specifies 'Construct {c1, . . . , crk} [Ik] by random sampling from [Ik] without replacement'. |