Convolutional Imputation of Matrix Networks
Authors: Qingyun Sun, Mengyuan Yan, David Donoho, boyd
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the algorithm with a variety of applications such as MRI and Facebook user network. (Abstract) and 7. Experimental results (Section heading) |
| Researcher Affiliation | Academia | 1Department of Mathematics, Stanford University, California, USA 2Department of Electrical Engineering, Stanford University, California, USA 3Department of Statistics, Stanford University, California, USA. |
| Pseudocode | Yes | Iterative Imputation: input PΩ(A). Initialization Aest 0 = 0, t = 0. for λ1 > λ2 > . . . > λC, where λj = (λj k), k = 1, . . . , N do Aimpute = PΩ(A) + P Ω(Aest t ). ˆAimpute = UAimpute. ˆAest t+1(k) = Sλj k( ˆAimpute(k)). Aest t+1 = U 1 ˆAest t+1. t=t+1. until Aest t Aest t 1 2/ Aest t 1 2 < ϵ. Assign Aλj = Aest t . end for output The sequence of solutions Aλ1, . . . , AλC. (Section 6) |
| Open Source Code | No | The paper does not contain an explicit statement offering the source code for its methodology, nor does it provide a link to a public repository. |
| Open Datasets | Yes | We take the ego networks from the SNAP Facebook dataset (33). (Section 7) (Reference 33: Jure Leskovec and Julian J Mcauley. Learning to discover social circles in ego networks. Advances in neural information processing systems, pages 539 547, 2012.) |
| Dataset Splits | No | The paper describes sampling schemes to generate partial observations (e.g., 'sampled each frame of the MRI images with i.i.d. Bernoulli distribution p = 0.2, and 2 out of 88 frames are completely unobserved'), but it does not specify conventional train/validation/test dataset splits for model training or hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions related software like 'softimpute' (25), but it does not specify any software dependencies (e.g., libraries, frameworks) with version numbers used for its own implementation or experiments. |
| Experiment Setup | Yes | The sequence of regularization parameters is chosen such that λ1 k > λ2 k > . . . > λC k for each k. The solution for each iteration with λs is a warm start for the next iteration with λs+1. Our recommended choice is to choose λ1 k as the largest singular value for ˆAimpute(k), and decay λs at a constant speed λs+1 = cλs. (Section 6) and adding i.i.d. Gaussian noise with mean 0 and variance σ2/50 to all observed entries. (Section 7, Facebook network) |