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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Fast Compressive Phase Retrieval under Bounded Noise
Authors: Hongyang Zhang, Shan You, Zhouchen Lin, Chao Xu
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real data verify our theory. We implement numerical simulations to evaluate the performance of the proposed Algorithm 1 and compare it with other competing methods. Synthetic Simulations and Real Experiment: Image Recovery. |
| Researcher Affiliation | Academia | 1Machine Learning Department, Carnegie Mellon University, U.S.A. 2Key Lab. of Machine Perception (MOE), School of EECS, Peking University, P. R. China 3 Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, P. R. China |
| Pseudocode | Yes | Algorithm 1 Robust Recovery of Sparse Signal by Phase Retrieval |
| Open Source Code | No | The paper states 'for the Recover step in Algorithm 1 we utilize the SPAMS toolbox (Mairal et al. 2009)' but does not provide any link or statement about releasing the source code for their own method. |
| Open Datasets | No | The paper describes generating synthetic data for simulations and uses a single image (from NASA) for a real-world example, but it does not specify any publicly available datasets used for training with access information (link, DOI, or formal citation). |
| Dataset Splits | No | The paper describes generating synthetic data for experiments and conducting '100 trials' to investigate recovery performance. However, it does not specify any training, validation, or test splits of a fixed dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. It only mentions 'laptop computer' in the context of memory limitations for other algorithms. |
| Software Dependencies | No | The paper states 'for the Recover step in Algorithm 1 we utilize the SPAMS toolbox (Mairal et al. 2009)' but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | We fix the dimension of the groundtruth sparse real signal x0 to be 256 (i.e. n = 256). We select the support size t in set {2, 4, 6, ..., 20} and vary the values of d and m to investigate the in๏ฌuence of their choices on the recovery accuracy of our method. All experiments are implemented over 100 trials and for the Recover step in Algorithm 1 we utilize the SPAMS toolbox (Mairal et al. 2009). We choose d = 2t(1+log n/t) , m = 10d, ฯ2 = 10^-2 in different t {6, 8, 10, 12, 14}. Specifically, d1 = 2t(1 + log n/t) and d2 = 2t log n/t. |