Select-and-Sample for Spike-and-Slab Sparse Coding
Authors: Abdul-Saboor Sheikh, Jörg Lücke
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Numerical Experiments. We first investigate the accuracy and convergence properties of our method on ground-truth data. we turned to verifying the approach on a denoising benchmark. Fig. 1C,D show the obtained results and a comparison to alternative approaches. Here we applied S5C with H = 10 000 hidden dimensions to demonstrate scalability of the method |
| Researcher Affiliation | Collaboration | Abdul-Saboor Sheikh Technical University of Berlin, Germany, and Cluster of Excellence Hearing4all University of Oldenburg, Germany, and SAP Innovation Center Network, Berlin sheikh.abdulsaboor@gmail.com Jörg Lücke Research Center Neurosensory Science and Cluster of Excellence Hearing4all and Dept. of Medical Physics and Acoustics University of Oldenburg, Germany joerg.luecke@uol.de |
| Pseudocode | Yes | Algorithm 1: Select-and-sample for spike-and-slab sparse coding (S5C) |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-sourcing the code for the methodology described. |
| Open Datasets | Yes | For our application we used the standard van Hateren database [22], extracted N = 10^6 image patches of size 16x16, and applied pseudo-whitening following [21]. We applied S5C using a noisy house image [following 11, 4, 8, 7]. |
| Dataset Splits | No | The paper describes the total number of data points used for training, but does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation setup). |
| Hardware Specification | No | The paper states 'We used a multi-core parallelized implementation and executed the algorithm on up to 1000 CPU cores' but does not specify the exact CPU models, types, or other hardware details. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | Yes | In all the experiments, the initial values of π were drawn from a uniform distribution on the interval [0.1, 0.5]..., µ was initialized with normally distributed random values, ψh was set to 1 and σd was initialized with the standard deviation of yd. The elements of W were iid drawn from a normal distribution with zero mean and a standard deviation of 5.0. We apply the S5C algorithm (Alg. 1) with H = 10 latents and M = 40 samples per data point and use two settings for preselection: (A) no preselection (H = H = 10) and (B) subspace preselection using H = 5. We applied the S5C algorithm with H = 256, select subspaces with H = 40 and used M = 100 samples per subspace. applied S5C for 50 EM iterations to the data using H = 20 dimensional subspaces and M = 50 samples per data point. |