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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Stable and Efficient Representation Learning with Nonnegativity Constraints
Authors: Tsung-Han Lin, H. T. Kung
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we use a multi-layer deep architecture for representation learning, where we use K-means for feature learning and NOMP for representation encoding. |
| Researcher Affiliation | Academia | Tsung-Han Lin EMAIL H. T. Kung EMAIL School of Engineering and Applied Sciences, Harvard University, Cambridge MA, USA |
| Pseudocode | Yes | NOMP iterates the following steps for up to k rounds: 1. Initialize the residual vector r(0) = x and round number l = 1. Select the atom dil that has the highest positive correlation with the residual, il = arg maxi di, r(l 1) . Terminate if the largest correlation is less than or equal to zero. 2. Approximate the coefficients of the selected atoms by nonnegative least squares. z(l) = arg minz x Pl h=1 dihzih 2 s.t. zih 0 3. Compute the new residual r(l) = x Dz(l). Increment l by 1. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it contain explicit statements about code release or links to repositories. |
| Open Datasets | Yes | We use a dictionary learned from 6 6 images patches from CIFAR-10. We first evaluate NOMP using the full CIFAR-10 dataset... The CIFAR-100 dataset is one of such datasets... Finally, we evaluate NOMP on the STL-10 dataset... |
| Dataset Splits | Yes | We choose k as 20 and 5 for NOMP and OMP, respectively, in the experiments.9 This choice is cross-validated over k = {1, 3, 5, 10, 20}. We report accuracy from the 5-fold cross validation on the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cluster specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers required to replicate the experiment. |
| Experiment Setup | Yes | Standard preprocessing steps are applied on image data to generate data vectors for layer-1. These include mean subtraction, contrast normalization , and ZCA-whitening, followed by sign-splitting as described in Section 3.2. We encode 6 6 patches, pool the sparse codes over the four quadrants of an image, and concatenate the four representations. We choose k as 20 and 5 for NOMP and OMP, respectively, in the experiments. The patch sizes and features sizes are 6 6, 9 9, 15 15, and 3200, 6400, 6400 for the three layers, respectively. we employ a linear classifier (L2-SVM). |