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 Interpretable Unrolled Dictionary Learning
Authors: Bahareh Tolooshams, Demba E. Ba
TMLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our findings through synthetic and image denoising experiments. Finally, we demonstrate PUDLE s interpretability, a driving factor in designing deep networks based on iterative optimizations, by building a mathematical relation between network weights, its output, and the training set. |
| Researcher Affiliation | Academia | Bahareh Tolooshams EMAIL Demba Ba EMAIL School of Engineering and Applied Sciences Harvard University |
| Pseudocode | Yes | Algorithm 1: Classical alternating-minimization-based dictionary learning using lasso (1). Algorithm 2: PUDLE: Provable unrolled dictionary learning framework. |
| Open Source Code | Yes | 1Source code is available at https://github.com/btolooshams/stable-interpretable-unrolled-dl |
| Open Datasets | Yes | We trained on 432 and tested on 68 images from BSD (Martin et al., 2001). ... We focused on digits of {0, 1, 2, 3, 4} MNIST. |
| Dataset Splits | Yes | We trained on 432 and tested on 68 images from BSD (Martin et al., 2001). |
| Hardware Specification | Yes | PUDLE is developed using Py Torch (Paszke et al., 2017). We used one Ge Force GTX 1080 Ti GPU. |
| Software Dependencies | Yes | PUDLE is developed using Py Torch (Paszke et al., 2017). ... with Adam optimizer (Kingma & Ba, 2014) ... We used linear sum assignment optimization (i.e., scipy.optimize.linear_sum_assignment) |
| Experiment Setup | Yes | We let T = 200, λ = 0.2, and α = 0.2. The network is trained for 600 epochs with full-batch gradient descent using Adam optimizer (Kingma & Ba, 2014) with learning rate of 10-3 and ϵ = 10-8. ... We trained PUDLE where the dictionary is convolutional with 64 filters of size 9 × 9 and strides of 4. The encoder unrolls for T = 15, and the step size is set to α = 0.1. ... trained stochastically with Adam optimizer (Kingma & Ba, 2014) with a learning rate of 10-4 and ϵ = 10-3 for 250 epochs. |