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..
A Bridging Framework for Model Optimization and Deep Propagation
Authors: Risheng Liu, Shichao Cheng, xiaokun liu, Long Ma, Xin Fan, Zhongxuan Luo
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments verify our theoretical results and demonstrate the superiority of PODM against these state-of-the-art approaches. |
| Researcher Affiliation | Academia | Risheng Liu1,2 , Shichao Cheng3, Xiaokun Liu1, Long Ma1, Xin Fan1,2, Zhongxuan Luo2,3 1International School of Information Science & Engineering, Dalian University of Technology 2Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province 3School of Mathematical Science, Dalian University of Technology |
| Pseudocode | No | The paper describes the iterative processes and model components but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link for the availability of open-source code for the methodology described. |
| Open Datasets | Yes | We plotted the iteration behaviors of PODM on example images from the commonly used Set5 super-resolution benchmark [4] and compared it with the most popular numerical solvers (e.g., FISTA [3]) and the recently proposed representative network based iteration methods (e.g., IRCNN [33]). We ๏ฌrst conducted experiments on the most widely used Levin et al. benchmark [15], with 32 blurry images of size 255 255. We also evaluated all these compared methods on the more challenging Sun et al. benchmark [28], which includes 640 blurry images with 1% Gaussian noises, sizes range from 620 1024 to 928 1024. |
| Dataset Splits | No | The paper mentions using common benchmark datasets but does not explicitly state the training, validation, or test data splits, such as percentages, sample counts, or specific splitting methodologies. |
| Hardware Specification | Yes | All the experiments are conducted on a PC with Intel Core i7 CPU @ 3.6 GHz, 32 GB RAM and an NVIDIA Ge Force GTX 1060 GPU. |
| Software Dependencies | No | The paper mentions general architectural components like 'CNN architecture' and 'multilayer perceptron' and references 'ELU [6] activations', but does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | No | The paper describes the network architecture (e.g., '6 convolutional layers', 'ELU activations') and some settings like 'H = ยตI/2 with ยต = 1e 2' for PODM, but does not provide specific hyperparameter values like learning rates, batch sizes, or optimizer settings needed for full experimental reproducibility. |