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..
Proximal Deep Structured Models
Authors: Shenlong Wang, Sanja Fidler, Raquel Urtasun
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our approach in the tasks of image denoising, depth refinement and optical flow estimation. |
| Researcher Affiliation | Academia | Shenlong Wang University of Toronto EMAIL Sanja Fidler University of Toronto EMAIL Raquel Urtasun University of Toronto EMAIL |
| Pseudocode | Yes | Figure 2: Algorithm for learning proximal deep structured models. (Contains a numbered list of steps) |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We use the BSDS image dataset [23]. (for image denoising) We conduct the depth refinement experiment on the 7 Scenes dataset [25]. (for depth refinement) We evaluate the task of optical flow estimation on the Flying Chairs dataset [11]. (for optical flow) |
| Dataset Splits | No | The paper mentions using training and testing subsets but does not explicitly provide specific details for a validation dataset split. |
| Hardware Specification | Yes | Our experiments are conducted on a Xeon 3.2 Ghz machine with a Titan X GPU. |
| Software Dependencies | Yes | We employ mxnet [4] with CUDNNv4 acceleration to implement the networks |
| Experiment Setup | Yes | We use mean square error as the loss function and set a weight decay strength of 0.0004 for all settings. MSRA initialization [16] is used for the convolution parameters and the initial gradient step for each iteration is set to be 0.02. We use adam [19] with a learning rate of t = 0.02 and hyper-parameters β1 = 0.9 and β2 = 0.999 as in Kingma et al. [19]. The learning rate is divided by 2 every 50 epoch, and we use a mini-batch size of 32. |