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 [1].
Learning Proximal Operators to Discover Multiple Optima
Authors: Lingxiao Li, Noam Aigerman, Vladimir Kim, Jiajin Li, Kristjan Greenewald, Mikhail Yurochkin, Justin Solomon
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further present an exhaustive benchmark for multi-solution optimization to demonstrate the effectiveness of our method. |
| Researcher Affiliation | Collaboration | Lingxiao Li MIT CSAIL EMAIL Noam Aigerman Adobe Research EMAIL Vladimir G. Kim Adobe Research EMAIL Jiajin Li Stanford University EMAIL Kristjan Greenewald IBM Research, MIT-IBM Watson AI Lab EMAIL Mikhail Yurochkin IBM Research, MIT-IBM Watson AI Lab EMAIL Justin Solomon MIT CSAIL EMAIL |
| Pseudocode | No | The paper describes the proposed methods but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | The complete source code for all experiments can be found at https://github.com/lingxiaoli94/POL. |
| Open Datasets | Yes | We apply the above MSO formulation to the COCO2017 dataset (Lin et al., 2014). |
| Dataset Splits | Yes | We use the training and validation split of COCO2017 (Lin et al., 2014) as the training and test dataset, keeping only images with at most 10 ground truth bounding boxes. |
| Hardware Specification | Yes | All training is done on a single NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions software like 'PyTorch' and 'DGCNN' but does not specify their version numbers or other key software dependencies with specific versions. |
| Experiment Setup | Yes | In each training iteration of POL and GOL, we sample 32 problem parameters from the training dataset of T , and 256 of x s from unif(X)... The learning rate of the operator is kept at 10 4 for both POL and GOL, and by default we train the operator network for 2 105 iterations. |