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..
SCoRe: Submodular Combinatorial Representation Learning
Authors: Anay Majee, Suraj Nandkishor Kothawade, Krishnateja Killamsetty, Rishabh K Iyer
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on several long-tail vision benchmarks to show the effectiveness of objectives in SCo Re. |
| Researcher Affiliation | Collaboration | Anay Majee 1 Suraj Kothawade 2 Krishnateja Killamsetty 3 Rishabh Iyer 1...1Department of Computer Science, The University of Texas at Dallas, Richardson, TX, USA 2Google Research, Sunnyvale, CA, USA 3IBM Research, San Jose, CA, USA. Correspondence to: Anay Majee <EMAIL>, Rishabh Iyer <EMAIL>. |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | Yes | We train all our models on 2 NVIDIA A6000 GPUs with code released at https://github. com/amajee11us/SCo Re.git. |
| Open Datasets | Yes | We perform experiments on several long-tail vision benchmarks... CIFAR-10-LT... CIFAR-100-LT... Image Net-LT introduced in (Liu et al., 2019)... Med MNIST (Yang et al., 2023)... IDD (Varma et al., 2019)... LVIS (Gupta et al., 2019)... |
| Dataset Splits | Yes | The training dataset comprises a total of 100,000 images, encompassing 1.3 million instances, and the validation set contains 20,000 images. |
| Hardware Specification | Yes | We train all our models on 2 NVIDIA A6000 GPUs with code released at https://github. com/amajee11us/SCo Re.git. |
| Software Dependencies | No | The paper mentions 'Detectron2 framework' but does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | For stage 1 we train a Res Net-50 backbone with a batch size of 512 (1024 after augmentations) with an initial learning rate of 0.4, trained for 1000 epochs with a cosine annealing scheduler and a temperature for the combinatorial objectives to be 0.7. |