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..
Set Prediction in the Latent Space
Authors: Konpat Preechakul, Chawan Piansaddhayanon, Burin Naowarat, Tirasan Khandhawit, Sira Sriswasdi, Ekapol Chuangsuwanich
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several set prediction tasks, including image captioning and object detection, demonstrate the effectiveness of our method. |
| Researcher Affiliation | Academia | 1Department of Computer Engineering, Chulalongkorn University 2Department of Mathematics, Faculty of Sciences, Mahidol University 3Computational Molecular Biology Group, Faculty of Medicine, Chulalongkorn University |
| Pseudocode | Yes | Algorithm 1 Single training step of Latent Set Prediction (LSP) and Algorithm 2 Gradient Cloning with Rejection (GCR) |
| Open Source Code | Yes | Code is available at https://github.com/phizaz/latent-set-prediction. |
| Open Datasets | Yes | We used our modified MNIST dataset [22] in this experiment. We re-purposed the CLEVR dataset [23]... We used MIMIC-CXR dataset [16] |
| Dataset Splits | No | The paper mentions '5,000 training and 1,000 test images' for the modified MNIST dataset, but does not explicitly provide validation splits for any of the datasets used. |
| Hardware Specification | No | The paper mentions 'We included a typical training time for a run on all experiments' but does not specify the type of GPUs, CPUs, or other hardware used. |
| Software Dependencies | No | The paper mentions software like 'Hugging Face s transformers' and 'spacy' but does not provide specific version numbers for these or other software dependencies required for replication. |
| Experiment Setup | No | The paper describes some general aspects of the experimental setup, such as dataset sizes and some task-specific details (e.g., predicting 10 sentences), but it does not provide specific hyperparameters like learning rate, batch size, or optimizer settings within the provided text. |