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].
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. |