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..
Discrete Dictionary-based Decomposition Layer for Structured Representation Learning
Authors: Taewon Park, Hyun-Chul Kim, Minho Lee
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate that D3 significantly improves the systematic generalization of various TPR-based models while requiring fewer additional parameters. Notably, D3 outperforms baseline models on the synthetic task that demands the systematic decomposition of unseen combinatorial data. |
| Researcher Affiliation | Collaboration | Taewon Park1 Hyun-Chul Kim1 Minho Lee1,2 1Kyungpook National University, South Korea 2ALI Co., Ltd., South Korea |
| Pseudocode | No | The paper describes a 'three-step process' but does not present it as formal pseudocode or an algorithm block. |
| Open Source Code | Yes | The code of D3 is publicly available at https://github.com/taewonpark/D3 |
| Open Datasets | Yes | The SAR task [23] evaluates systematic generalization in memorizing and recalling combinatorial data. The sys-b Ab I task [23] is a variant of the b Ab I task [42] designed to evaluate systematic generalization in text understanding and reasoning. The sort-of-CLEVR task [26] evaluates compositional generalization in visual relational reasoning. The Wiki Text-103 task [19] is a language modeling dataset consisting of lengthy corpora from Wikipedia. |
| Dataset Splits | Yes | The sys-b Ab I task uses the en-valid-10k version, which is already divided into training, validation, and test datasets. The Wiki Text-103 task comprises 28,475 articles for training, 60 for validation, and 60 for testing. |
| Hardware Specification | Yes | Each experiment was conducted on a single 48GB NVIDIA RTX A6000 GPU and an AMD EPYC 7513 32-Core Processor. |
| Software Dependencies | No | The paper mentions general software like 'Adam optimizer' but does not provide specific version numbers for any key software components or libraries (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We train the model using the Adam optimizer with a batch size of 64 and a learning rate of 1e 3, β1 of 0.9, and β2 of 0.98 for training iterations of 30K. |