Learning Robust Rule Representations for Abstract Reasoning via Internal Inferences
Authors: Wenbo Zhang, likai tang, Site Mo, Xianggen Liu, Sen Song
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we propose a novel framework, ARII, that learns rule representations for Abstract Reasoning via Internal Inferences. ... We evaluate ARII on two benchmark datasets, including PGM and I-RAVEN. We observe that ARII achieves new state-of-the-art records on the majority of the reasoning tasks, including most of the generalization tests in PGM. Our codes are available at https://github.com/Zhangwenbo0324/ARII. |
| Researcher Affiliation | Academia | 1College of Computer Science, Sichuan University 2Department of Biomedical Engineering, Tsinghua University 3Laboratory of Brain and Intelligence, Tsinghua University 4College of Electrical Engineering, Sichuan University |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our codes are available at https://github.com/Zhangwenbo0324/ARII. |
| Open Datasets | Yes | We evaluate ARII on two benchmark datasets, including PGM and I-RAVEN. ... The PGM [5] together with the RAVEN dataset [7] expands the size of RPM instances through automatic generation algorithms, serving as the benchmark datasets for deep learning network to study abstract reasoning. However, Hu et al. [8] found severe defects (a short cut for predictions) existing in the RAVEN dataset and created an unbiased version called I-RAVEN dataset to solve it. Therefore, we evaluate our method on the PGM and I-RAVEN rather than RAVEN. |
| Dataset Splits | No | The paper mentions training and testing sets for PGM ("neutral where the training and test set are sampled from the same distribution") but does not explicitly state the use of a validation set or specific percentages for any data splits in the provided text. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions using convolutional neural networks (CNNs) and neural network models but does not provide specific version numbers for any software dependencies, libraries, or programming languages used. |
| Experiment Setup | No | The paper does not explicitly provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed system-level training settings in the main text. It mentions lambda values for optimization but not their specific numerical values. |