Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Effective Abstract Reasoning with Dual-Contrast Network
Authors: Tao Zhuo, Mohan Kankanhalli
ICLR 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,931 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,549 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the RAVEN and PGM datasets show that DCNet outperforms the state-of-the-art methods by a large margin of 5.77%. Further experiments on few training samples and model generalization also show the effectiveness of DCNet. |
| Researcher Affiliation | Academia | Tao Zhuo, Mohan Kankanhalli School of Computing, National University of Singapore EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/visiontao/dcnet. |
| Open Datasets | Yes | Similar to the previous works (Zhang et al., 2019b; Zheng et al., 2019; Wang et al., 2020), we conduct experiments on the RAVEN (Zhang et al., 2019a) and PGM (Santoro et al., 2018). |
| Dataset Splits | Yes | In each configuration, the dataset is randomly split into three parts, 6 folds for training, 2 for validation, and the remaining 2 for testing. |
| Hardware Specification | Yes | In addition, all models are trained and evaluated on a single GPU of NVIDIA Ge Force 1080 Ti with 11 GB memory. |
| Software Dependencies | No | The paper mentions the use of "Adam optimizer (Kingma & Ba, 2015)" but does not specify software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | During the training phase, a mini-batch size of 32 with Adam optimizer (Kingma & Ba, 2015) is employed to learn the network parameters, and the learning rate is set to 0.001 and fixed. |