Knowledge-based Residual Learning
Authors: Guanjie Zheng, Chang Liu, Hua Wei, Porter Jenkins, Chacha Chen, Tao Wen, Zhenhui Li
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have shown the superior performance of KRL over baselines.We have conducted comprehensive experiments on real datasets from a variety of domains and have shown the superior performance of the proposed hybrid residual model.5 Experiment 5.1 Experiment Settings We conducted experiments on the following domains, covering regression, classification and information retrieval (IR) problems. Details and citations of the datasets can be found in the supplementary materials. |
| Researcher Affiliation | Academia | Guanjie Zheng1,2 , Chang Liu1, Hua Wei2, Porter Jenkins2, Chacha Chen2, Tao Wen3, Zhenhui Li2 1Shanghai Jiao Tong University 2The Pennsylvania State University 3Syracuse University |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | Weather [Brantley et al., 2008].Radi [TEPCO, 2019].Pend [Greydanus et al., 2019].Spring [Greydanus et al., 2019].Routing [Moreira-Matias et al., 2013].Loan [Kaggle, 2019]. |
| Dataset Splits | No | The paper uses phrases like 'train a neural net model' but does not provide specific training, validation, or test split percentages or sample counts for any of the datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming language versions, library versions like PyTorch or TensorFlow). |
| Experiment Setup | Yes | By default, all neural net models are composed of 3 hidden layers with 32 neurons. |