Augmenting Transfer Learning with Semantic Reasoning
Authors: Freddy Lécué, Jiaoyan Chen, Jeff Z. Pan, Huajun Chen
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | It has demonstrated to be robust in two real-world applications: bus delay forecasting and air quality forecasting. St Ada B is evaluated by two Intra-domain transfer learning cases: (i) air quality forecasting from Beijing to Hangzhou (IBH), (ii) traffic condition prediction from London to Dublin (ILD), one Inter-domain case: (iii) from traffic condition prediction in London to air quality forecasting in Beijing (ILB). |
| Researcher Affiliation | Collaboration | Freddy L ecu e1,2 , Jiaoyan Chen3 , Jeff Z. Pan4,5 and Huajun Chen6,7 1Cort AIx Thales, Montreal, Canada 2Inria, Sophia Antipolis, France 3Department of Computer Science, University of Oxford, UK 4Department of Computer Science, The University of Aberdeen, UK 5Edinburgh Research Centre, Huawei, UK 6College of Computer Science, Zhejiang University, China 7ZJU-Alibaba Joint Lab on Knowledge Engine, China |
| Pseudocode | Yes | Algorithm 1: St Ada B( DS, TS , DT , TT , O T , G, L, N, α, β) |
| Open Source Code | No | The paper does not contain an explicit statement about releasing the source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | air quality data: https://bit.ly/2BUxKsi. See more about the application and data in [Chen et al., 2015]. |
| Dataset Splits | No | The paper mentions training and testing splits (e.g., "18 (resp. 5) months of observations are used as training (resp. testing)"), but it does not explicitly describe a separate validation split for the datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory specifications, or cloud instances) used to run the experiments. |
| Software Dependencies | No | The paper mentions the use of models like "Random Forest (RF), Stochastic Gradient Descent (SGD), Ada Boost (AB)", but it does not specify the version numbers of any software dependencies, libraries, or frameworks used. |
| Experiment Setup | Yes | We compare St Ada B (L = Logistic Regression, N = 800) with Transfer Ada Boost Tr AB [Dai et al., 2007]... All tasks are performed with a respective value of .3, .4, .7 for variability v(G, α, β). α and β are set to .5. |