Hypothesis Transfer Learning via Transformation Functions
Authors: Simon S. Du, Jayanth Koushik, Aarti Singh, Barnabas Poczos
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real world data demonstrate the effectiveness of our framework. |
| Researcher Affiliation | Academia | Simon S. Du Carnegie Mellon University ssdu@cs.cmu.edu |
| Pseudocode | Yes | Algorithm 1 Transformation Function based Transfer Learning |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We use two datasets from the kin family in Delve [Rasmussen et al., 1996]. |
| Dataset Splits | Yes | Hyper-parameters were picked using grid search with 10-fold cross-validation on the target data (or source domain data when not using the target domain data). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software components or libraries used. |
| Experiment Setup | Yes | We set nso to 320, and vary nta in {10, 20, 40, 80, 160, 320}. Hyper-parameters were picked using grid search with 10-fold cross-validation on the target data (or source domain data when not using the target domain data). |