Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks
Authors: Sung Moon Ko, Sumin Lee, Dae-Woong Jeong, Woohyung Lim, Sehui Han
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To test our algorithms, we used a total of 14 datasets from three different open databases named Pub Chem(Kim et al., 2022), Ochem(Sushko et al., 2011), and CCCB(III, 2022)...For the evaluation, we compare the performance of GATE against that of single task learning (STL), MTL, KD, global structure preserving loss based KD (GSPKD) (Joshi et al., 2022), and transfer learning (retrain all or head network only). |
| Researcher Affiliation | Industry | Sung Moon Ko , Sumin Lee , Dae-Woong Jeong , Woohyung Lim, Sehui Han LG AI Research {sungmoon.ko, sumin.lee, dw.jeong, w.lim, hansse.han}@lgresearch.ai |
| Pseudocode | Yes | Algorithm 1 GATE |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the source code for the described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | To test our algorithms, we used a total of 14 datasets from three different open databases named Pub Chem(Kim et al., 2022), Ochem(Sushko et al., 2011), and CCCB(III, 2022), as described in Appendix Table 3. |
| Dataset Splits | Yes | The training and test split is an 80:20 ratio, and two sets of data were prepared: random split and scaffold-based split(Bemis & Murcko, 1996). Every experiment is tested in a fourfold cross-validation setting with uniform sampling for accurate evaluation |
| Hardware Specification | Yes | Every experiment is tested in a fourfold cross-validation setting with uniform sampling for accurate evaluation, and a single NVIDIA A40 is used for the experiments. |
| Software Dependencies | No | The paper mentions using Adam W for optimization and DMPNN as a backbone architecture. However, it does not specify version numbers for these or any other software components (e.g., Python, PyTorch, TensorFlow) required for reproducibility. |
| Experiment Setup | Yes | We trained 600 epochs with batch size 512 while using Adam W (Loshchilov & Hutter, 2017) for optimization with learning rate 5e-5. The hyperparameters for α, β, γ, δ are 1, 1, 1, 1 respectively. |