Notice: 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. Full accuracy metrics and methodology are described in [1].
An SVD and Derivative Kernel Approach to Learning from Geometric Data
Authors: Eric Wong, J. Zico Kolter
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluated the method in the context of molecular energy prediction, showing good performance for modeling previously unseen molecular configurations. |
| Researcher Affiliation | Academia | Eric Wong School of Computer Science Carnegie Mellon University EMAIL J. Zico Kolter School of Computer Science Carnegie Mellon University EMAIL |
| Pseudocode | No | The paper describes algorithms in text and through mathematical derivations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not state that source code for the described methodology is publicly available. |
| Open Datasets | No | For both molecules, we generated 100 data points by adding noise to the original coordinates. |
| Dataset Splits | Yes | The kernel hyperparameters, namely exponential parameter γ and regularization parameter λ, were chosen by a grid-search with an inner 4-fold cross validation and optimized for the root mean squared error. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for experiments are mentioned in the paper. |
| Software Dependencies | No | all computations were carried out using the GPAW numerical code (Mortensen, Hansen, and Jacobsen 2005), a gridbased implementation of the DFT calculator. We also use the Python Atomic Simulation Environment (ASE) (Bahn and Jacobsen 2002) to set up the computations and later to perform the molecular optimization. |
| Experiment Setup | Yes | The kernel hyperparameters, namely exponential parameter γ and regularization parameter λ, were chosen by a grid-search with an inner 4-fold cross validation and optimized for the root mean squared error. We introduce an additional regularization parameter λderiv to account for the difference in magnitude between the energies and derivatives. |