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].
Testing and Learning on Distributions with Symmetric Noise Invariance
Authors: Ho Chung Law, Christopher Yau, Dino Sejdinovic
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 5 provides experiments on synthetic and real data, before concluding in section 6. |
| Researcher Affiliation | Academia | Ho Chung Leon Law Department of Statistics University Of Oxford EMAIL Christopher Yau Centre for Computational Biology University of Birmingham EMAIL Dino Sejdinovic Department of Statistics University Of Oxford EMAIL |
| Pseudocode | Yes | To do this, we can construct a neural network (NN) with special activation functions, pooling layers as shown in Algorithm D.1 and Figure D.1 in the Appendix. |
| Open Source Code | No | The paper does not provide any explicit statements or links about the availability of source code for the methodology described. |
| Open Datasets | Yes | To demonstrate the phase features invariance to SPD noise component, we use the Aerosol MISR dataset also studied by [24] and [25] and consider a situation with covariate shift [18] on distribution inputs: the testing data is impaired by additive SPD components different to that in the training data. |
| Dataset Splits | Yes | For experiments, we use approximately 9000 bags for training, and 3000 bags each for validation and testing, keeping those of multiple lines of sight in the same set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | In the SME test, we take the number of test locations J to be 10, and use 20% of the samples to optimise the test locations. |