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].
Spectral Inference Networks: Unifying Deep and Spectral Learning
Authors: David Pfau, Stig Petersen, Ashish Agarwal, David G. T. Barrett, Kimberly L. Stachenfeld
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner. |
| Researcher Affiliation | Industry | David Pfau1, Stig Petersen1, Ashish Agarwal2, David G. T. Barrett1 & Kimberly L. Stachenfeld1 1Deep Mind 2Google Brain London, UK Mountain View, CA, USA EMAIL |
| Pseudocode | Yes | The full algorithm for training Spectral Inference Networks is given in Alg. 1, with Tensor Flow pseudocode in the supplementary material in Sec. B. |
| Open Source Code | Yes | Code is available at https://github.com/deepmind/spectral_inference_networks |
| Open Datasets | Yes | We provide a qualitative comparison of the performance of Sp IN with the SFA objective against the successor feature approach for learning eigenpurposes Machado et al. (2018) on the Arcade Learning Environment (Bellemare et al., 2013). |
| Dataset Splits | No | The paper does not explicitly describe training, validation, and test splits with specific percentages or counts. It mentions using 'held-out frames' for testing, but no distinct validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' in the supplementary material but does not specify a version number. |
| Experiment Setup | Yes | Details of the training network and experimental setup are given in the supplementary material in Sec. C.1. |