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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adaptive wavelet distillation from neural networks through interpretations
Authors: Wooseok Ha, Chandan Singh, Francois Lanusse, Srigokul Upadhyayula, Bin Yu
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In close collaboration with domain experts, we showcase how AWD addresses challenges in two real-world settings: cosmological parameter inference and molecular-partner prediction. In both cases, AWD yields a scientifically interpretable and concise model which gives predictive performance better than state-of-the-art neural networks. Moreover, AWD identifies predictive features that are scientifically meaningful in the context of respective domains. All code and models are released in a full-fledged package available on Github. 1 |
| Researcher Affiliation | Academia | 1 Statistics Department, UC Berkeley 2 EECS Department, UC Berkeley 3 AIM, CEA, CNRS; Université Paris-Saclay, Université Paris Diderot, Sorbonne Paris Cité 4 Advanced Bioimaging Center, Department of Molecular & Cell Biology, UC Berkeley |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All code and models are released in a full-fledged package available on Github. 1 github.com/Yu-Group/adaptive-wavelets |
| Open Datasets | Yes | We use a recently published dataset [50] which tags two molecules: clathrin light chain A, which is used as the predictor variable, and auxilin 1, the target variable. ... We train a DNN7 to predict m from 100,000 mass maps simulated with 10 different sets of cosmological parameter values at the universe origin from the Massive Nu S simulations [62] (full simulation details given in Appendix D). |
| Dataset Splits | Yes | The hyperparameters for AWD are selected by evaluating the predictive model s performance on a held-out validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments beyond a general acknowledgment of "AWS computing credits". |
| Software Dependencies | No | The paper mentions "Py Wavelets package [44]" and "Pytorch Wavelets [45, Chapter 3] package" but does not specify their version numbers, which are necessary for reproducibility. |
| Experiment Setup | Yes | Fig 2 shows the best learned wavelet (for one particular run) extracted by AWD corresponding to the setting of hyperparameters λ = 0.005 and γ = 0.043. ... The hyperparameters for AWD are selected by evaluating the predictive model s performance on a held-out validation set. |