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..
Weight Agnostic Neural Networks
Authors: Adam Gaier, David Ha
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training. On a supervised learning domain, we find network architectures that achieve much higher than chance accuracy on MNIST using random weights. Interactive version of this paper at https://weightagnostic.github.io/ |
| Researcher Affiliation | Collaboration | Adam Gaier Bonn-Rhein-Sieg University of Applied Sciences Inria / CNRS / Université de Lorraine EMAIL David Ha Google Brain Tokyo, Japan EMAIL |
| Pseudocode | No | The paper provides a diagrammatic overview of the search process in Figure 2 and describes the steps numerically (1), (2), (3), (4). However, it does not present these steps in a formal pseudocode or algorithm block format. |
| Open Source Code | Yes | We released a software toolkit not only to facilitate reproduction, but also to further research in this direction. Refer to the Supplementary Materials for more information about the code repository. |
| Open Datasets | Yes | We evaluate WANNs on three continuous control tasks. The first, Cart Pole Swing Up... The second task, Bipedal Walker-v2 [10]... The third, Car Racing-v0 [10]... As a proof of concept, we investigate how WANNs perform on the MNIST dataset [56] |
| Dataset Splits | No | The paper mentions evaluating performance (e.g., |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or cloud computing instance specifications). |
| Software Dependencies | No | The paper mentions using OpenAI Gym [10] and Keras [14] as part of its setup but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In these experiments we used a fixed series of weight values ([−2, −1, −0.5, +0.5, +1, +2]) to decrease the variance between evaluations. |