Weight Agnostic Neural Networks
Authors: Adam Gaier, David Ha
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 adam.gaier@h-brs.de David Ha Google Brain Tokyo, Japan hadavid@google.com |
| 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. |