Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity
Authors: Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P Adams
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate in simulation how it is possible to achieve translation, rotation, and shape matching, as well as a digital MNIST task. We additionally manufacture and evaluate one of the designs to verify its real-world behavior. |
| Researcher Affiliation | Academia | Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P. Adams Department of Computer Science Princeton University {doktay,mehranmir,em2368,rpa}@princeton.edu |
| Pseudocode | No | The paper describes methods and processes but does not include a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide a specific link to source code or an explicit statement about the release of their implementation code. |
| Open Datasets | Yes | Our input to the neural network is an image sampled from the MNIST dataset. |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits, exact percentages, or sample counts needed to reproduce the experiment. |
| Hardware Specification | Yes | Most computation was done on NVIDIA RTX 2080 GPUs. |
| Software Dependencies | No | The paper mentions 'JAX-based' simulator but does not provide specific version numbers for JAX or other key software components like Python. |
| Experiment Setup | Yes | The learning rate was 0.0001 M where M is the number of MPI tasks. In this case, we used 8 MPI tasks. The neural network was a fully-connected network with activation sizes: 2 30 30 10 2 (including input/output). The final layer was clipped by a tanh and multiplied by a maximum displacement of 60% of cell width. |