The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry
Authors: Dian Wang, Jung Yeon Park, Neel Sortur, Lawson L.S. Wong, Robin Walters, Robert Platt
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show empirically that this is indeed the case and that an inaccurate equivariant model is often better than a completely unstructured model. For example, suppose we want to model a function with the object-wise rotation symmetry expressed in Figure 1a and b. Notice that whereas it is difficult to encode the object-wise symmetry, it is easy to encode an image-wise symmetry because it involves simple image rotations. Although the image-wise symmetry model is imprecise in this situation, our experiments indicate that this imprecise model is still a much better choice than a completely unstructured model. |
| Researcher Affiliation | Academia | Dian Wang, Jung Yeon Park, Neel Sortur, Lawson L.S. Wong, Robin Walters , Robert Platt Northeastern University {wang.dian,park.jungy,sortur.n,l.wong,r.walters,r.platt}@northeastern.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Supplementary video and code are available at https://pointw.github.io/extrinsic_page/. |
| Open Datasets | Yes | We use the environments provided by the Bullet Arm benchmark (Wang et al., 2022b) implemented in the Py Bullet simulator (Coumans & Bai, 2016). |
| Dataset Splits | Yes | In all training, we perform a three-way data split with N training data, 200 holdout validation data, and 200 holdout test data. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments, such as specific GPU or CPU models. |
| Software Dependencies | Yes | We implement the training in Py Torch (Paszke et al., 2017) using a cross-entropy loss. ... We use the Adam optimizer (Kingma & Ba, 2014)... We use the environments provided by the Bullet Arm benchmark (Wang et al., 2022b) implemented in the Py Bullet simulator (Coumans & Bai, 2016). |
| Experiment Setup | Yes | The pixel size of the image is 152x152 (and will be cropped to 128x128 during training). We implement the training in Py Torch (Paszke et al., 2017) using a cross-entropy loss. ... We use the Adam optimizer (Kingma & Ba, 2014) with a learning rate of 10^-4. The batch size is 64. |