Latent Functional Maps: a spectral framework for representation alignment
Authors: Marco Fumero, Marco Pegoraro, Valentino Maiorca, Francesco Locatello, Emanuele Rodolà
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our framework on various applications, ranging from stitching to retrieval tasks, and on multiple modalities, demonstrating that Latent Functional Maps can serve as a swiss-army knife for representation alignment. |
| Researcher Affiliation | Academia | Marco Fumero IST Austria marco.fumero@ist.ac.at Marco Pegoraro Sapienza, University of Rome pegoraro@di.uniroma1.it Valentino Maiorca Sapienza, University of Rome maiorca@di.uniroma1.it Francesco Locatello IST Austria francesco.locatello@ist.ac.at Emanuele Rodolà Sapienza, University of Rome rodola@di.uniroma1.it |
| Pseudocode | No | The paper describes methods and processes in paragraph form, but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | No | We will share the code after the paper acceptance. All the data we use are open-source and publically available. |
| Open Datasets | Yes | We train 10 CNN models (the architecture is depicted in Appendix B.1) on the CIFAR-10 dataset [24], changing the initialization seed. |
| Dataset Splits | Yes | We use 2K random corresponding samples to construct the k-NN graphs and evaluate the retrieval performance on the remaining 18K word embeddings. |
| Hardware Specification | Yes | In all our experiments we used gpu rtx 3080ti and 3090. |
| Software Dependencies | No | The paper mentions general software like PyTorch but does not provide specific version numbers for any key software dependencies. |
| Experiment Setup | Yes | For each encoder, we compute a graph of 3,000 points with 300 neighbors per node. We optimize the problem in Equation 2 using the first 50 eigenvectors of the graph Laplacian and consider two different descriptors: the distance functions defined from the anchors (LFM+Ortho) and the labels (LFM+Ortho (Labels)). |