Parallel Backpropagation for Shared-Feature Visualization
Authors: Alexander Lappe, Anna Bognár, Ghazaleh Ghamkahri Nejad, Albert Mukovskiy, Lucas Martini, Martin Giese, Rufin Vogels
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the algorithm to novel recordings from body-selective regions in macaque IT cortex in order to understand why some images of objects excite these neurons. and We show results for a novel set of multi-unit recordings from body-selective regions in macaque superior temporal sulcus. and 4 Experimental setup |
| Researcher Affiliation | Academia | Alexander Lappe1,2 Anna Bognár3 Ghazaleh Ghamkhari Nejad3 Albert Mukovskiy1 Lucas Martini1,2 Martin A. Giese1 Rufin Vogels3 1Hertie Institute, University Clinics Tübingen 2IMPRS-IS 3 KU Leuven |
| Pseudocode | No | The paper describes the procedure in text and with a diagram (Figure 2), but does not contain a formal pseudocode or algorithm block. |
| Open Source Code | Yes | Code and data necessary to reproduce all results given in the paper are included in the submission. |
| Open Datasets | Yes | The second set comprised 6,857 objects from varying categories shown on the same gray background and was combined from the Open Images dataset [33], as well as several smaller ones [34, 35]. |
| Dataset Splits | Yes | We split the monkey image set into a training/validation/test split consisting of 400/50/25 images. |
| Hardware Specification | Yes | All experiments were run on a single Nvidia RTX 2080Ti. |
| Software Dependencies | No | Models and training runs, as well as the visualization procedure were implemented in Py Torch [38]. The paper mentions PyTorch but does not provide a specific version number. |
| Experiment Setup | Yes | We set the learning rate to 10 4 and the weight of the regularization to 0.1 After training for 2500 epochs, we selected the model with lowest loss on the validation set. |