Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Neural Experts: Mixture of Experts for Implicit Neural Representations
Authors: Yizhak Ben-Shabat, Chamin Hewa Koneputugodage, Sameera Ramasinghe, Stephen Gould
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the effectiveness of our approach on multiple reconstruction tasks, including surface reconstruction, image reconstruction, and audio signal reconstruction and show improved performance compared to non-Mo E methods. |
| Researcher Affiliation | Collaboration | Yizhak Ben-Shabat Roblox, The Australian National University EMAIL Chamin Hewa Koneputugodage The Australian National University EMAIL Sameera Ramasinghe Amazon, Australia EMAIL Stephen Gould The Australian National University EMAIL |
| Pseudocode | No | The paper describes the architecture and method steps in text and diagrams, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code is available at our project page https://sitzikbs.github.io/neural-experts-projectpage/. |
| Open Datasets | Yes | We conduct a comprehensive evaluation of image reconstruction on the full Kodak dataset [12] (24 images) in Table 1. |
| Dataset Splits | No | The paper describes training for 30K iterations and then training only experts for a final period, but it does not specify a distinct validation dataset or split used for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | These models were trained on an NVIDIA A5000 GPU. ... We run our surface reconstruction experiments on a single RTX 3090 (24GB VRAM). |
| Software Dependencies | No | The paper mentions using an "Adam optimizer" and implicitly deep learning frameworks, but it does not specify software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, CUDA 11.x). |
| Experiment Setup | Yes | For our approach we used 4 experts, 2 hidden layers for encoder and 2 for the experts. The manager has a similar architecture with 2 layers for the manager encoder and 2 for the final manager block. Each layer has 128 elements. All models were trained using an Adam optimizer, a learning rate of 10 5 with exponential decay. All models are trained for 30K iterations where for our approach we use tall = 80% and te = 20%. |