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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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%. |