Bibliographic record
Abstract
The metaverse is a virtual world that exists entirely in a computer-generated environment, and it offers a new frontier for machine learning. One of the major challenges for using machine learning in the metaverse is MLOps (Machine Learning Operations), an emerging field that focuses on deploying and managing machine learning models in production. It has been widely acknowledged that machine learning models require a large amount of data to learn and make accurate predictions, and such data is generated progressively in real-time as human users interact with the metaverse. Due to the human-centric nature of the metaverse, it goes without saying that, once deployed, models need to be able to adapt to the constantly changing interactive environment and still make accurate predictions. Borrowing a page from software engineering, in this paper, we explore the design space of human-centric continuous integration in metaverse environments, where labeled data samples accumulated with explicit human interactive behavior (e.g., using virtual reality or augmented reality headsets) are used for fine-tuning a deployed deep learning model over a sustained period of time. We propose SPIN, a new mechanism that efficiently utilizes data samples collected from a large number of participating human users over time to fine-tune a deployed model that is shared across all the users. In an extensive array of experimental results using image classification and state-of-the-artYOLOv8object detection models as case studies, we show that SPIN outperforms FedBuff, a state-of-the-art asynchronous FL mechanism from conventional federated learning, by a substantial margin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".