Opposition-based Multi-Objective ADAM Optimizer (OMAdam) for Training ANNs
Bibliographic record
Abstract
Multi-loss functions are present in various aspects of deep learning. In multi-modal, cross-modal, and multi-task learning contexts, multi-loss functions are essential elements for handling complex data with diverse information sources. Different tasks or modalities may have conflicting objectives. By combining them into a single loss function, the model might struggle to strike the right balance between these objectives, leading to suboptimal performance. The Multi-objective Adam optimizer, also referred to as MAdam, is an extension of Adam optimizer that is applied for optimizing several competing loss functions in deep learning. The MAdam algorithm exhibits sensitivity to its initialization, necessitating the injection of ex-treme points into the initial population. Additionally, this scheme encounters difficulties in effectively capturing the disconnected and non-convex Pareto fronts. In this paper, an opposition-based scheme was introduced into MAdam framework as global search is necessary for escaping local optima in gradient-based multi-objective optimization approaches. The Opposition-based MAdam, explores multiple directions over the landscape, that leads to independence from specific initialization. In a series of experiments, we demonstrate the scalability of our method by capturing the entire Pareto front using the MNIST dataset for binary classification of digit images 2 and 3. This was achieved with a fully connected network, employing multi-objective mean absolute error and binary cross-entropy as losses. OMAdam matches Adam's Fl-score in the early generations, a result to its high exploratory capacity which enhances its performance in initial stages of classification tasks. This results in a reduction of computational costs compared to both Adam and MAdam. The variation in Fl-score values along the Pareto front trajectory enables practitioners to select a post hoc solution based on the trade-offs achieved among conflicting loss functions as multiple objectives. This contrasts with Adam, which offers limited options due to its single-solution approach.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".