Gradient Population Optimization: A Tensorflow-Based Heterogeneous Non-Von-Neumann Paradigm for Large-Scale Search
Bibliographic record
Abstract
This paper presents a novel scalable algorithm, Gradient Population Optimization (GPO), which is specifically designed to optimize cost functions with extremely high dimensionality. GPO uses the Tensorflow platform, a non-von-Neumann computation model, which implements dataflow graphs on heterogeneous computing hardware (e.g., multi-core central processing unit, graphics processing unit (GPU), and field-programmable gate array) in order to perform massively parallel processing tasks on scalable platforms, such as the cloud. GPO is based on the combination of population-based dynamics with gradient-based determinism, in which a coupling term is introduced between the local and global corrections to the positions of population's agents' positions. The GPO exhibited excellent performance in most of the standard benchmark functions that were tested. In particular, GPO demonstrated superb scalability in solving largescale optimization problems using GPU-hardware-accelerated computing platform, positing the algorithm as an effective strategy for real-life massive scale problems, such as machine learning, data mining, and modeling wireless communication systems, such as 5G and massive MIMO.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".