Data collection and quality challenges for deep learning
Bibliographic record
Abstract
Software 2.0 refers to the fundamental shift in software engineering where using machine learning becomes the new norm in software with the availability of big data and computing infrastructure. As a result, many software engineering practices need to be rethought from scratch where data becomes a first-class citizen, on par with code. It is well known that 80--90% of the time for machine learning development is spent on data preparation. Also, even the best machine learning algorithms cannot perform well without good data or at least handling biased and dirty data during model training. In this tutorial, we focus on data collection and quality challenges that frequently occur in deep learning applications. Compared to traditional machine learning, there is less need for feature engineering, but more need for significant amounts of data. We thus go through state-of-the-art data collection techniques for machine learning. Then, we cover data validation and cleaning techniques for improving data quality. Even if the data is still problematic, hope is not lost, and we cover fair and robust training techniques for handling data bias and errors. We believe that the data management community is well poised to lead the research in these directions. The presenters have extensive experience in developing machine learning platforms and publishing papers in top-tier database, data mining, and machine learning venues.
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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 | 0.057 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".