Bibliographic record
Abstract
Message from the General ChairThere is excitement about the return to in-person conferences in the computer-architecture community.The 29 th IEEE International Symposium on High-Performance Computer Architecture, taking place in Montreal in February/March 2023, provides evidence to the growth in research initiatives in computer architecture.A high-mark for the number of paper submissions and acceptances ---with 91 papers out of 364 manuscript submissions selected by the Program Committee for presentation at HPCA ---led us to have three parallel tracks for HPCA instead of the two parallel tracks that we had the last time that we got together in person in San Diego in February of 2020.HPCA is planned to be an in-person-only conference to bring back all the benefits of gathering with fellow researchers and students to discuss ideas and research trends.Issues that may prevent all the authors of a paper from presenting in person are dealt on a case-by-case basis.The overall direction is that if none of the authors of a paper can attend, then the authors should try to identify a colleague who is able to deliver an in-person presentation of the paper.We are thankful to those who volunteered to present their colleague's work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.077 | 0.059 |
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".