Exponential Family Predictive Representations of State
Bibliographic record
Abstract
2008 To my wife, Martha. ii Acknowledgments This work would not have been possible without generous help, both intellectually and financially. I am grateful to my advisor, Satinder Singh, for the long discussions we have had as he has patiently taught me to think clearly through my own ideas, sharpen my writing, and to raise my sights. A special thanks also to my lab mates, Matt Rudary, Britton Wolfe, Vishal Soni, Erik Talviti, Jonathan Sorg and Ishan Chaudhuri for always letting me bounce ideas around, for listening, and for patient tutoring. Thanks to Andrew Nuxoll for being a kindred spirit, to Nick Gorski for the occasional foosball game and to my collaborators at the University of Alberta. Finally, I would like to gratefully acknowledge the National Science Foundation for financially supporting me through most of my studies with a Graduate Research Fellowship. Finally, a special thank you to my wife Martha for her love, her constancy, her feistiness and for always keeping me on the straight and narrow. Thank you, Grace, Peterson and Andrew for reminding
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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".