Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.221 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
We describe a probabilistic approach to the task of embedding highdimensional objects into a low-dimensional space in a way that preserves neighbor identities. A Gaussian is centered on each object in the highdimensional space and the densities under this Gaussian are used to define a probability distribution over all the potential neighbors of the object.
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.
The record
- Venue
- Topic
- Neural Networks and Applications
- Field
- Computer Science
- Canadian institutions
- University of Toronto
- Funders
- —
- Keywords
- EmbeddingProbabilistic logicDimensionality reductionGaussianArtificial intelligenceComputer sciencePairwise comparisonObject (grammar)Pattern recognition (psychology)Curse of dimensionalityProbability distributionMathematicsStatistics
- Has abstract in OpenAlex
- yes