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7 results for set-continuity

The output scores of a neural network classifier are converted to probabilities via normalizing over the scores of all competing categories. Computing this partition function, ZZ, is then linear in the number of categories, which is problematic as real-world problem sets continue to grow in categorical types, such as …

2015-08-07abs ↗pdf ↗

Branching Flows generates sequences of varying lengths using binary trees.

problem Generating sequences of unknown lengths or fixed elements.
method A generative modeling framework that evolves states over binary trees, controlling sequence length.
result Branching Flows can generate sequences of varying lengths and mix different types of state spaces.

A new method helps deep learning systems adapt to changing conditions.

problem Deep learning systems struggle with environmental drifts and long healing cycles.
method Intentional forgetting integrated into continual learning to overcome issues.
result Dr. DRL reduces healing time and fine-tuning episodes by 18.74% and 17.72% respectively.

New algorithm tackles multiclass transductive online learning with unbounded labels.

problem Characterizing optimal mistake bound for unbounded label spaces.
method Introducing new combinatorial dimensions (Level-constrained Littlestone and Branching dimensions) to characterize online learnability.
result Established trichotomy of possible minimax rates for unbounded label spaces: Θ(T)Θ(T), Θ(logT)Θ(\log T), or Θ(1)Θ(1).