Structured Prediction Cascades
Add to your list(s)
Download to your calendar using vCal
If you have a question about this talk, please contact Simon Lacoste-Julien.
Structured prediction tasks pose a fundamental bias-computation
trade-off: The need for complex models to increase predictive power
on the one hand and the limited computational resources for inference
in the exponentially-sized output spaces on the other. We formulate
and develop structured prediction cascades to address this trade-off:
a sequence of increasingly complex models that progressively filter the
space of possible outputs. We represent an exponentially large set of
filtered outputs using max marginals and propose a novel convex loss
for learning cascades that balances filtering error with filtering efficiency.
We provide generalization bounds for error and efficiency losses and
evaluate our approach on several natural language and vision problems.
We find that the learned cascades are capable of reducing the complexity
of inference by up to several orders of magnitude, enabling the use of
models which incorporate higher order dependencies and features and
yield significantly higher accuracy.
This talk is part of the Machine Learning @ CUED series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
|