AI News, NIPS 2010
- On Sunday, March 11, 2018
- By Read More
Examples include structured problems such as object detection and segmentation, natural language parsing and translation, as well as standard classification with kernelized or costly features or a very large number of classes.
These problems present a fundamental trade-off between approximation error (bias) and inference or search error due to computational constraints as we consider models of increasing complexity.
The primary aim of this workshop is to formally explore this trade-off and to unify a variety of recent approaches, which can be broadly described as coarse-to-fine methods, that explicitly learn to control this trade-off.
- On Friday, January 18, 2019
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