171 citations · 694 across the 32 of their papers we have counts for
7 papers · 1 filter
Curriculum Learning of Multiple Tasks
Anastasia Pentina, Viktoriia Sharmanska, Christoph H. Lampert
Sharing information between multiple tasks enables algorithms to achieve good generalization performance even from small amounts of training data. However, in a realistic scenario…
Learning to Transfer Privileged Information
Viktoriia Sharmanska, Novi Quadrianto, Christoph H. Lampert
We introduce a learning framework called learning using privileged information (LUPI) to the computer vision field. We focus on the prototypical computer vision problem of teaching…
A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly max-Oracle
Neel Shah, Vladimir Kolmogorov, Christoph H. Lampert
Structural support vector machines (SSVMs) are amongst the best performing models for structured computer vision tasks, such as semantic image segmentation or human pose estimation…
Mind the Nuisance: Gaussian Process Classification using Privileged Noise
Daniel Hernández-Lobato, Viktoriia Sharmanska, Kristian Kersting +2
The learning with privileged information setting has recently attracted a lot of attention within the machine learning community, as it allows the integration of additional knowled…
Predicting the Future Behavior of a Time-Varying Probability Distribution
Christoph H. Lampert
We study the problem of predicting the future, though only in the probabilistic sense of estimating a future state of a time-varying probability distribution. This is not only an i…
Proceedings of The 38th Annual Workshop of the Austrian Association for Pattern Recognition (ÖAGM), 2014
Vladimir Kolmogorov, Christoph Lampert, Emilie Morvant +1
The 38th Annual Workshop of the Austrian Association for Pattern Recognition (ÖAGM) will be held at IST Austria, on May 22-23, 2014. The workshop provides a platform for researcher…
Closed-Form Training of Conditional Random Fields for Large Scale Image Segmentation
Alexander Kolesnikov, Matthieu Guillaumin, Vittorio Ferrari +1
We present LS-CRF, a new method for very efficient large-scale training of Conditional Random Fields (CRFs). It is inspired by existing closed-form expressions for the maximum like…