Archive for Projects

Advanced Structured Prediction Book


S. Nowozin, J. Jancsary, P. V. Gehler, C. H. Lampert (editors)

(project page)

The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components.

These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning.

Regression Tree Fields code released


I am happy to announce that our reference implementation of the Regression Tree Fields model is now publicly available on the Microsoft Research downloads website:

The code has been used to obtain state-of-the-art results in low-level image processing tasks such as natural image denoising and natural image deblurring, but has meanwhile also become useful for discrete labeling tasks such as semantic segmentation and even part-of-speech tagging.

We put a lot of effort into the release and hope that the academic community will find it useful. Enjoy! – Jeremy