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High-probability grants
According to our matching algorithm, Thomas Hofmann is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2003 — 2006 |
Hofmann, Thomas |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Itr: Automatic Content Categorization and Annotation With Ambiguous Training Information
Machine learning algorithms are crucial for efficiently automating key tasks in content and information management such as content annotation and categorization, taxonomy creation, content linking, information routing and filtering, robust search, and information extraction. One key factor that has limited the success of machine learning methods in this domain is a conceptual mismatch: The formal assumptions with regard to the type and nature of available training information often differ from what is actually available in real-world applications.
This project addresses this mismatch by developing innovative machine learning algorithms and architectures that can make use of more realistic types of training information. This includes in particular the use of weakly labeled data, i.e. training data with ambiguous or incomplete annotations, and a systematic exploitation of dependencies between concepts and between concept annotations. The proposed research will lead to algorithms that have an increased range of applicability and that are more accurate and robust. The scope of the project encompasses well-known special cases like multiple instance learning, label ambiguity, learning with concept taxonomies, learning with overlapping concepts, and label sequence learning. As a proof of concept, tools for categorizing medical documents and for supporting content-based image search will be developed.
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1 |
2004 — 2007 |
Hofmann, Thomas Domini, Fulvio [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
A New Approach to the Problem of Cue-Integration For the Perception of 3d Shape
Long ago artists discovered how cues like linear perspective, shading, cast-shadows, and occlusions create the illusion of a three-dimensional world on a two-dimensional canvas. Likewise, vision scientists have long assumed human perception exploits similar depth-cues to construct a three-dimensional experience of visual objects. It has been postulated that depth perception is informed by separate and different depth-cues that may effectively add up to a unified percept. However, this conventional wisdom may fall short of the mark. With NSF support Dr. Fulvio Domini will test whether depth cues are intimately interrelated, whether perception is based on the relation among depth cues not cues considered separately. This bold hypothesis will be developed as a model and tested in experiments with human participants. Broader Impacts include implications of the basic research for studies of visual impairments in disease and aging, and for the development of robust computer vision algorithms.
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1 |