We show that the algorithm to extract diverse M -solutions from a Conditional Random Field (called divMbest [1]) takes exactly the form of a Herding procedure [2], i.e. a deterministic dynamical system that produces a sequence of hypotheses that respect a set of observed moment constraints. This generalization enables us to invoke properties of Herding that show that divMbest enforces implausib...

In highly dynamic and adversarial domains such as sports, short-term predictions are made by incorporating both local immediate as well global situational information. For forecasting complex events, higher-order models such as Hidden Conditional Random Field (HCRF) have been used to good effect as capture the long-term, high-level semantics of the signal. However, as the prediction is based so...

This paper presents Japanese morphological analysis based on conditional random fields (CRFs). Previous work in CRFs assumed that observation sequence (word) boundaries were fixed. However, word boundaries are not clear in Japanese, and hence a straightforward application of CRFs is not possible. We show how CRFs can be applied to situations where word boundary ambiguity exists. CRFs offer a so...

Eukaryotic genomes are packaged by the wrapping of DNA around histone octamers to form nucleosomes. Nucleosome occupancies together with their acetylation and methylation are important modification factors on all nuclear processes involving DNA. There have been recently many studies of mapping these modifications in DNA sequences and of relationship between them and various genetic activities, ...

Many learning tasks have subtasks for which much training data exists. Therefore, we want to transfer learning from the old, generalpurpose subtask to a more specific new task, for which there is often less data. While work in transfer learning often considers how the old task should affect learning on the new task, in this paper we show that it helps to take into account how the new task affec...

The visual analysis of human manipulation actions is of interest for e.g. human-robot interaction applications where a robot learns how to perform a task by watching a human. In this paper, a method for classifying manipulation actions in the context of the objects manipulated, and classifying objects in the context of the actions used to manipulate them is presented. Hand and object features a...

Automatic image classification is of major importance for a wide range of applications and is supported by a complex process that usually requires the identification of individual regions and spatial patterns (contextual information) among neighboring regions within images. Hierarchical conditional random fields (CRF) consider both multi-scale and contextual information in a unified discriminat...

Now, we would like to know what happens when y itself is a sequence? (i.e want P (y|x)). Traditionally, graphical models were used to represent the joint probability P (y, x). This however, can lead to difficulties. In the presence of rich local features in the relational data the distribution P (x) needs to be modelled, which can include complex dependencies. A solution to this is to directly ...

Social roles characterize relation between participants in a conversation and, in turn, influence their interaction patterns. This paper investigates automatic social role recognition in professional meetings using a completely discriminative framework based on conditional random fields. We present a novel approach which combines information from multiple layers of data. The conversation layer ...

In American Sign Language (ASL) the structure of signed sentences is conveyed by grammatical markers which are represented by facial feature movements and head motions. Without recovering grammatical markers, a sign language recognition system cannot fully reconstruct a signed sentence. However, this problem has been largely neglected in the literature. In this paper, we propose to use a 2-laye...