Decoding Finger Movements from ECoG Signals Using Switching Linear Models

One of the most interesting challenges in ECoG-based Brain-Machine Interface is movement prediction. Being able to perform such a prediction paves the way to high-degree precision command for a machine such as a robotic arm or robotic hands. As a witness of the BCI community increasing interest toward such a problem, the fourth BCI Competition provides a dataset which aim is to predict individual finger movements from ECoG signals. The difficulty of the problem relies on the fact that there is no simple relation between ECoG signals and finger movements. We propose in this paper, to estimate and decode these finger flexions using switching models controlled by an hidden state. Switching models can integrate prior knowledge about the decoding problem and helps in predicting fine and precise movements. Our model is thus based on a first block which estimates which finger is moving and another block which, knowing which finger is moving, predicts the movements of all other fingers. Numerical results that have been submitted to the Competition show that the model yields high decoding performances when the hidden state is well estimated. This approach achieved the second place in the BCI competition with a correlation measure between real and predicted movements of 0.42

DEEP, a methodology for entity extraction using organizational patterns: application to job offers

International audiencePlain texts written in natural language may have several specific features, such as organizationalpatterns and an ambiguous and evolving vocabulary. From the literature, entity extractionapproaches are not sufficient to consider these specific features jointly. To address this issue,we propose DEEP, a methodology that improves the quality of entity extraction by usingorganizational patterns through a sequence labelling technique. To this end, DEEP creates ahigh-quality corpus and relies on an appropriate learning algorithm. DEEP is validated on a realcorpus of job offers. Experiments show that (1) considering organizational patterns improvesthe quality of entity extraction, (2) vocabulary evolution is taken into consideration andambiguity in vocabulary is reduced, (3) DEEP provides clear guidelines for the creation of ahigh-quality corpus for entity extraction, (4) the Bidirectional Long Short-Term Memory +Conditional Random Field architecture for sequence labelling is the one that takes the mostadvantage of the organizational patterns

Efficiently and Effectively Learning Models of Similarity from Human Feedback

Vital to the success of many machine learning tasks is the ability to reason about how objects relate. For this, machine learning methods utilize a model of similarity that describes how objects are to be compared. While traditional methods commonly compare objects as feature vectors by standard measures such as the Euclidean distance or cosine similarity, other models of similarity can be used that include auxiliary information outside of that which is conveyed through features. To build such models, information must be given about object relationships that is beneficial to the task being considered. In many tasks, such as object recognition, ranking, product recommendation, and data visualization, a model based on human perception can lead to high performance. Other tasks require models that reflect certain domain expertise. In both cases, humans are able to provide information that can be used to build useful models of similarity. It is this reason that motivates similarity-learning methods that use human feedback to guide the construction of models of similarity. Associated with the task of learning similarity from human feedback are many practical challenges that must be considered. In this dissertation we explicitly define these challenges as being those of efficiency and effectiveness. Efficiency deals with both making the most of obtained feedback, as well as, reducing the computational run time of the learning algorithms themselves. Effectiveness concerns itself with producing models that accurately reflect the given feedback, but also with ensuring the queries posed to humans are those they can answer easily and without errors. After defining these challenges, we create novel learning methods that explicitly focus on one or more of these challenges as a means to improve on the state-of-the-art in similarity-learning. Specifically, we develop methods for learning models of perceptual similarity, as well as models that reflect domain expertise. In doing so, we enable similarity-learning methods to be practically applied in more real-world problem settings