UJM at INEX 2009 Ad Hoc track

7 pagesInternational audienceThis paper1 presents our participation to the INEX 2009 Ad- Hoc track. We have experimented the tuning of various parameters using a ”training” collection (i.e. INEX 2008) quite different than the ”testing” collection used for 2009 INEX Ad-Hoc track. Several parameters have been studied for article retrieval as well as for element retrieval, especially the two main BM25 weighting function parameters: b and k1

An exact relation between Eulerian and Lagrangian velocity increment statistics

We present a formal connection between Lagrangian and Eulerian velocity increment distributions which is applicable to a wide range of turbulent systems ranging from turbulence in incompressible fluids to magnetohydrodynamic turbulence. For the case of the inverse cascade regime of two-dimensional turbulence we numerically estimate the transition probabilities involved in this connection. In this context we are able to directly identify the processes leading to strongly non-Gaussian statistics for the Lagrangian velocity increments.Comment: 5 pages, 3 figure

Learning to Learn from Weak Supervision by Full Supervision

In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model, we train two neural networks: a target network, the learner and a confidence network, the meta-learner. The target network is optimized to perform a given task and is trained using a large set of unlabeled data that are weakly annotated. We propose to control the magnitude of the gradient updates to the target network using the scores provided by the second confidence network, which is trained on a small amount of supervised data. Thus we avoid that the weight updates computed from noisy labels harm the quality of the target network model.Comment: Accepted at NIPS Workshop on Meta-Learning (MetaLearn 2017), Long Beach, CA, US