1,495 research outputs found
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
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