34,036 research outputs found
Convex Banding of the Covariance Matrix
We introduce a new sparse estimator of the covariance matrix for
high-dimensional models in which the variables have a known ordering. Our
estimator, which is the solution to a convex optimization problem, is
equivalently expressed as an estimator which tapers the sample covariance
matrix by a Toeplitz, sparsely-banded, data-adaptive matrix. As a result of
this adaptivity, the convex banding estimator enjoys theoretical optimality
properties not attained by previous banding or tapered estimators. In
particular, our convex banding estimator is minimax rate adaptive in Frobenius
and operator norms, up to log factors, over commonly-studied classes of
covariance matrices, and over more general classes. Furthermore, it correctly
recovers the bandwidth when the true covariance is exactly banded. Our convex
formulation admits a simple and efficient algorithm. Empirical studies
demonstrate its practical effectiveness and illustrate that our exactly-banded
estimator works well even when the true covariance matrix is only close to a
banded matrix, confirming our theoretical results. Our method compares
favorably with all existing methods, in terms of accuracy and speed. We
illustrate the practical merits of the convex banding estimator by showing that
it can be used to improve the performance of discriminant analysis for
classifying sound recordings
Improving carbon cycle projections for better carbon management
Forests absorb large amounts of carbon from the atmosphere through photosynthesis and store a significant fraction of the carbon in biomass and soils. A March 2016 workshop focused on how best to use modeling approaches, field measurements, and satellite observations to improve projections of carbon cycle dynamics in response to climate change and human activities
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
Event extraction is of practical utility in natural language processing. In
the real world, it is a common phenomenon that multiple events existing in the
same sentence, where extracting them are more difficult than extracting a
single event. Previous works on modeling the associations between events by
sequential modeling methods suffer a lot from the low efficiency in capturing
very long-range dependencies. In this paper, we propose a novel Jointly
Multiple Events Extraction (JMEE) framework to jointly extract multiple event
triggers and arguments by introducing syntactic shortcut arcs to enhance
information flow and attention-based graph convolution networks to model graph
information. The experiment results demonstrate that our proposed framework
achieves competitive results compared with state-of-the-art methods.Comment: accepted by EMNLP 201
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