47,689 research outputs found
Spectrally-normalized margin bounds for neural networks
This paper presents a margin-based multiclass generalization bound for neural
networks that scales with their margin-normalized "spectral complexity": their
Lipschitz constant, meaning the product of the spectral norms of the weight
matrices, times a certain correction factor. This bound is empirically
investigated for a standard AlexNet network trained with SGD on the mnist and
cifar10 datasets, with both original and random labels; the bound, the
Lipschitz constants, and the excess risks are all in direct correlation,
suggesting both that SGD selects predictors whose complexity scales with the
difficulty of the learning task, and secondly that the presented bound is
sensitive to this complexity.Comment: Comparison to arXiv v1: 1-norm in main bound refined to
(2,1)-group-norm. Comparison to NIPS camera ready: typo fixe
Optimal Estimation and Prediction for Dense Signals in High-Dimensional Linear Models
Estimation and prediction problems for dense signals are often framed in
terms of minimax problems over highly symmetric parameter spaces. In this
paper, we study minimax problems over l2-balls for high-dimensional linear
models with Gaussian predictors. We obtain sharp asymptotics for the minimax
risk that are applicable in any asymptotic setting where the number of
predictors diverges and prove that ridge regression is asymptotically minimax.
Adaptive asymptotic minimax ridge estimators are also identified. Orthogonal
invariance is heavily exploited throughout the paper and, beyond serving as a
technical tool, provides additional insight into the problems considered here.
Most of our results follow from an apparently novel analysis of an equivalent
non-Gaussian sequence model with orthogonally invariant errors. As with many
dense estimation and prediction problems, the minimax risk studied here has
rate d/n, where d is the number of predictors and n is the number of
observations; however, when d is roughly proportional to n the minimax risk is
influenced by the spectral distribution of the predictors and is notably
different from the linear minimax risk for the Gaussian sequence model
(Pinsker, 1980) that often appears in other dense estimation and prediction
problems.Comment: 29 pages, 0 figure
Prediction and Signal Extraction of Strong Dependent Processess in the Frequency Domain
We frequently observe that one of the aims of time series analysts is to predict future values of the data. For weakly dependent data, when the model is known up to a finite set of parameters, its statistical properties are well documented and exhaustively examined. However, if the model was misspecified, the predictors would no longer be correct. Motivated by this observation and due to the interest in obtaining adequate and reliable predictors, Bhansali (1974) examined the properties of a nonparametric predictor based on the canonical factorization of the spectral density function given in Whittle (1963) and known as FLES. However, the above work does not cover the so-called strongly dependent data. Due to the interest in this type of process, one of our objectives in this paper is to examine the properties of the FLES for these processes. In addition, we illustrate how the FLES can be adapted to recover the signal of a strongly dependent process, showing its consistency. The proposed method is semiparametric, in the sense that, in contrast to other methods, we do not need to assume any particular model for the noise except that it is weakly dependent.Prediction, strong dependence, spectral density function, canonical factorization, signal extraction.
Historical forest biomass dynamics modelled with Landsat spectral trajectories
Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin
The Spitzer Atlas of Stellar Spectra
We present the Spitzer Atlas of Stellar Spectra (SASS), which includes 159
stellar spectra (5 to 32 mic; R~100) taken with the Infrared Spectrograph on
the Spitzer Space Telescope. This Atlas gathers representative spectra of a
broad section of the Hertzsprung-Russell diagram, intended to serve as a
general stellar spectral reference in the mid-infrared. It includes stars from
all luminosity classes, as well as Wolf-Rayet (WR) objects. Furthermore, it
includes some objects of intrinsic interest, like blue stragglers and certain
pulsating variables. All the spectra have been uniformly reduced, and all are
available online. For dwarfs and giants, the spectra of early-type objects are
relatively featureless, dominated by Hydrogen lines around A spectral types.
Besides these, the most noticeable photospheric features correspond to water
vapor and silicon monoxide in late-type objects and methane and ammonia
features at the latest spectral types. Most supergiant spectra in the Atlas
present evidence of circumstellar gas. The sample includes five M supergiant
spectra, which show strong dust excesses and in some cases PAH features.
Sequences of WR stars present the well-known pattern of lines of HeI and HeII,
as well as forbidden lines of ionized metals. The characteristic flat-top shape
of the [Ne III] line is evident even at these low spectral resolutions. Several
Luminous Blue Variables and other transition stars are present in the Atlas and
show very diverse spectra, dominated by circumstellar gas and dust features. We
show that the [8]-[24] Spitzer colors (IRAC and MIPS) are poor predictors of
spectral type for most luminosity classes.Comment: Accepted by ApJS; Atlas contents available from:
http://web.ipac.caltech.edu/staff/ardila/Atlas/index.html;
http://irsa.ipac.caltech.edu/data/SPITZER/SASS/; 70 PDF pages, including
figure
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