4,257 research outputs found
Nonparametric Estimation of Multi-View Latent Variable Models
Spectral methods have greatly advanced the estimation of latent variable
models, generating a sequence of novel and efficient algorithms with strong
theoretical guarantees. However, current spectral algorithms are largely
restricted to mixtures of discrete or Gaussian distributions. In this paper, we
propose a kernel method for learning multi-view latent variable models,
allowing each mixture component to be nonparametric. The key idea of the method
is to embed the joint distribution of a multi-view latent variable into a
reproducing kernel Hilbert space, and then the latent parameters are recovered
using a robust tensor power method. We establish that the sample complexity for
the proposed method is quadratic in the number of latent components and is a
low order polynomial in the other relevant parameters. Thus, our non-parametric
tensor approach to learning latent variable models enjoys good sample and
computational efficiencies. Moreover, the non-parametric tensor power method
compares favorably to EM algorithm and other existing spectral algorithms in
our experiments
Three-Body Recombination near a Narrow Feshbach Resonance in 6 Li
We experimentally measure and theoretically analyze the three-atom recombination rate,
L3, around a narrow s-wave magnetic Feshbach resonance of 6Li−6Li at 543.3 G. By examining both the magnetic field dependence and, especially, the temperature dependence of L3 over a wide range of temperatures from a few μK to above 200 μK, we show that three-atom recombination through a narrow resonance follows a universal behavior determined by the long-range van der Waals potential and can be described by a set of rate equations in which three-body recombination proceeds via successive pairwise interactions. We expect the underlying physical picture to be applicable not only to narrow
s wave resonances, but also to resonances in nonzero partial waves, and not only at ultracold temperatures, but also at much higher temperatures
Scalable Kernel Methods via Doubly Stochastic Gradients
The general perception is that kernel methods are not scalable, and neural
nets are the methods of choice for nonlinear learning problems. Or have we
simply not tried hard enough for kernel methods? Here we propose an approach
that scales up kernel methods using a novel concept called "doubly stochastic
functional gradients". Our approach relies on the fact that many kernel methods
can be expressed as convex optimization problems, and we solve the problems by
making two unbiased stochastic approximations to the functional gradient, one
using random training points and another using random functions associated with
the kernel, and then descending using this noisy functional gradient. We show
that a function produced by this procedure after iterations converges to
the optimal function in the reproducing kernel Hilbert space in rate ,
and achieves a generalization performance of . This doubly
stochasticity also allows us to avoid keeping the support vectors and to
implement the algorithm in a small memory footprint, which is linear in number
of iterations and independent of data dimension. Our approach can readily scale
kernel methods up to the regimes which are dominated by neural nets. We show
that our method can achieve competitive performance to neural nets in datasets
such as 8 million handwritten digits from MNIST, 2.3 million energy materials
from MolecularSpace, and 1 million photos from ImageNet.Comment: 32 pages, 22 figure
Comparison of two efficient methods for calculating partition functions
In the long-time pursuit of the solution to calculate the partition function
(or free energy) of condensed matter, Monte-Carlo-based nested sampling should
be the state-of-the-art method, and very recently, we established a direct
integral approach that works at least four orders faster. In present work, the
above two methods were applied to solid argon at temperatures up to K, and
the derived internal energy and pressure were compared with the molecular
dynamics simulation as well as experimental measurements, showing that the
calculation precision of our approach is about 10 times higher than that of the
nested sampling method.Comment: 6 pages, 4 figure
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