Improved Dropout for Shallow and Deep Learning

Dropout has been witnessed with great success in training deep neural networks by independently zeroing out the outputs of neurons at random. It has also received a surge of interest for shallow learning, e.g., logistic regression. However, the independent sampling for dropout could be suboptimal for the sake of convergence. In this paper, we propose to use multinomial sampling for dropout, i.e., sampling features or neurons according to a multinomial distribution with different probabilities for different features/neurons. To exhibit the optimal dropout probabilities, we analyze the shallow learning with multinomial dropout and establish the risk bound for stochastic optimization. By minimizing a sampling dependent factor in the risk bound, we obtain a distribution-dependent dropout with sampling probabilities dependent on the second order statistics of the data distribution. To tackle the issue of evolving distribution of neurons in deep learning, we propose an efficient adaptive dropout (named \textbf{evolutional dropout}) that computes the sampling probabilities on-the-fly from a mini-batch of examples. Empirical studies on several benchmark datasets demonstrate that the proposed dropouts achieve not only much faster convergence and but also a smaller testing error than the standard dropout. For example, on the CIFAR-100 data, the evolutional dropout achieves relative improvements over 10\% on the prediction performance and over 50\% on the convergence speed compared to the standard dropout.Comment: In NIPS 201

Dark energy imprints on the kinematic Sunyaev-Zel'dovich signal

We investigate the imprint of dark energy on the kinetic Sunyaev-Zel'dovich (kSZ) angular power spectrum on scales of $\ell=1000$ to $10000$, and find that the kSZ signal is sensitive to the dark energy parameter. For example, varying the constant $w$ by 20\% around $w=-1$ results in a $\gtrsim10\%$ change on the kSZ spectrum; changing the dark energy dynamics parametrized by $w_a$ by $\pm0.5$, a 30\% change on the kSZ spectrum is expected. We discuss the observational aspects and develop a fitting formula for the kSZ power spectrum. Finally, we discuss how the precise modeling of the post-reionization signal would help the constraints on patchy reionization signal, which is crucial for measuring the duration of reionization.Comment: 12 pages, 9 figures, 2 table

Cosmic Mach Number: A Sensitive Probe for the Growth of Structure

In this Letter, we investigate the potential power of the Cosmic Mach Number (CMN), which is the ratio between the mean velocity and the velocity dispersion of galaxies as a function of cosmic scales, to constrain cosmologies. We first measure the CMN from 5 catalogues of galaxy peculiar velocity surveys at low redshift (0.002<z<0.03), and use them to contrast cosmological models. Overall, current data is consistent with the WMAP7 LCDM model. We find that the CMN is highly sensitive to the growth of structure on scales 0.01<k<0.1 h/Mpc in Fourier space. Therefore, modified gravity models, and models with massive neutrinos, in which the structure growth generally deviates from that in the LCDM model in a scale-dependent way, can be well differentiated from the LCDM model using future CMN data.Comment: 7 pages, matches the version accepted to JCA