We introduce modifications to state-of-the-art approaches to aggregating local video descriptors by using attention mechanisms and function approximations. Rather than using ensembles of existing architectures, we provide an insight on creating new architectures. We demonstrate our solutions in the "The 2nd YouTube-8M Video Understanding Challenge", by using frame-level video and audio descriptors. We obtain testing accuracy similar to the state of the art, while meeting budget constraints, and touch upon strategies to improve the state of the art. Model implementations are available in https://github.com/pomonam/LearnablePoolingMethods.
Variational Bayesian neural networks combine the flexibility of deep learning with Bayesian uncertainty estimation. However, inference procedures for flexible variational posteriors are computationally expensive. A recently proposed method, noisy natural gradient, is a surprisingly simple method to fit expressive posteriors by adding weight noise to regular natural gradient updates. Noisy K-FAC is an instance of noisy natural gradient that fits a matrix-variate Gaussian posterior with minor changes to ordinary K-FAC. Nevertheless, a matrix-variate Gaussian posterior does not capture an accurate diagonal variance. In this work, we extend on noisy K-FAC to obtain a more flexible posterior distribution called eigenvalue corrected matrix-variate Gaussian. The proposed method computes the full diagonal re-scaling factor in Kronecker-factored eigenbasis. Empirically, our approach consistently outperforms existing algorithms (e.g., noisy K-FAC) on regression and classification tasks.Nevertheless, we note that a matrix-variate Gaussian cannot capture an accurate diagonal variance. In this work, we build upon the large body of noisy K-FAC and Eigenvalue corrected Kronecker-factored Approximate Curvature (EK-FAC) [George et al., 2018] to improve the flexibility of the posterior distribution. We compute the diagonal variance, not in parameter coordinates, but in K-FAC eigenbasis. This leads to a more expressive posterior distribution. The relationship is described in Figure 1. Using this insight, we introduce a modified training method for variational Bayesian neural networks called noisy EK-FAC.
Single cell gap transflective liquid crystal display mode with vertically aligned negative liquid crystal was developed. Reflectance curve as a function of voltage matched exactly with transmittance curve. Threshold voltage in reflectance curve was around 2.0V, as in transmittance curve, and the voltage for maximum reflectance was around 4.5V∼5.0V, which gave also the maximum transmittance. Reflective region was divided into two parts. First reflective part was driven by the voltage supplied by switching transistor directly, while the second reflective part was driven by the voltage lower than that of first part using the voltage‐dividing capacitor connected to switching transistor in series. Normal 8 mask‐count process was applied to fabrication of real panels. No special optical film or extra driving circuit was required.
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