6,697 research outputs found
Inference of time-varying regression models
We consider parameter estimation, hypothesis testing and variable selection
for partially time-varying coefficient models. Our asymptotic theory has the
useful feature that it can allow dependent, nonstationary error and covariate
processes. With a two-stage method, the parametric component can be estimated
with a -convergence rate. A simulation-assisted hypothesis testing
procedure is proposed for testing significance and parameter constancy. We
further propose an information criterion that can consistently select the true
set of significant predictors. Our method is applied to autoregressive models
with time-varying coefficients. Simulation results and a real data application
are provided.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1010 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning
Computer-generated graphics (CGs) are images generated by computer software.
The~rapid development of computer graphics technologies has made it easier to
generate photorealistic computer graphics, and these graphics are quite
difficult to distinguish from natural images (NIs) with the naked eye. In this
paper, we propose a method based on sensor pattern noise (SPN) and deep
learning to distinguish CGs from NIs. Before being fed into our convolutional
neural network (CNN)-based model, these images---CGs and NIs---are clipped into
image patches. Furthermore, three high-pass filters (HPFs) are used to remove
low-frequency signals, which represent the image content. These filters are
also used to reveal the residual signal as well as SPN introduced by the
digital camera device. Different from the traditional methods of distinguishing
CGs from NIs, the proposed method utilizes a five-layer CNN to classify the
input image patches. Based on the classification results of the image patches,
we deploy a majority vote scheme to obtain the classification results for the
full-size images. The~experiments have demonstrated that (1) the proposed
method with three HPFs can achieve better results than that with only one HPF
or no HPF and that (2) the proposed method with three HPFs achieves 100\%
accuracy, although the NIs undergo a JPEG compression with a quality factor of
75.Comment: This paper has been published by Sensors. doi:10.3390/s18041296;
Sensors 2018, 18(4), 129
Accelerating and Improving AlphaZero Using Population Based Training
AlphaZero has been very successful in many games. Unfortunately, it still
consumes a huge amount of computing resources, the majority of which is spent
in self-play. Hyperparameter tuning exacerbates the training cost since each
hyperparameter configuration requires its own time to train one run, during
which it will generate its own self-play records. As a result, multiple runs
are usually needed for different hyperparameter configurations. This paper
proposes using population based training (PBT) to help tune hyperparameters
dynamically and improve strength during training time. Another significant
advantage is that this method requires a single run only, while incurring a
small additional time cost, since the time for generating self-play records
remains unchanged though the time for optimization is increased following the
AlphaZero training algorithm. In our experiments for 9x9 Go, the PBT method is
able to achieve a higher win rate for 9x9 Go than the baselines, each with its
own hyperparameter configuration and trained individually. For 19x19 Go, with
PBT, we are able to obtain improvements in playing strength. Specifically, the
PBT agent can obtain up to 74% win rate against ELF OpenGo, an open-source
state-of-the-art AlphaZero program using a neural network of a comparable
capacity. This is compared to a saturated non-PBT agent, which achieves a win
rate of 47% against ELF OpenGo under the same circumstances.Comment: accepted by AAAI2020 as oral presentation. In this version,
supplementary materials are adde
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