584 research outputs found
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy
Large convolutional neural networks (CNN) can be difficult to train in the
differentially private (DP) regime, since the optimization algorithms require a
computationally expensive operation, known as the per-sample gradient clipping.
We propose an efficient and scalable implementation of this clipping on
convolutional layers, termed as the mixed ghost clipping, that significantly
eases the private training in terms of both time and space complexities,
without affecting the accuracy. The improvement in efficiency is rigorously
studied through the first complexity analysis for the mixed ghost clipping and
existing DP training algorithms.
Extensive experiments on vision classification tasks, with large ResNet, VGG,
and Vision Transformers, demonstrate that DP training with mixed ghost clipping
adds memory overhead and slowdown to the standard
non-private training. Specifically, when training VGG19 on CIFAR10, the mixed
ghost clipping is faster than state-of-the-art Opacus library with
larger maximum batch size. To emphasize the significance of
efficient DP training on convolutional layers, we achieve 96.7\% accuracy on
CIFAR10 and 83.0\% on CIFAR100 at using BEiT, while the previous
best results are 94.8\% and 67.4\%, respectively. We open-source a privacy
engine (\url{https://github.com/JialinMao/private_CNN}) that implements DP
training of CNN with a few lines of code.Comment: Accepted to NeurIPS 202
A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Control
This paper addresses the challenges associated with decentralized voltage
control in power grids due to an increase in distributed generations (DGs).
Traditional model-based voltage control methods struggle with the rapid energy
fluctuations and uncertainties of these DGs. While multi-agent reinforcement
learning (MARL) has shown potential for decentralized secondary control,
scalability issues arise when dealing with a large number of DGs. This problem
lies in the dominant centralized training and decentralized execution (CTDE)
framework, where the critics take global observations and actions. To overcome
these challenges, we propose a scalable network-aware (SNA) framework that
leverages network structure to truncate the input to the critic's Q-function,
thereby improving scalability and reducing communication costs during training.
Further, the SNA framework is theoretically grounded with provable
approximation guarantee, and it can seamlessly integrate with multiple
multi-agent actor-critic algorithms. The proposed SNA framework is successfully
demonstrated in a system with 114 DGs, providing a promising solution for
decentralized voltage control in increasingly complex power grid systems
Usersâ Emotional Attachments to Internet Celebrities: Based on the Perspective of Extended-self
Although previous researcher has focused on the use of social media between celebrities and fans from the use and gratification perspective, knowledge on why users stick with live streaming is limited. Therefore, the authors propose that sticking with live streaming reflects a strong connection between celebrities and users. We define emotional attachment as the strength of the cognitive and emotional bond connecting the celebrities with the self. Consequently, in this study, we adopt attachment theory to investigate usersâ tendency to stick with live streaming from the extended-self perspective. The findings of this study fully support the hypotheses specifying the relationships between constructs. Emotional attachment was influenced by gratifying the self, enriching the self and enabling the self, and which in turn are strong predictors of usersâ stickiness intention. The current research contributes to the further expansion of social media research and applied attachment theory into the live streaming context
Measurement and Simulation Study on Effective Drainage Radius of Borehole Along Coal Seam
A measurement study was conducted for the effective drainage radius of borehole along coal seam #9 of the Kaiyuan Coal Mine using the gas pressure method and gas flow method. The measurement results show that the effective drainage radius of borehole along coal seam #9 was 0.75 m, 27 days after drainage, and 1.5 m, 92 days after drainage. Experimental schemes were designed for the entire evolution of the stress in the coal mass around the borehole, and an experimental study on methane seepage in the coal mass around borehole was conducted. Fitting functions for the permeability of the coal sample and its vertical stress were obtained by fitting the experimental data. Based on the vertical stressâpermeability functional relationship of coal masses around borehole, a numerical calculation model for methane seepage from coal masses around borehole was established, and the influences of drainage time, initial gas pressure, borehole diameter, and drainage negative pressure on the effective drainage radius of borehole were investigated. The numerical simulation results show that with the increase in initial gas pressure and borehole diameter, the effective drainage radius of borehole increases continuously but its increase amplitude decreases constantly. With the increase in drainage negative pressure, the effective drainage radius of borehole increases linearly but the increase amplitude is relatively small. The layout parameters of borehole along coal seam #9 of the Kaiyuan Coal Mine were optimized based on the numerical calculation results, and the reasonable drainage time, reasonable borehole diameter, and reasonable drainage negative pressure are 180 days, 120 mm, and 15 kPa, respectively, for the borehole along coal seam #9
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