3,791 research outputs found
SoK: Certified Robustness for Deep Neural Networks
Great advances in deep neural networks (DNNs) have led to state-of-the-art
performance on a wide range of tasks. However, recent studies have shown that
DNNs are vulnerable to adversarial attacks, which have brought great concerns
when deploying these models to safety-critical applications such as autonomous
driving. Different defense approaches have been proposed against adversarial
attacks, including: a) empirical defenses, which can usually be adaptively
attacked again without providing robustness certification; and b) certifiably
robust approaches, which consist of robustness verification providing the lower
bound of robust accuracy against any attacks under certain conditions and
corresponding robust training approaches. In this paper, we systematize
certifiably robust approaches and related practical and theoretical
implications and findings. We also provide the first comprehensive benchmark on
existing robustness verification and training approaches on different datasets.
In particular, we 1) provide a taxonomy for the robustness verification and
training approaches, as well as summarize the methodologies for representative
algorithms, 2) reveal the characteristics, strengths, limitations, and
fundamental connections among these approaches, 3) discuss current research
progresses, theoretical barriers, main challenges, and future directions for
certifiably robust approaches for DNNs, and 4) provide an open-sourced unified
platform to evaluate 20+ representative certifiably robust approaches.Comment: To appear at 2023 IEEE Symposium on Security and Privacy (SP); 14
pages for the main text; benchmark & tool website:
http://sokcertifiedrobustness.github.io
Particle-resolved thermal lattice Boltzmann simulation using OpenACC on multi-GPUs
We utilize the Open Accelerator (OpenACC) approach for graphics processing
unit (GPU) accelerated particle-resolved thermal lattice Boltzmann (LB)
simulation. We adopt the momentum-exchange method to calculate fluid-particle
interactions to preserve the simplicity of the LB method. To address load
imbalance issues, we extend the indirect addressing method to collect
fluid-particle link information at each timestep and store indices of
fluid-particle link in a fixed index array. We simulate the sedimentation of
4,800 hot particles in cold fluids with a domain size of , and the
simulation achieves 1750 million lattice updates per second (MLUPS) on a single
GPU. Furthermore, we implement a hybrid OpenACC and message passing interface
(MPI) approach for multi-GPU accelerated simulation. This approach incorporates
four optimization strategies, including building domain lists, utilizing
request-answer communication, overlapping communications with computations, and
executing computation tasks concurrently. By reducing data communication
between GPUs, hiding communication latency through overlapping computation, and
increasing the utilization of GPU resources, we achieve improved performance,
reaching 10846 MLUPS using 8 GPUs. Our results demonstrate that the
OpenACC-based GPU acceleration is promising for particle-resolved thermal
lattice Boltzmann simulation.Comment: 45 pages, 18 figure
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
Prediction of popularity has profound impact for social media, since it
offers opportunities to reveal individual preference and public attention from
evolutionary social systems. Previous research, although achieves promising
results, neglects one distinctive characteristic of social data, i.e.,
sequentiality. For example, the popularity of online content is generated over
time with sequential post streams of social media. To investigate the
sequential prediction of popularity, we propose a novel prediction framework
called Deep Temporal Context Networks (DTCN) by incorporating both temporal
context and temporal attention into account. Our DTCN contains three main
components, from embedding, learning to predicting. With a joint embedding
network, we obtain a unified deep representation of multi-modal user-post data
in a common embedding space. Then, based on the embedded data sequence over
time, temporal context learning attempts to recurrently learn two adaptive
temporal contexts for sequential popularity. Finally, a novel temporal
attention is designed to predict new popularity (the popularity of a new
user-post pair) with temporal coherence across multiple time-scales.
Experiments on our released image dataset with about 600K Flickr photos
demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms,
with an average of 21.51% relative performance improvement in the popularity
prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1
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