Author name cluster

Guixiang Ma

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5 papers

2 author rows

NeurIPS Conference 2024 Conference Paper

A Structure-Aware Framework for Learning Device Placements on Computation Graphs

Shukai Duan
Heng Ping
Nikos Kanakaris
Xiongye Xiao
Panagiotis Kyriakis
Nesreen K. Ahmed
Peiyu Zhang
Guixiang Ma

Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks. The device placement problem aims to identify optimal allocations of those nodes to a set of (potentially heterogeneous) devices. Existing approaches rely on two types of architectures known as grouper-placer and encoder-placer, respectively. In this work, we bridge the gap between encoder-placer and grouper-placer techniques and propose a novel framework for the task of device placement, relying on smaller computation graphs extracted from the OpenVINO toolkit. The framework consists of five steps, including graph coarsening, node representation learning and policy optimization. It facilitates end-to-end training and takes into account the DAG nature of the computation graphs. We also propose a model variant, inspired by graph parsing networks and complex network analysis, enabling graph representation learning and jointed, personalized graph partitioning, using an unspecified number of groups. To train the entire framework, we use reinforcement learning using the execution time of the placement as a reward. We demonstrate the flexibility and effectiveness of our approach through multiple experiments with three benchmark models, namely Inception-V3, ResNet, and BERT. The robustness of the proposed framework is also highlighted through an ablation study. The suggested placements improve the inference speed for the benchmark models by up to $58. 2\%$ over CPU execution and by up to $60. 24\%$ compared to other commonly used baselines.

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NeurIPS Conference 2024 Conference Paper

Training for Stable Explanation for Free

Chao Chen
Chenghua Guo
Rufeng Chen
Guixiang Ma
Ming Zeng
Xiangwen Liao
Xi Zhang
Sihong Xie

To foster trust in machine learning models, explanations must be faithful and stable for consistent insights. Existing relevant works rely on the $\ell_p$ distance for stability assessment, which diverges from human perception. Besides, existing adversarial training (AT) associated with intensive computations may lead to an arms race. To address these challenges, we introduce a novel metric to assess the stability of top-$k$ salient features. We introduce R2ET which trains for stable explanation by efficient and effective regularizer, and analyze R2ET by multi-objective optimization to prove numerical and statistical stability of explanations. Moreover, theoretical connections between R2ET and certified robustness justify R2ET's stability in all attacks. Extensive experiments across various data modalities and model architectures show that R2ET achieves superior stability against stealthy attacks, and generalizes effectively across different explanation methods. The code can be found at https: //github. com/ccha005/R2ET.

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AAAI Conference 2022 Conference Paper

DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media

Mengzhu Sun
Xi Zhang
Jiaqi Zheng
Guixiang Ma

Detecting rumors on social media has become particular important due to the rapid dissemination and adverse impacts on our lives. Though a set of rumor detection models have exploited the message propagation structural or temporal information, they seldom model them altogether to enjoy the best of both worlds. Moreover, the dynamics of knowledge information associated with the comments are not involved, either. To this end, we propose a novel Dual-Dynamic Graph Convolutional Networks, termed as DDGCN, which can model the dynamics of messages in propagation as well as the dynamics of the background knowledge from Knowledge graphs in one unified framework. Specifically, two Graph Convolutional Networks are adopted to capture the above two types of structure information at different time stages, which are then combined with a temporal fusing unit. This allows for learning the dynamic event representations in a more fine-grained manner, and incrementally aggregating them to capture the cascading effect for better rumor detection. Extensive experiments on two public real-world datasets demonstrate that our proposal yields significant improvements compared to strong baselines and can detect rumors at early stages.

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AAAI Conference 2022 Conference Paper

Explainable Survival Analysis with Convolution-Involved Vision Transformer

Yifan Shen
Li Liu
Zhihao Tang
Zongyi Chen
Guixiang Ma
Jiyan Dong
Xi Zhang
Lin Yang

Image-based survival prediction models can facilitate doctors in diagnosing and treating cancer patients. With the advance of digital pathology technologies, the big whole slide images (WSIs) provide increased resolution and more details for diagnosis. However, the gigabytesize or even terabyte-size WSIs would make most models computationally infeasible. To this end, instead of using the complete WSIs, most of the existing models only use a pre-selected subset of key patches or patch clusters as input, which might discard some important morphology information. In this work, we propose a novel survival analysis model to fully utilize the complete WSI information. We show that the use of a Vision Transformer (ViT) backbone, together with convolution operations involved in it, is an effective approach to improve the prediction performance. Additionally, we present a post-hoc explainable method to identify the most salient patches and distinct morphology features, making the model more faithful and the results easier to comprehend by human users. Evaluations on two large cancer datasets show that our proposed model is more effective and has better interpretability for survival prediction. We would make the code publicly available upon acceptance.

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ICML Conference 2017 Conference Paper

Kernelized Support Tensor Machines

Lifang He 0001
Chun-Ta Lu
Guixiang Ma
Shen Wang 0005
LinLin Shen
Philip S. Yu
Ann B. Ragin

In the context of supervised tensor learning, preserving the structural information and exploiting the discriminative nonlinear relationships of tensor data are crucial for improving the performance of learning tasks. Based on tensor factorization theory and kernel methods, we propose a novel Kernelized Support Tensor Machine (KSTM) which integrates kernelized tensor factorization with maximum-margin criterion. Specifically, the kernelized factorization technique is introduced to approximate the tensor data in kernel space such that the complex nonlinear relationships within tensor data can be explored. Further, dual structural preserving kernels are devised to learn the nonlinear boundary between tensor data. As a result of joint optimization, the kernels obtained in KSTM exhibit better generalization power to discriminative analysis. The experimental results on real-world neuroimaging datasets show the superiority of KSTM over the state-of-the-art techniques.

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