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- Harvard UniversityUS15 papers
- Robert Bosch (Slovenia)SI15 papers
- Carnegie Mellon UniversityUS14 papers
- Robert Bosch (Germany)DE9 papers
- École Polytechnique Fédérale de LausanneCH8 papers
- Lawrence Berkeley National LaboratoryUS8 papers
- University of MichiganUS8 papers
- Ludwig-Maximilians-Universität MünchenDE6 papers
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- Excellence Cluster UniverseDE5 papers
11 papers · 1 filter
Interactive Visual Pattern Search on Graph Data via Graph Representation Learning
Huan Song, Zeng Dai, Panpan Xu +1
Graphs are a ubiquitous data structure to model processes and relations in a wide range of domains. Examples include control-flow graphs in programs and semantic scene graphs in im…
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors
Zhengyi Wang, Zhongkai Hao, Ziqiao Wang +2
While deep neural networks have achieved great success in graph analysis, recent work has shown that they are vulnerable to adversarial attacks. Compared with adversarial attacks o…
Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation
Wenhao Ding, Baiming Chen, Bo Li +2
Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importan…
Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes
Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott +1
We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples for deep learning models solely based on information limited to output l…
Interpretable and Steerable Sequence Learning via Prototypes
Yao Ming, Panpan Xu, Huamin Qu +1
One of the major challenges in machine learning nowadays is to provide predictions with not only high accuracy but also user-friendly explanations. Although in recent years we have…
MATCHA: Speeding Up Decentralized SGD via Matching Decomposition Sampling
Jianyu Wang, Anit Kumar Sahu, Zhouyi Yang +2
This paper studies the problem of error-runtime trade-off, typically encountered in decentralized training based on stochastic gradient descent (SGD) using a given network. While a…