Zobrazeno 1 - 10
of 2 777
pro vyhledávání: '"Akoglu, A."'
Given the GPS coordinates of a large collection of human agents over time, how can we model their mobility behavior toward effective anomaly detection (e.g. for bad-actor or malicious behavior detection) without any labeled data? Human mobility and t
Externí odkaz:
http://arxiv.org/abs/2410.01281
Outlier detection (OD) has a vast literature as it finds numerous applications in environmental monitoring, cybersecurity, finance, and medicine to name a few. Being an inherently unsupervised task, model selection is a key bottleneck for OD (both al
Externí odkaz:
http://arxiv.org/abs/2409.05672
The astonishing successes of ML have raised growing concern for the fairness of modern methods when deployed in real world settings. However, studies on fairness have mostly focused on supervised ML, while unsupervised outlier detection (OD), with nu
Externí odkaz:
http://arxiv.org/abs/2408.13667
Autor:
Hassan, Sahil, Inouye, Michael, Gonzalez, Miguel C., Aliyev, Ilkin, Mack, Joshua, Hafiz, Maisha, Akoglu, Ali
Open-source simulation tools play a crucial role for neuromorphic application engineers and hardware architects to investigate performance bottlenecks and explore design optimizations before committing to silicon. Reconfigurable Architecture for Neur
Externí odkaz:
http://arxiv.org/abs/2404.16208
Autor:
Deforce, Boje, Lee, Meng-Chieh, Baesens, Bart, Asensio, Estefanía Serral, Yoo, Jaemin, Akoglu, Leman
Time series anomaly detection (TSAD) finds many applications such as monitoring environmental sensors, industry KPIs, patient biomarkers, etc. A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types
Externí odkaz:
http://arxiv.org/abs/2404.02865
A major threat to the peer-review systems of computer science conferences is the existence of "collusion rings" between reviewers. In such collusion rings, reviewers who have also submitted their own papers to the conference work together to manipula
Externí odkaz:
http://arxiv.org/abs/2402.07860
Graph kernels used to be the dominant approach to feature engineering for structured data, which are superseded by modern GNNs as the former lacks learnability. Recently, a suite of Kernel Convolution Networks (KCNs) successfully revitalized graph ke
Externí odkaz:
http://arxiv.org/abs/2402.06087
Discrete diffusion models have seen a surge of attention with applications on naturally discrete data such as language and graphs. Although discrete-time discrete diffusion has been established for a while, only recently Campbell et al. (2022) introd
Externí odkaz:
http://arxiv.org/abs/2402.03701
Publikováno v:
NeurIPS 2024
Graph generation has been dominated by autoregressive models due to their simplicity and effectiveness, despite their sensitivity to ordering. Yet diffusion models have garnered increasing attention, as they offer comparable performance while being p
Externí odkaz:
http://arxiv.org/abs/2402.03687
ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach
Given a complex graph database of node- and edge-attributed multi-graphs as well as associated metadata for each graph, how can we spot the anomalous instances? Many real-world problems can be cast as graph inference tasks where the graph representat
Externí odkaz:
http://arxiv.org/abs/2311.07355