Search Results for author:
Found 11 papers, 6 papers with code
Learning Dual-arm Object Rearrangement for Cartesian Robots
To achieve the goal, the core idea is to develop an effective object-to-arm task assignment strategy for minimizing the cumulative task execution time and maximizing the dual-arm cooperation efficiency.
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
Our one-class classifier is calibrated in two ways: (1) by adaptively penalizing uncertain predictions, which helps eliminate the impact of anomaly contamination while accentuating the predictions that the one-class model is confident in, and (2) by discriminating the normal samples from native anomaly examples that are generated to simulate genuine time series abnormal behaviors on the basis of original data.
Deep Isolation Forest for Anomaly Detection
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability.
Ranked #1 on
Anomaly Detection
on NB15-DoS
Network resilience in the aging brain
Degeneration and adaptation are two competing sides of the same coin called resilience in the progressive processes of brain aging or diseases.
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities
DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers.
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network
We obtain an optimal attention-guided embedding space with expanded high-level information and rich semantics, and thus outlying behaviors of the queried outlier can be better unfolded.
Few-Shot Website Fingerprinting Attack
To address this, we introduce a model-agnostic, efficient, and Harmonious Data Augmentation (HDA) method that can improve deep WF attacking methods significantly.
Data Augmentation
Cryptography and Security
68M25
K.4.1
MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data
Mixed-type data are pervasive in real life, but very limited outlier detection methods are available for these data.
Ranked #1 on
Outlier Detection
on Hepatitis
Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical Data
Non-IID categorical data are ubiquitous and distinct in real-world applications.
Linearly Supporting Feature Extraction For Automated Estimation Of Stellar Atmospheric Parameters
On real spectra, we extracted 23 features to estimate $T_{eff}$, 62 features for log$~g$, and 68 features for [Fe/H].
