Search Results for author:
Found 11 papers, 9 papers with code
A Novel Multi-Stage Approach for Hierarchical Intrusion Detection
An intrusion detection system (IDS), traditionally an example of an effective security monitoring system, is facing significant challenges due to the ongoing digitization of our modern society.
Ranked #1 on
Network Intrusion Detection
on CICIDS2017
MAGIC: Mining an Augmented Graph using INK, starting from a CSV
A large portion of structured data does not yet reap the benefits of the Semantic Web.
Ranked #5 on
Cell Entity Annotation
on BiodivTab
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs
As KGs are symbolic constructs, specialized techniques have to be applied in order to make them compatible with data mining techniques.
Overly Optimistic Prediction Results on Imbalanced Data: a Case Study of Flaws and Benefits when Applying Over-sampling
Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth.
GENDIS: GENetic DIscovery of Shapelets
It has been shown that classifiers are able to achieve state-of-the-art results on a plethora of datasets by taking as input distances from the input time series to different discriminative shapelets.
Ranked #3 on
Outlier Detection
on ECG5000
Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph
Deep-learning based techniques are increasingly being used for different machine learning tasks on knowledge graphs.
Ranked #3 on
Node Classification
on BGS
Positive blood culture detection in time series data using a BiLSTM network
The presence of bacteria or fungi in the bloodstream of patients is abnormal and can lead to life-threatening conditions.
GENESIM: genetic extraction of a single, interpretable model
The results show that GENESIM achieves a better predictive performance on most of these data sets than decision tree induction techniques and a predictive performance in the same order of magnitude as the ensemble techniques.
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks.
Ranked #1 on
Atari Games
on Atari 2600 Freeway
VIME: Variational Information Maximizing Exploration
While there are methods with optimality guarantees in the setting of discrete state and action spaces, these methods cannot be applied in high-dimensional deep RL scenarios.
Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines
To improve prediction accuracy, this paper proposes: (i) Joint inference and learning by integration of back-propagation and loss-augmented inference in SSVM subgradient descent; (ii) Extending SSVM factors to neural networks that form highly nonlinear functions of input features.
