Search Results for author:
Found 27 papers, 10 papers with code
SmartKex: Machine Learning Assisted SSH Keys Extraction From The Heap Dump
A commonly used method in digital forensics is to extract data from the main memory of a digital device.
The Importance of Future Information in Credit Card Fraud Detection
We believe that future works on this new paradigm will have a significant impact on the detection of compromised cards.
deepstruct -- linking deep learning and graph theory
deepstruct connects deep learning models and graph theory such that different graph structures can be imposed on neural networks or graph structures can be extracted from trained neural network models.
Experiments on Properties of Hidden Structures of Sparse Neural Networks
Sparsity in the structure of Neural Networks can lead to less energy consumption, less memory usage, faster computation times on convenient hardware, and automated machine learning.
Conditional Generative Adversarial Networks for Speed Control in Trajectory Simulation
During prediction, CSG forecasts future speed from latent space and conditions its generation based on it.
NLP\_Passau at SemEval-2020 Task 12: Multilingual Neural Network for Offensive Language Detection in English, Danish and Turkish
This paper describes a neural network (NN) model that was used for participating in the OffensEval, Task 12 of the SemEval 2020 workshop.
nlpUP at SemEval-2020 Task 12 : A Blazing Fast System for Offensive Language Detection
In this paper, we introduce our submission for the SemEval Task 12, sub-tasks A and B for offensive language identification and categorization in English tweets.
GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models
We introduce an approach to multilingual Offensive Language Detection based on the mBERT transformer model.
Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots
For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitive to the presence of stellar spots.
HateBERT: Retraining BERT for Abusive Language Detection in English
In this paper, we introduce HateBERT, a re-trained BERT model for abusive language detection in English.
Ranked #1 on
Hate Speech Detection
on AbusEval
ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level
We evaluate ADSAGE on authentication, email traffic and web browsing logs from the CERT insider threat datasets, as well as on real-world authentication events.
DeepGG: a Deep Graph Generator
Learning distributions of graphs can be used for automatic drug discovery, molecular design, complex network analysis, and much more.
Ranked #1 on
Graph Embedding
on Barabasi-Albert
Language Proficiency Scoring
The Common European Framework of Reference (CEFR) provides generic guidelines for the evaluation of language proficiency.
I Feel Offended, Don't Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language
However, there is a lack of data sets that take into account the degree of explicitness.
Investigating Extensions to Random Walk Based Graph Embedding
Graph embedding has recently gained momentum in the research community, in particular after the introduction of random walk and neural network based approaches.
Predicting event attendance exploring social influence
In this paper, we propose to model the social influence of friends on event attendance.
Global and Local Feature Learning for Ego-Network Analysis
This social network can be efficiently analyzed after learning representations of the ego and its alters in a low-dimensional, real vector space.
Shortest path distance approximation using deep learning techniques
Computing shortest path distances between nodes lies at the heart of many graph algorithms and applications.
Competitive Influence Maximization: Integrating Budget Allocation and Seed Selection
In this work, we integrate these two lines of research and propose a new scenario where competition happens in two phases.
Social and Information Networks Computer Science and Game Theory
Parallel Total Variation Distance Estimation with Neural Networks for Merging Over-Clusterings
We consider the initial situation where a dataset has been over-partitioned into $k$ clusters and seek a domain independent way to merge those initial clusters.
Policy Learning for Malaria Control
In this report, we introduce the progress to learn the policy for Malaria Control as a Reinforcement Learning problem in the KDD Cup Challenge 2019 and propose diverse solutions to deal with the limited observations problem.
Structural Analysis of Sparse Neural Networks
Sparse Neural Networks regained attention due to their potential for mathematical and computational advantages.
Ranked #1 on
Neural Architecture Search
on MNIST
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs
Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection.
Dataset shift quantification for credit card fraud detection
This phenomenon is named dataset shift or concept drift in the domain of fraud detection.
nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection
In addition, we evaluate our approach on a different dataset and show that our model is capable of detecting online aggressiveness in both English and German tweets.
Multiple perspectives HMM-based feature engineering for credit card fraud detection
In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud?
Analysing Neural Network Topologies: a Game Theoretic Approach
In previous work, heuristics based on using the weight distribution of a neuron as contribution measure have shown some success, but do not provide a proper theoretical understanding.
Ranked #1 on
Network Pruning
on MNIST
