Search Results for author:
Found 26 papers, 5 papers with code
Impact of Training Instance Selection on Domain-Specific Entity Extraction using BERT
(2) which examples to annotate?
Causal Investigation of Public Opinion during the COVID-19 Pandemic via Social Media Text
Understanding the needs and fears of citizens, especially during a pandemic such as COVID-19, is essential for any government or legislative entity.
Recommendation Fairness in Social Networks Over Time
We further study how interventions on network properties influence fairness by examining counterfactual scenarios with alternative evolution outcomes and differing network properties.
Improving OCR Quality in 19th Century Historical Documents Using a Combined Machine Learning Based Approach
The model was then fine-tuned with a smaller set of manually annotated historical source data.
Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey
Differential Privacy has become a widely popular method for data protection in machine learning, especially since it allows formulating strict mathematical privacy guarantees.
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory
Rate-distortion theory-based outlier detection builds upon the rationale that a good data compression will encode outliers with unique symbols.
Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks
We hope our findings raise awareness about this issue in our community and lay a foundation for the future development of GNN models that are more robust to such attacks.
How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing
Deep learning (DL) models for natural language processing (NLP) tasks often handle private data, demanding protection against breaches and disclosures.
Privacy in Open Search: A Review of Challenges and Solutions
Privacy is of worldwide concern regarding activities and processes that include sensitive data.
State-Space Constraints Improve the Generalization of the Differentiable Neural Computer in some Algorithmic Tasks
We show that both approaches can improve the generalization capability of a particular type of MANN, the differentiable neural computer (DNC), and compare our approaches to a stateful and a stateless controller on a set of algorithmic tasks.
Structack: Structure-based Adversarial Attacks on Graph Neural Networks
In this work, we study adversarial attacks that are uninformed, where an attacker only has access to the graph structure, but no information about node attributes.
Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data
This paper explores the use of reinforcement learning (RL) models for autonomous racing.
Towards a General Framework to Embed Advanced Machine Learning in Process Control Systems
Since high data volume and complex data formats delivered in modern high-end production environments go beyond the scope of classical process control systems, more advanced tools involving machine learning are required to reliably recognize failure patterns.
Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact
These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years.
On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks
In this work, we systematically study the impact of community structure on the performance of GNNs in semi-supervised node classification on graphs.
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.
A Formally Robust Time Series Distance Metric
Distance-based classification is among the most competitive classification methods for time series data.
Robust Parameter-Free Season Length Detection in Time Series
Many of the methods for identifying periodic patterns require time series' season length as input parameter.
Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets
The presented work contributes to the tripartite recommendation problem in general and to the under-researched portfolio of evaluating recommender systems for data markets in particular.
Know-Center at SemEval-2019 Task 5: Multilingual Hate Speech Detection on Twitter using CNNs
This paper presents the Know-Center system submitted for task 5 of the SemEval-2019 workshop.
Know-Center at SemEval-2017 Task 10: Sequence Classification with the CODE Annotator
We competed in Subtask 1 and 2 which consist respectively in identifying all the key phrases in scientific publications and label them with one of the three categories: Task, Process, and Material.
