148 citations
- Saha Institute of Nuclear PhysicsIN14 papers
- University of RichmondUS10 papers
- Variable Energy Cyclotron CentreIN10 papers
- University of CincinnatiUS5 papers
- University of VirginiaUS4 papers
- Virginia TechUS4 papers
- CEA Paris-SaclayFR3 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR3 papers
- Florida State UniversityUS3 papers
- University of South CarolinaUS3 papers
- A. Alikhanyan National LaboratoryAM2 papers
5 papers · 1 filter
Quantum Semi-Supervised Kernel Learning
Seyran Saeedi, Aliakbar Panahi, Tom Arodz
Quantum computing leverages quantum effects to build algorithms that are faster then their classical variants. In machine learning, for a given model architecture, the speed of tra…
On the combined effect of class imbalance and concept complexity in deep learning
Kushankur Ghosh, Colin Bellinger, Roberto Corizzo +2
Structural concept complexity, class overlap, and data scarcity are some of the most important factors influencing the performance of classifiers under class imbalance conditions.…
Class-Incremental Experience Replay for Continual Learning under Concept Drift
Łukasz Korycki, Bartosz Krawczyk
Modern machine learning systems need to be able to cope with constantly arriving and changing data. Two main areas of research dealing with such scenarios are continual learning an…
Concept Drift Detection from Multi-Class Imbalanced Data Streams
Łukasz Korycki, Bartosz Krawczyk
Continual learning from data streams is among the most important topics in contemporary machine learning. One of the biggest challenges in this domain lies in creating algorithms t…
word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement
Aliakbar Panahi, Seyran Saeedi, Tom Arodz
Deep learning natural language processing models often use vector word embeddings, such as word2vec or GloVe, to represent words. A discrete sequence of words can be much more easi…