Zobrazeno 1 - 10
of 20 342
pro vyhledávání: '"Caglar AT"'
In recent years, knowledge graph embedding models have been successfully applied in the transductive setting to tackle various challenging tasks including link prediction, and query answering. Yet, the transductive setting does not allow for reasonin
Externí odkaz:
http://arxiv.org/abs/2410.06742
Autor:
Fortman, Margaret A., Harrison, David C., Rodriguez, Ramiro H., Krebs, Zachary J., Han, Sangjun, Jang, Min Seok, McDermott, Robert, Girit, Caglar O., Brar, Victor W.
Josephson junction spectroscopy is a powerful local microwave spectroscopy technique that has promising potential as a diagnostic tool to probe the microscopic origins of noise in superconducting qubits. We present advancements toward realizing Josep
Externí odkaz:
http://arxiv.org/abs/2410.03009
Autor:
Tunc, Caglar
With the massive advancements in processing power, Digital Twins (DTs) have become powerful tools to monitor and analyze physical entities. However, due to the potentially very high number of Physical Systems (PSs) to be tracked and emulated, for ins
Externí odkaz:
http://arxiv.org/abs/2410.02487
We introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing significantly from standard knowledge graph embedding techniques. Ini
Externí odkaz:
http://arxiv.org/abs/2409.14857
Precipitation nowcasting is crucial for mitigating the impacts of severe weather events and supporting daily activities. Conventional models predominantly relying on radar data have limited performance in predicting cases with complex temporal featur
Externí odkaz:
http://arxiv.org/abs/2409.10367
Deep learning regularization techniques, such as dropout, layer normalization, or weight decay, are widely adopted in the construction of modern artificial neural networks, often resulting in more robust training processes and improved generalization
Externí odkaz:
http://arxiv.org/abs/2409.07606
The emergence of beyond 5G (B5G) and 6G networks underscores the critical role of advanced computer-aided tools, such as network digital twins (DTs), in fostering autonomous networks and ubiquitous intelligence. Existing solutions in the DT domain pr
Externí odkaz:
http://arxiv.org/abs/2409.01136
Autor:
Terekhov, Mikhail, Gulcehre, Caglar
Multi-objective reinforcement learning (MORL) is essential for addressing the intricacies of real-world RL problems, which often require trade-offs between multiple utility functions. However, MORL is challenging due to unstable learning dynamics wit
Externí odkaz:
http://arxiv.org/abs/2407.16807
Machine translation is indispensable in healthcare for enabling the global dissemination of medical knowledge across languages. However, complex medical terminology poses unique challenges to achieving adequate translation quality and accuracy. This
Externí odkaz:
http://arxiv.org/abs/2407.12126
State-of-the-art LLMs often rely on scale with high computational costs, which has sparked a research agenda to reduce parameter counts and costs without significantly impacting performance. Our study focuses on Transformer-based LLMs, specifically a
Externí odkaz:
http://arxiv.org/abs/2407.09835