All Outputs (4)

LLM-guided instance-level image manipulation with diffusion u-net cross-attention maps (2024)
Presentation / Conference Contribution

The advancement of text-to-image synthesis has introduced powerful generative models capable of creating realistic images from textual prompts. However, precise control over image attributes remains challenging, especially at the instance level. Whil... Read More about LLM-guided instance-level image manipulation with diffusion u-net cross-attention maps.

A novel deep reinforcement learning-based approach for task-offloading in vehicular networks (2022)
Presentation / Conference Contribution

Next-generation vehicular networks will impose unprecedented computation demand due to the wide adoption of compute-intensive services with stringent latency requirements. Computational capacity of vehicular networks can be enhanced by integration of... Read More about A novel deep reinforcement learning-based approach for task-offloading in vehicular networks.

On the fairness of generative adversarial networks (GANs) (2022)
Presentation / Conference Contribution

Generative adversarial networks (GANs) are one of the greatest advances in AI in recent years. With their ability to directly learn the probability distribution of data and then sample synthetic realistic data. Many applications have emerged, using G... Read More about On the fairness of generative adversarial networks (GANs).

API security in large enterprises: Leveraging machine learning for anomaly detection (2021)
Presentation / Conference Contribution

Large enterprises offer thousands of micro-services applications to support their daily business activities by using Application Programming Interfaces (APIs). These applications generate huge amounts of traffic via millions of API calls every day, w... Read More about API security in large enterprises: Leveraging machine learning for anomaly detection.