Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Gomaa, Amr"'
Autor:
Gomaa, Amr, Mahdy, Bilal
Integration of human feedback plays a key role in improving the learning capabilities of intelligent systems. This comparative study delves into the performance, robustness, and limitations of imitation learning compared to traditional reinforcement
Externí odkaz:
http://arxiv.org/abs/2410.21403
The increasing integration of machine learning across various domains has underscored the necessity for accessible systems that non-experts can utilize effectively. To address this need, the field of automated machine learning (AutoML) has developed
Externí odkaz:
http://arxiv.org/abs/2410.17469
Robust frame-wise embeddings are essential to perform video analysis and understanding tasks. We present a self-supervised method for representation learning based on aligning temporal video sequences. Our framework uses a transformer-based encoder t
Externí odkaz:
http://arxiv.org/abs/2409.04607
The rapid advancement of the automotive industry towards automated and semi-automated vehicles has rendered traditional methods of vehicle interaction, such as touch-based and voice command systems, inadequate for a widening range of non-driving rela
Externí odkaz:
http://arxiv.org/abs/2401.16123
Robot-assisted surgical systems have demonstrated significant potential in enhancing surgical precision and minimizing human errors. However, existing systems cannot accommodate individual surgeons' unique preferences and requirements. Additionally,
Externí odkaz:
http://arxiv.org/abs/2311.17693
Despite significant advances in gesture recognition technology, recognizing gestures in a driving environment remains challenging due to limited and costly data and its dynamic, ever-changing nature. In this work, we propose a model-adaptation approa
Externí odkaz:
http://arxiv.org/abs/2310.01659
There is an growing interest in using Large Language Models (LLMs) in multi-agent systems to tackle interactive real-world tasks that require effective collaboration and assessing complex situations. Yet, we still have a limited understanding of LLMs
Externí odkaz:
http://arxiv.org/abs/2309.17234
Autor:
Gomaa, Amr, Feld, Michael
Recent advances in machine learning, particularly deep learning, have enabled autonomous systems to perceive and comprehend objects and their environments in a perceptual subsymbolic manner. These systems can now perform object detection, sensor data
Externí odkaz:
http://arxiv.org/abs/2309.05787
Creating a diverse and comprehensive dataset of hand gestures for dynamic human-machine interfaces in the automotive domain can be challenging and time-consuming. To overcome this challenge, we propose using synthetic gesture datasets generated by vi
Externí odkaz:
http://arxiv.org/abs/2309.04421
Recent advances in machine learning models allowed robots to identify objects on a perceptual nonsymbolic level (e.g., through sensor fusion and natural language understanding). However, these primarily black-box learning models still lack interpreta
Externí odkaz:
http://arxiv.org/abs/2307.03853