Zobrazeno 1 - 10
of 22 318
pro vyhledávání: '"Erman, A."'
Addressing the question of how to achieve optimal decision-making under risk and uncertainty is crucial for enhancing the capabilities of artificial agents that collaborate with or support humans. In this work, we address this question in the context
Externí odkaz:
http://arxiv.org/abs/2408.00682
Generating group dance motion from the music is a challenging task with several industrial applications. Although several methods have been proposed to tackle this problem, most of them prioritize optimizing the fidelity in dancing movement, constrai
Externí odkaz:
http://arxiv.org/abs/2407.18839
Autor:
Rahmani, Zahra, Shahini, Nahal, Gat, Nadav, Yun, Zebin, Jiang, Yuzhou, Farchy, Ofir, Harel, Yaniv, Chaudhary, Vipin, Sharif, Mahmood, Ayday, Erman
The data revolution holds significant promise for the health sector. Vast amounts of data collected from individuals will be transformed into knowledge, AI models, predictive systems, and best practices. One area of health that stands to benefit grea
Externí odkaz:
http://arxiv.org/abs/2407.09004
Autor:
Antwi, Richard Boadu, Takyi, Samuel, Karaer, Alican, Ozguven, Eren Erman, Kimollo, Michael, Moses, Ren, Dulebenets, Maxim A., Sando, Thobias
Identification of changes in pavement markings has become crucial for infrastructure monitoring, maintenance, development, traffic management, and safety. Automated extraction of roadway geometry is critical in helping with this, given the increasing
Externí odkaz:
http://arxiv.org/abs/2406.09731
Autor:
Antwi, Richard Boadu, Takyi, Samuel, Michael, Kimollo, Karaer, Alican, Ozguven, Eren Erman, Moses, Ren, Dulebenets, Maxim A., Sando, Thobias
Efficient and current roadway geometry data collection is critical to transportation agencies in road planning, maintenance, design, and rehabilitation. Data collection methods are divided into land-based and aerial-based. Land-based methods for exte
Externí odkaz:
http://arxiv.org/abs/2406.08822
Autor:
Ghasemian, Maryam, Ayday, Erman
This study investigates the optimal selection of parameters for collaborative clustering while ensuring data privacy. We focus on key clustering algorithms within a collaborative framework, where multiple data owners combine their data. A semi-truste
Externí odkaz:
http://arxiv.org/abs/2406.05545
Research on causal effects often relies on synthetic data due to the scarcity of real-world datasets with ground-truth effects. Since current data-generating tools do not always meet all requirements for state-of-the-art research, ad-hoc methods are
Externí odkaz:
http://arxiv.org/abs/2405.13092
As Artificial Intelligence (AI) is having more influence on our everyday lives, it becomes important that AI-based decisions are transparent and explainable. As a consequence, the field of eXplainable AI (or XAI) has become popular in recent years. O
Externí odkaz:
http://arxiv.org/abs/2404.11208
Recent advancements of large language models (LLMs) have resulted in indistinguishable text outputs comparable to human-generated text. Watermarking algorithms are potential tools that offer a way to differentiate between LLM- and human-generated tex
Externí odkaz:
http://arxiv.org/abs/2404.02138
Recent developments in face restoration have achieved remarkable results in producing high-quality and lifelike outputs. The stunning results however often fail to be faithful with respect to the identity of the person as the models lack necessary co
Externí odkaz:
http://arxiv.org/abs/2403.08436