Zobrazeno 1 - 10
of 96 157
pro vyhledávání: '"Ammar OF"'
Autor:
Liu, Puze, Günster, Jonas, Funk, Niklas, Gröger, Simon, Chen, Dong, Bou-Ammar, Haitham, Jankowski, Julius, Marić, Ante, Calinon, Sylvain, Orsula, Andrej, Olivares-Mendez, Miguel, Zhou, Hongyi, Lioutikov, Rudolf, Neumann, Gerhard, Zhalehmehrabi, Amarildo Likmeta Amirhossein, Bonenfant, Thomas, Restelli, Marcello, Tateo, Davide, Liu, Ziyuan, Peters, Jan
Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robo
Externí odkaz:
http://arxiv.org/abs/2411.05718
In real-world applications where confidence is key, like autonomous driving, the accurate detection and appropriate handling of classes differing from those used during training are crucial. Despite the proposal of various unknown object detection ap
Externí odkaz:
http://arxiv.org/abs/2411.05564
We consider a strategic decision-making problem where a logistics provider (LP) seeks to locate collection and delivery points (CDPs) with the objective to reduce total logistics costs. The customers maximize utility that depends on their perception
Externí odkaz:
http://arxiv.org/abs/2411.04200
Autor:
Grosnit, Antoine, Maraval, Alexandre, Doran, James, Paolo, Giuseppe, Thomas, Albert, Beevi, Refinath Shahul Hameed Nabeezath, Gonzalez, Jonas, Khandelwal, Khyati, Iacobacci, Ignacio, Benechehab, Abdelhakim, Cherkaoui, Hamza, El-Hili, Youssef Attia, Shao, Kun, Hao, Jianye, Yao, Jun, Kegl, Balazs, Bou-Ammar, Haitham, Wang, Jun
We introduce Agent K v1.0, an end-to-end autonomous data science agent designed to automate, optimise, and generalise across diverse data science tasks. Fully automated, Agent K v1.0 manages the entire data science life cycle by learning from experie
Externí odkaz:
http://arxiv.org/abs/2411.03562
While overparameterization is known to benefit generalization, its impact on Out-Of-Distribution (OOD) detection is less understood. This paper investigates the influence of model complexity in OOD detection. We propose an expected OOD risk metric to
Externí odkaz:
http://arxiv.org/abs/2411.02184
Autor:
Gowaikar, Shreeyash, Berard, Hugo, Mushkani, Rashid, Marchand, Emmanuel Beaudry, Ammar, Toumadher, Koseki, Shin
Advancements in AI heavily rely on large-scale datasets meticulously curated and annotated for training. However, concerns persist regarding the transparency and context of data collection methodologies, especially when sourced through crowdsourcing
Externí odkaz:
http://arxiv.org/abs/2411.00956
Autor:
Wang, Xinran, Le, Qi, Ahmed, Ammar, Diao, Enmao, Zhou, Yi, Baracaldo, Nathalie, Ding, Jie, Anwar, Ali
Ensuring that generative AI systems align with human values is essential but challenging, especially when considering multiple human values and their potential trade-offs. Since human values can be personalized and dynamically change over time, the d
Externí odkaz:
http://arxiv.org/abs/2410.19198
With the rapid advancement of autonomous driving technology, efficient and accurate object detection capabilities have become crucial factors in ensuring the safety and reliability of autonomous driving systems. However, in low-visibility environment
Externí odkaz:
http://arxiv.org/abs/2410.17734
Autor:
Barroso-Luque, Luis, Shuaibi, Muhammed, Fu, Xiang, Wood, Brandon M., Dzamba, Misko, Gao, Meng, Rizvi, Ammar, Zitnick, C. Lawrence, Ulissi, Zachary W.
The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing hardware. AI has the potential to accelerate materials discovery and d
Externí odkaz:
http://arxiv.org/abs/2410.12771
Autor:
Yan, Xue, Song, Yan, Feng, Xidong, Yang, Mengyue, Zhang, Haifeng, Ammar, Haitham Bou, Wang, Jun
In sequential decision-making (SDM) tasks, methods like reinforcement learning (RL) and heuristic search have made notable advances in specific cases. However, they often require extensive exploration and face challenges in generalizing across divers
Externí odkaz:
http://arxiv.org/abs/2410.07927