Zobrazeno 1 - 10
of 120
pro vyhledávání: '"Hasani, Ramin"'
Autor:
Makiyeh, Fouad, Bastourous, Mark, Bairouk, Anass, Xiao, Wei, Maras, Mirjana, Wangb, Tsun-Hsuan, Blanchon, Marc, Hasani, Ramin, Chareyre, Patrick, Rus, Daniela
Autonomous vehicle navigation is a key challenge in artificial intelligence, requiring robust and accurate decision-making processes. This research introduces a new end-to-end method that exploits multimodal information from a single monocular camera
Externí odkaz:
http://arxiv.org/abs/2409.12716
Autor:
Nguyen, Huy-Dung, Bairouk, Anass, Maras, Mirjana, Xiao, Wei, Wang, Tsun-Hsuan, Chareyre, Patrick, Hasani, Ramin, Blanchon, Marc, Rus, Daniela
Autonomous driving holds great potential to transform road safety and traffic efficiency by minimizing human error and reducing congestion. A key challenge in realizing this potential is the accurate estimation of steering angles, which is essential
Externí odkaz:
http://arxiv.org/abs/2409.10095
Simulators are powerful tools for autonomous robot learning as they offer scalable data generation, flexible design, and optimization of trajectories. However, transferring behavior learned from simulation data into the real world proves to be diffic
Externí odkaz:
http://arxiv.org/abs/2406.15149
Autor:
Parnichkun, Rom N., Massaroli, Stefano, Moro, Alessandro, Smith, Jimmy T. H., Hasani, Ramin, Lechner, Mathias, An, Qi, Ré, Christopher, Asama, Hajime, Ermon, Stefano, Suzuki, Taiji, Yamashita, Atsushi, Poli, Michael
We approach designing a state-space model for deep learning applications through its dual representation, the transfer function, and uncover a highly efficient sequence parallel inference algorithm that is state-free: unlike other proposed algorithms
Externí odkaz:
http://arxiv.org/abs/2405.06147
Autor:
Bairouk, Anass, Maras, Mirjana, Herlin, Simon, Amini, Alexander, Blanchon, Marc, Hasani, Ramin, Chareyre, Patrick, Rus, Daniela
Autonomous driving presents a complex challenge, which is usually addressed with artificial intelligence models that are end-to-end or modular in nature. Within the landscape of modular approaches, a bio-inspired neural circuit policy model has emerg
Externí odkaz:
http://arxiv.org/abs/2404.01750
Developing autonomous agents that can interact with changing environments is an open challenge in machine learning. Robustness is particularly important in these settings as agents are often fit offline on expert demonstrations but deployed online wh
Externí odkaz:
http://arxiv.org/abs/2310.03915
Dataset Distillation is the task of synthesizing small datasets from large ones while still retaining comparable predictive accuracy to the original uncompressed dataset. Despite significant empirical progress in recent years, there is little underst
Externí odkaz:
http://arxiv.org/abs/2305.14113
Modern end-to-end learning systems can learn to explicitly infer control from perception. However, it is difficult to guarantee stability and robustness for these systems since they are often exposed to unstructured, high-dimensional, and complex obs
Externí odkaz:
http://arxiv.org/abs/2304.02733
Autor:
Buckman, Noam, Sreeram, Shiva, Lechner, Mathias, Ban, Yutong, Hasani, Ramin, Karaman, Sertac, Rus, Daniela
Intelligent intersection managers can improve safety by detecting dangerous drivers or failure modes in autonomous vehicles, warning oncoming vehicles as they approach an intersection. In this work, we present FailureNet, a recurrent neural network t
Externí odkaz:
http://arxiv.org/abs/2303.12224
We propose a new dataset distillation algorithm using reparameterization and convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art. To this end, we first formulate dataset distillation as a bi-level optimizati
Externí odkaz:
http://arxiv.org/abs/2302.06755