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pro vyhledávání: '"Seff, Ari"'
A major challenge in autonomous vehicle research is modeling agent behaviors, which has critical applications including constructing realistic and reliable simulations for off-board evaluation and forecasting traffic agents motion for onboard plannin
Externí odkaz:
http://arxiv.org/abs/2409.18343
Autor:
Seff, Ari, Cera, Brian, Chen, Dian, Ng, Mason, Zhou, Aurick, Nayakanti, Nigamaa, Refaat, Khaled S., Al-Rfou, Rami, Sapp, Benjamin
Reliable forecasting of the future behavior of road agents is a critical component to safe planning in autonomous vehicles. Here, we represent continuous trajectories as sequences of discrete motion tokens and cast multi-agent motion prediction as a
Externí odkaz:
http://arxiv.org/abs/2309.16534
Publikováno v:
ICLR 2022
Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards. The key characteristic of parametric CAD is that design intent is encoded not
Externí odkaz:
http://arxiv.org/abs/2109.14124
Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design. Distinguished by relational geometry, parametric CAD models begin as two-dimensional sketches consisting of geometric primitives (e.g., lin
Externí odkaz:
http://arxiv.org/abs/2007.08506
The success of generative modeling in continuous domains has led to a surge of interest in generating discrete data such as molecules, source code, and graphs. However, construction histories for these discrete objects are typically not unique and so
Externí odkaz:
http://arxiv.org/abs/1907.08268
Developments in deep generative models have allowed for tractable learning of high-dimensional data distributions. While the employed learning procedures typically assume that training data is drawn i.i.d. from the distribution of interest, it may be
Externí odkaz:
http://arxiv.org/abs/1705.08395
Autor:
Seff, Ari, Xiao, Jianxiong
Today's autonomous vehicles rely extensively on high-definition 3D maps to navigate the environment. While this approach works well when these maps are completely up-to-date, safe autonomous vehicles must be able to corroborate the map's information
Externí odkaz:
http://arxiv.org/abs/1611.08583
While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry. Large labeled training datasets, expensive and tedious to produce, are required to optimiz
Externí odkaz:
http://arxiv.org/abs/1506.03365
Autor:
Roth, Holger R., Lu, Le, Liu, Jiamin, Yao, Jianhua, Seff, Ari, Cherry, Kevin, Kim, Lauren, Summers, Ronald M.
Publikováno v:
IEEE Transactions on Medical Imaging, 28 September 2015, Volume:PP, Issue: 99
Automated computer-aided detection (CADe) in medical imaging has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities but at the cost of high false-positives (FP) per patient rates. We desig
Externí odkaz:
http://arxiv.org/abs/1505.03046
Despite tremendous progress in computer vision, there has not been an attempt for machine learning on very large-scale medical image databases. We present an interleaved text/image deep learning system to extract and mine the semantic interactions of
Externí odkaz:
http://arxiv.org/abs/1505.00670