Zobrazeno 1 - 10
of 3 392
pro vyhledávání: '"Weiss, Michael A."'
We construct a Poincar\'e complex whose periodic total surgery obstruction vanishes but whose Spivak normal fibration does not admit a reduction to a stable euclidean bundle. This contradicts the conjunction of two claims in the literature: Namely, o
Externí odkaz:
http://arxiv.org/abs/2406.14677
Autor:
Kumar, Amarjeet, Jiang, Hongxu, Imran, Muhammad, Valdes, Cyndi, Leon, Gabriela, Kang, Dahyun, Nataraj, Parvathi, Zhou, Yuyin, Weiss, Michael D., Shao, Wei
Deep learning has become the de facto method for medical image segmentation, with 3D segmentation models excelling in capturing complex 3D structures and 2D models offering high computational efficiency. However, segmenting 2.5D images, which have hi
Externí odkaz:
http://arxiv.org/abs/2405.00130
We show that in codimension at least 3, spaces of locally flat topological embeddings of manifolds are correctly modelled by derived spaces of maps between their configuration categories (under mild smoothability conditions, and perhaps with one nota
Externí odkaz:
http://arxiv.org/abs/2401.00799
The configuration category of a manifold is a topological category which we view as a Segal space, via the nerve construction. Our main result is that the unordered configuration category, suitably truncated, admits a finite presentation as a complet
Externí odkaz:
http://arxiv.org/abs/2312.17632
We investigate the relationship between the configuration category of a manifold and the configuration category of a covering space of that manifold.
Comment: 18 pages
Comment: 18 pages
Externí odkaz:
http://arxiv.org/abs/2312.17631
In two parts, we present a bigness criterion for the cotangent bundle of resolutions of orbifold surfaces of general type. As a corollary, we obtain the \textit{canonical model singularities} (CMS) criterion that can be applied to determine when a bi
Externí odkaz:
http://arxiv.org/abs/2312.03190
Autor:
Weiss, Michael, Tonella, Paolo
Recent decades have seen the rise of large-scale Deep Neural Networks (DNNs) to achieve human-competitive performance in a variety of artificial intelligence tasks. Often consisting of hundreds of millions, if not hundreds of billion parameters, thes
Externí odkaz:
http://arxiv.org/abs/2304.02654
Autor:
Weiss, Michael, Tonella, Paolo
Deep Neural Networks (DNN) are increasingly used as components of larger software systems that need to process complex data, such as images, written texts, audio/video signals. DNN predictions cannot be assumed to be always correct for several reason
Externí odkaz:
http://arxiv.org/abs/2212.07118
Autor:
Weiss, Michael
On complex problems, state of the art prediction accuracy of Deep Neural Networks (DNN) can be achieved using very large-scale models, consisting of billions of parameters. Such models can only be run on dedicated servers, typically provided by a 3rd
Externí odkaz:
http://arxiv.org/abs/2208.11552
Deep Neural Networks (DNNs) are becoming a crucial component of modern software systems, but they are prone to fail under conditions that are different from the ones observed during training (out-of-distribution inputs) or on inputs that are truly am
Externí odkaz:
http://arxiv.org/abs/2207.10495