Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Defferrard, Michaël"'
A core objective of physical design is to minimize wirelength (WL) when placing chip components on a canvas. Computing the minimal WL of a placement requires finding rectilinear Steiner minimum trees (RSMTs), an NP-hard problem. We propose NeuroStein
Externí odkaz:
http://arxiv.org/abs/2407.03792
Autor:
Luo, Zhishang, Hy, Truong Son, Tabaghi, Puoya, Koh, Donghyeon, Defferrard, Michael, Rezaei, Elahe, Carey, Ryan, Davis, Rhett, Jain, Rajeev, Wang, Yusu
The run-time for optimization tools used in chip design has grown with the complexity of designs to the point where it can take several days to go through one design cycle which has become a bottleneck. Designers want fast tools that can quickly give
Externí odkaz:
http://arxiv.org/abs/2404.00477
Autor:
Butt, Natasha, Manczak, Blazej, Wiggers, Auke, Rainone, Corrado, Zhang, David W., Defferrard, Michaël, Cohen, Taco
Large language models are increasingly solving tasks that are commonly believed to require human-level reasoning ability. However, these models still perform very poorly on benchmarks of general intelligence such as the Abstraction and Reasoning Corp
Externí odkaz:
http://arxiv.org/abs/2402.04858
We introduce ChebLieNet, a group-equivariant method on (anisotropic) manifolds. Surfing on the success of graph- and group-based neural networks, we take advantage of the recent developments in the geometric deep learning field to derive a new approa
Externí odkaz:
http://arxiv.org/abs/2111.12139
A major challenge in single-particle cryo-electron microscopy (cryo-EM) is that the orientations adopted by the 3D particles prior to imaging are unknown; yet, this knowledge is essential for high-resolution reconstruction. We present a method to rec
Externí odkaz:
http://arxiv.org/abs/2104.06237
Designing a convolution for a spherical neural network requires a delicate tradeoff between efficiency and rotation equivariance. DeepSphere, a method based on a graph representation of the sampled sphere, strikes a controllable balance between these
Externí odkaz:
http://arxiv.org/abs/2012.15000
We present simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called simplicial complexes. These are natural multi-dimensional extensions of graphs that encode not only pair
Externí odkaz:
http://arxiv.org/abs/2010.03633
Spherical data is found in many applications. By modeling the discretized sphere as a graph, we can accommodate non-uniformly distributed, partial, and changing samplings. Moreover, graph convolutions are computationally more efficient than spherical
Externí odkaz:
http://arxiv.org/abs/1904.05146
Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning toolbox and have led to many breakthroughs in Artificial Intelligence. These networks have mostly been developed for regular Euclidean domains such as those supporting images
Externí odkaz:
http://arxiv.org/abs/1810.12186
We here summarize our experience running a challenge with open data for musical genre recognition. Those notes motivate the task and the challenge design, show some statistics about the submissions, and present the results.
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Externí odkaz:
http://arxiv.org/abs/1803.05337