Zobrazeno 1 - 10
of 67 398
pro vyhledávání: '"Levent, A."'
Neural networks can fail when the data contains spurious correlations. To understand this phenomenon, researchers have proposed numerous spurious correlations benchmarks upon which to evaluate mitigation methods. However, we observe that these benchm
Externí odkaz:
http://arxiv.org/abs/2409.04188
Autor:
Chen, Hongrui, Joglekar, Aditya, Rubinstein, Zack, Schmerl, Bradley, Fedder, Gary, de Nijs, Jan, Garlan, David, Smith, Stephen, Kara, Levent Burak
Advances in CAD and CAM have enabled engineers and design teams to digitally design parts with unprecedented ease. Software solutions now come with a range of modules for optimizing designs for performance requirements, generating instructions for ma
Externí odkaz:
http://arxiv.org/abs/2409.03089
Autor:
Alpöge, Levent, Lawrence, Brian
We specify a Turing machine $T_{\text{Mordell}}$ with the following properties. 1. On input $(K,C/K)$, with $K/\mathbb{Q}$ a number field and $C/K$ a smooth projective hyperbolic curve, if $T_{\text{Mordell}}$ terminates, then it outputs $C(K)$. 2. T
Externí odkaz:
http://arxiv.org/abs/2408.11653
We provide a primal-dual framework for randomized approximation algorithms utilizing semidefinite programming (SDP) relaxations. Our framework pairs a continuum of APX-complete problems including MaxCut, Max2Sat, MaxDicut, and more generally, Max-Boo
Externí odkaz:
http://arxiv.org/abs/2406.18670
Designing for manufacturing poses significant challenges in part due to the computation bottleneck of Computer-Aided Manufacturing (CAM) simulations. Although deep learning as an alternative offers fast inference, its performance is dependently bound
Externí odkaz:
http://arxiv.org/abs/2406.12286
Pseudocode in a scholarly paper provides a concise way to express the algorithms implemented therein. Pseudocode can also be thought of as an intermediary representation that helps bridge the gap between programming languages and natural languages. H
Externí odkaz:
http://arxiv.org/abs/2406.04635
Autor:
Korosteleva, Maria, Kesdogan, Timur Levent, Kemper, Fabian, Wenninger, Stephan, Koller, Jasmin, Zhang, Yuhan, Botsch, Mario, Sorkine-Hornung, Olga
Recent research interest in the learning-based processing of garments, from virtual fitting to generation and reconstruction, stumbles on a scarcity of high-quality public data in the domain. We contribute to resolving this need by presenting the fir
Externí odkaz:
http://arxiv.org/abs/2405.17609
When a group acts on a set, it naturally partitions it into orbits, giving rise to orbit problems. These are natural algorithmic problems, as symmetries are central in numerous questions and structures in physics, mathematics, computer science, optim
Externí odkaz:
http://arxiv.org/abs/2405.15368
Reduced order modeling lowers the computational cost of solving PDEs by learning a low-order spatial representation from data and dynamically evolving these representations using manifold projections of the governing equations. While commonly used, l
Externí odkaz:
http://arxiv.org/abs/2405.14890
Data driven and learning based solutions for modeling dynamic garments have significantly advanced, especially in the context of digital humans. However, existing approaches often focus on modeling garments with respect to a fixed parametric human bo
Externí odkaz:
http://arxiv.org/abs/2407.06101