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pro vyhledávání: '"Zubić, Nikola"'
A central challenge in sequence modeling is efficiently handling tasks with extended contexts. While recent state-space models (SSMs) have made significant progress in this area, they often lack input-dependent filtering or require substantial increa
Externí odkaz:
http://arxiv.org/abs/2410.03464
Despite their successes, deep learning models struggle with tasks requiring complex reasoning and function composition. We present a theoretical and empirical investigation into the limitations of Structured State Space Models (SSMs) and Transformers
Externí odkaz:
http://arxiv.org/abs/2405.16674
Publikováno v:
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 2024
Today, state-of-the-art deep neural networks that process event-camera data first convert a temporal window of events into dense, grid-like input representations. As such, they exhibit poor generalizability when deployed at higher inference frequenci
Externí odkaz:
http://arxiv.org/abs/2402.15584
Today, state-of-the-art deep neural networks that process events first convert them into dense, grid-like input representations before using an off-the-shelf network. However, selecting the appropriate representation for the task traditionally requir
Externí odkaz:
http://arxiv.org/abs/2304.13455
Autor:
Zubić, Nikola, Liò, Pietro
Differentiable rendering is a very successful technique that applies to a Single-View 3D Reconstruction. Current renderers use losses based on pixels between a rendered image of some 3D reconstructed object and ground-truth images from given matched
Externí odkaz:
http://arxiv.org/abs/2103.03390
Today, state-of-the-art deep neural networks that process events first convert them into dense, grid-like input representations before using an off-the-shelf network. However, selecting the appropriate representation for the task traditionally requir
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09478abcdbb1714314ca4bc1db81f0ad
http://arxiv.org/abs/2304.13455
http://arxiv.org/abs/2304.13455