Zobrazeno 1 - 10
of 4 749
pro vyhledávání: '"Lucey P"'
This paper challenges the conventional belief that softmax attention in transformers is effective primarily because it generates a probability distribution for attention allocation. Instead, we theoretically show that its success lies in its ability
Externí odkaz:
http://arxiv.org/abs/2410.18613
Autor:
Lucey, Madeline, Sanderson, Robyn, Horta, Danny, Kundu, Aritra, Hopkins, Philip F., Arora, Arpit, Singh, Jasjeev, Panithanpaisal, Nondh
$\Lambda$CDM cosmology predicts the hierarchical formation of galaxies which build up mass by merger events and accreting smaller systems. The stellar halo of the Milky Way has proven to be useful a tool for tracing this accretion history. However, m
Externí odkaz:
http://arxiv.org/abs/2410.03627
In this article, we introduce a novel normalization technique for neural network weight matrices, which we term weight conditioning. This approach aims to narrow the gap between the smallest and largest singular values of the weight matrices, resulti
Externí odkaz:
http://arxiv.org/abs/2409.03424
Autor:
Said, Khaled, Howlett, Cullan, Davis, Tamara, Lucey, John, Saulder, Christoph, Douglass, Kelly, Kim, Alex G., Kremin, Anthony, Ross, Caitlin, Aldering, Greg, Aguilar, Jessica Nicole, Ahlen, Steven, BenZvi, Segev, Bianchi, Davide, Brooks, David, Claybaugh, Todd, Dawson, Kyle, de la Macorra, Axel, Dey, Biprateep, Doel, Peter, Fanning, Kevin, Ferraro, Simone, Font-Ribera, Andreu, Forero-Romero, Jaime E., Gaztañaga, Enrique, Gontcho, Satya Gontcho A, Guy, Julien, Honscheid, Klaus, Kehoe, Robert, Kisner, Theodore, Lambert, Andrew, Landriau, Martin, Guillou, Laurent Le, Manera, Marc, Meisner, Aaron, Miquel, Ramon, Moustakas, John, Muñoz-Gutiérrez, Andrea, Myers, Adam, Nie, Jundan, Palanque-Delabrouille, Nathalie, Percival, Will, Prada, Francisco, Rossi, Graziano, Sanchez, Eusebio, Schlegel, David, Schubnell, Michael, Silber, Joseph Harry, Sprayberry, David, Tarlé, Gregory, Magana, Mariana Vargas, Weaver, Benjamin Alan, Wechsler, Risa, Zhou, Zhimin, Zou, Hu
The Dark Energy Spectroscopic Instrument (DESI) Peculiar Velocity Survey aims to measure the peculiar velocities of early and late type galaxies within the DESI footprint using both the Fundamental Plane and Tully-Fisher relations. Direct measurement
Externí odkaz:
http://arxiv.org/abs/2408.13842
This paper tackles the simultaneous optimization of pose and Neural Radiance Fields (NeRF). Departing from the conventional practice of using explicit global representations for camera pose, we propose a novel overparameterized representation that mo
Externí odkaz:
http://arxiv.org/abs/2407.12354
In this paper, we present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR. Depth-based reconstructions tend to focus on small-scale objects or large-scale SLAM reconstructions that treat moving objects as outliers.
Externí odkaz:
http://arxiv.org/abs/2406.13896
Autor:
Dogruel, M. Burak, Taylor, Edward, Cluver, Michelle, Colless, Matthew, de Graaff, Anna, Sonnenfeld, Alessandro, Lucey, John R., D'Eugenio, Francesco, Howlett, Cullan, Said, Khaled
Empirical correlations connecting starlight to galaxy dynamics (e.g., the fundamental plane (FP) of elliptical/quiescent galaxies and the Tully--Fisher relation of spiral/star-forming galaxies) provide cosmology-independent distance estimation and ar
Externí odkaz:
http://arxiv.org/abs/2405.10866
The training of vision transformer (ViT) networks on small-scale datasets poses a significant challenge. By contrast, convolutional neural networks (CNNs) have an architectural inductive bias enabling them to perform well on such problems. In this pa
Externí odkaz:
http://arxiv.org/abs/2404.01139
Low-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learning. These techniques significantly lower the number of parameters, st
Externí odkaz:
http://arxiv.org/abs/2403.19243
In the realm of computer vision, Neural Fields have gained prominence as a contemporary tool harnessing neural networks for signal representation. Despite the remarkable progress in adapting these networks to solve a variety of problems, the field st
Externí odkaz:
http://arxiv.org/abs/2403.19205