Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Marks, Markus"'
Autor:
Marks, Markus, Knott, Manuel, Kondapaneni, Neehar, Cole, Elijah, Defraeye, Thijs, Perez-Cruz, Fernando, Perona, Pietro
Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn about the data structure or context by solving a pretext task. With SSL,
Externí odkaz:
http://arxiv.org/abs/2407.12210
We introduce Discovering Conceptual Network Explanations (DCNE), a new approach for generating human-comprehensible visual explanations to enhance the interpretability of deep neural image classifiers. Our method automatically finds visual explanatio
Externí odkaz:
http://arxiv.org/abs/2405.15243
Autor:
Israel, Uriah, Marks, Markus, Dilip, Rohit, Li, Qilin, Schwartz, Morgan, Pradhan, Elora, Pao, Edward, Li, Shenyi, Pearson-Goulart, Alexander, Perona, Pietro, Gkioxari, Georgia, Barnowski, Ross, Yue, Yisong, Van Valen, David
Cells are the fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. While deep learning methods have led to substantial progress on this pr
Externí odkaz:
http://arxiv.org/abs/2311.11004
Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However, the best way to harness the perceptual knowledge of these gener
Externí odkaz:
http://arxiv.org/abs/2310.00031
Autor:
Sun, Jennifer J., Marks, Markus, Ulmer, Andrew, Chakraborty, Dipam, Geuther, Brian, Hayes, Edward, Jia, Heng, Kumar, Vivek, Oleszko, Sebastian, Partridge, Zachary, Peelman, Milan, Robie, Alice, Schretter, Catherine E., Sheppard, Keith, Sun, Chao, Uttarwar, Param, Wagner, Julian M., Werner, Eric, Parker, Joseph, Perona, Pietro, Yue, Yisong, Branson, Kristin, Kennedy, Ann
We introduce MABe22, a large-scale, multi-agent video and trajectory benchmark to assess the quality of learned behavior representations. This dataset is collected from a variety of biology experiments, and includes triplets of interacting mice (4.7
Externí odkaz:
http://arxiv.org/abs/2207.10553
Disentanglement is at the forefront of unsupervised learning, as disentangled representations of data improve generalization, interpretability, and performance in downstream tasks. Current unsupervised approaches remain inapplicable for real-world da
Externí odkaz:
http://arxiv.org/abs/2010.13527
Publikováno v:
In NeuroImage 1 November 2021 241
Autor:
Mannion, Kelly Ray1 (AUTHOR) kellyray7891@gmail.com, Ballare, Elizabeth F.1 (AUTHOR), Marks, Markus2,3 (AUTHOR), Gruber, Thibaud1,4 (AUTHOR) thibaud.gruber@unige.ch
Publikováno v:
Journal of Animal Ecology. Aug2023, Vol. 92 Issue 8, p1478-1488. 11p.
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Akademický článek
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