Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Laurens van der Maaten"'
Publikováno v:
PLoS ONE, Vol 8, Iss 1, p e52884 (2013)
The interpretation of biological data sets is essential for generating hypotheses that guide research, yet modern methods of global analysis challenge our ability to discern meaningful patterns and then convey results in a way that can be easily appr
Externí odkaz:
https://doaj.org/article/f3fb134332e44aa491b6b5466e81f768
Publikováno v:
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.
Machine-learning models contain information about the data they were trained on. This information leaks either through the model itself or through predictions made by the model. Consequently, when the training data contains sensitive attributes, asse
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::961cc1f50ada31d91e5b5d646c80ec3a
https://doi.org/10.24963/ijcai.2022/736
https://doi.org/10.24963/ijcai.2022/736
Publikováno v:
Information and Inference: A Journal of the IMA. 11:103-135
Secure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data while being unable to glean much information about the data (at least not
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7f31d6e3bbc599c7df2f91d9813ac1c
https://doi.org/10.1093/imaiai/iaaa038
https://doi.org/10.1093/imaiai/iaaa038
Autor:
Rohit Girdhar, Mannat Singh, Nikhila Ravi, Laurens van der Maaten, Armand Joulin, Ishan Misra
Prior work has studied different visual modalities in isolation and developed separate architectures for recognition of images, videos, and 3D data. Instead, in this paper, we propose a single model which excels at classifying images, videos, and sin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8dd602ae96d7f6b1c22119ed909baa11
http://arxiv.org/abs/2201.08377
http://arxiv.org/abs/2201.08377
Federated learning (FL) enables clients to collaborate with a server to train a machine learning model. To ensure privacy, the server performs secure aggregation of updates from the clients. Unfortunately, this prevents verification of the well-forme
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e326bc1413df21c29e078f727ee41ae
http://arxiv.org/abs/2112.12727
http://arxiv.org/abs/2112.12727
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff96793c466943f4f5667a7f15401540
http://arxiv.org/abs/2001.02394
http://arxiv.org/abs/2001.02394
Autor:
Laurens van der Maaten, Ishan Misra
Publikováno v:
CVPR
The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations for a large training set of images. Many pretext tasks lead to represen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a3f3526aec0739162c24bcc68ddc94e5
http://arxiv.org/abs/1912.01991
http://arxiv.org/abs/1912.01991
Publikováno v:
ICCV Workshops
Providing systems the ability to relate linguistic and visual content is one of the hallmarks of computer vision. Tasks such as text-based image retrieval and image captioning were designed to test this ability but come with evaluation measures that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef2ae990ca21e5f3f0f22dcb16cfa803
http://arxiv.org/abs/1901.06595
http://arxiv.org/abs/1901.06595
Publikováno v:
CVPR
A plethora of recent work has shown that convolutional networks are not robust to adversarial images: images that are created by perturbing a sample from the data distribution as to maximize the loss on the perturbed example. In this work, we hypothe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65126d6771172d9559d75709eaaae577
Publikováno v:
The Journal of Immunology. 196:924-932
Rapid progress in single-cell analysis methods allow for exploration of cellular diversity at unprecedented depth and throughput. Visualizing and understanding these large, high-dimensional datasets poses a major analytical challenge. Mass cytometry
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ccbb8e7da330f56c99e326f0bf7d55f0
https://doi.org/10.4049/jimmunol.1501928
https://doi.org/10.4049/jimmunol.1501928