Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Léon Bottou"'
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
The discovery of hierarchies in biological processes is central to developmental biology. Here the authors propose Poincaré maps, a method based on hyperbolic geometry to discover continuous hierarchies from pairwise similarities.
Externí odkaz:
https://doaj.org/article/d26503839ec34d59b49e180b57c33053
Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets.Pervasive and networked computers have dramatically red
Machine learning systems based on minimizing average error have been shown to perform inconsistently across notable subsets of the data, which is not exposed by a low average error for the entire dataset. In consequential social and economic applicat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70483fd323faffd30e3fcdf9d0909cf6
Autor:
Aaron Defazio, Léon Bottou
Publikováno v:
Neural Computing and Applications. 34:14807-14821
We propose a system for calculating a “scaling constant” for layers and weights of neural networks. We relate this scaling constant to two important quantities that relate to the optimizability of neural networks, and argue that a network that is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c348b12b4b6fd7b9c621514fe13a060a
https://doi.org/10.1007/s00521-022-07308-z
https://doi.org/10.1007/s00521-022-07308-z
Publikováno v:
Nature Communications
Nature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
Nature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
The need to understand cell developmental processes spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclidean geometry, a suboptimal choice for modeling complex cel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::43bf53d68a49e5bda6ebe7ddac3413e0
https://doi.org/10.1038/s41467-020-16822-4
https://doi.org/10.1038/s41467-020-16822-4
Publikováno v:
Braverman Readings in Machine Learning. Key Ideas from Inception to Current State ISBN: 9783319994918
Braverman Readings in Machine Learning
Braverman Readings in Machine Learning
Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean D
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a73143db72512d3d775fd68bc7d2f6c4
https://doi.org/10.1007/978-3-319-99492-5_11
https://doi.org/10.1007/978-3-319-99492-5_11
Publikováno v:
CVPR
This paper establishes the existence of observable footprints that reveal the "causal dispositions" of the object categories appearing in collections of images. We achieve this goal in two steps. First, we take a learning approach to observational ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6f9e7bce66898dbd876f9130a2f6524
https://doi.org/10.1109/cvpr.2017.14
https://doi.org/10.1109/cvpr.2017.14
Publikováno v:
Machine Learning
Machine Learning, Springer Verlag, 2014, pp.1-5. ⟨10.1007/s10994-013-5381-4⟩
Machine Learning, Springer Verlag, 2014, pp.1-5. ⟨10.1007/s10994-013-5381-4⟩
A key ambition of AI is to render computers able to evolve and interact with the real world.This can be made possible only if the machine is able to produce an interpretation of its avail-able modalities (image, audio, text, etc.) which can be used t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b49516bb806dc5e422501898ef62471
https://doi.org/10.1007/s10994-013-5381-4
https://doi.org/10.1007/s10994-013-5381-4
Autor:
Léon Bottou
Publikováno v:
Machine Learning. 94:133-149
A plausible definition of "reasoning" could be "algebraically manipulating previously acquired knowledge in order to answer a new question". This definition covers first-order logical inference or probabilistic inference. It also includes much simple
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1fc25ec1762f40486d14befcbb5e9554
https://doi.org/10.1007/s10994-013-5335-x
https://doi.org/10.1007/s10994-013-5335-x
This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b938b3f9f0a67788ea8f338d2391cb0
http://arxiv.org/abs/1606.04838
http://arxiv.org/abs/1606.04838