Zobrazeno 1 - 10
of 156
pro vyhledávání: '"Gavves, E."'
Robust Multi-Agent Reinforcement Learning with Social Empowerment for Coordination and Communication
We consider the problem of robust multi-agent reinforcement learning (MARL) for cooperative communication and coordination tasks. MARL agents, mainly those trained in a centralized way, can be brittle because they can adopt policies that act under th
Externí odkaz:
http://arxiv.org/abs/2012.08255
Publikováno v:
2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML) at the 40th International Conference on Machine Learning
The problem of detecting and quantifying the presence of symmetries in datasets is useful for model selection, generative modeling, and data analysis, amongst others. While existing methods for hard-coding transformations in neural networks require p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee0fa4bef6f86bf9ad25b4e3916b1d0e
http://arxiv.org/abs/2307.01583
http://arxiv.org/abs/2307.01583
Akademický článek
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WeakSTIL: weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need
Autor:
Schirris, Y., Engelaer, M., Panteli, A., Horlings, H.M., Gavves, E., Teuwen, J., Tomaszewski, J.E., Ward, A.D., Levenson, R.M.
Publikováno v:
Medical Imaging 2022: Digital and Computational Pathology: 21-27 March 2022
Medical Imaging 2022: Digital and Computational Pathology
Medical Imaging 2022: Digital and Computational Pathology
We present WeakSTIL, an interpretable two-stage weak label deep learning pipeline for scoring the percentage of stromal tumor infiltrating lymphocytes (sTIL%) in H&E-stained whole-slide images (WSIs) of breast cancer tissue. The sTIL% score is a prog
Autor:
Bereska, L., Gavves, E.
Publikováno v:
Proceedings of Machine Learning Research, 199, 335-350
Conference on Lifelong Learning Agents
Conference on Lifelong Learning Agents
Machine learning recently proved efficient in learning differential equations and dynamical systems from data. However, the data is commonly assumed to originate from a single never-changing system. In contrast, when modeling real-world dynamical pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c01716cd8d5e7d63ac07ddf10a3d2c4e
Autor:
Habibian, A., Ben Yahia, H., Abati, D., Gavves, E., Porikli, F., Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T.
Publikováno v:
Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022 : proceedings, XXXV, 213-229
Lecture Notes in Computer Science ISBN: 9783031198328
Lecture Notes in Computer Science ISBN: 9783031198328
This paper aims to accelerate video stream processing, such as object detection and semantic segmentation, by leveraging the temporal redundancies that exist between video frames. Instead of propagating and warping features using motion alignment, su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e62fe49f9b549a41b4e80a8f262d828
https://dare.uva.nl/personal/pure/en/publications/delta-distillation-for-efficient-video-processing(bb5e74b2-a6f2-4807-bbf3-defd773a932c).html
https://dare.uva.nl/personal/pure/en/publications/delta-distillation-for-efficient-video-processing(bb5e74b2-a6f2-4807-bbf3-defd773a932c).html
Autor:
Chen, Y., Fernando, B., Bilen, H., Nießner, M., Gavves, E., Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T.
Publikováno v:
Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022 : proceedings, III
In recent years, neural implicit representations have made remarkable progress in modeling of 3D shapes with arbitrary topology. In this work, we address two key limitations of such representations, in failing to capture local 3D geometric fine detai
Autor:
Gavves, E., Kofinas, M., Nagaraja, N., Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P.S., Wortman Vaughan, J.
Publikováno v:
35th Conference on Neural Information Processing Systems (NeurIPS 2021): online, 6-14 December 2021, 8, 6417-6429
Modelling interactions is critical in learning complex dynamical systems, namely systems of interacting objects with highly non-linear and time-dependent behaviour. A large class of such systems can be formalized as geometric graphs, i.e. graphs with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::2ecd09a02bdf3f61a92f09206ff007ff
https://dare.uva.nl/personal/pure/en/publications/rototranslated-local-coordinate-frames-for-interacting-dynamical-systems(5c8a8182-8dce-4fdb-bacf-98d987af0bfd).html
https://dare.uva.nl/personal/pure/en/publications/rototranslated-local-coordinate-frames-for-interacting-dynamical-systems(5c8a8182-8dce-4fdb-bacf-98d987af0bfd).html
Akademický článek
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Autor:
Pervez, A., Gavves, E.
Publikováno v:
Proceedings of Machine Learning Research, 139, 8536-8545
Variational autoencoders with deep hierarchies of stochastic layers have been known to suffer from the problem of posterior collapse, where the top layers fall back to the prior and become independent of input. We suggest that the hierarchical VAE ob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::15b5c36a9f1d0326f9574a250a53f4be
https://dare.uva.nl/personal/pure/en/publications/spectral-smoothing-unveils-phase-transitions-in-hierarchical-variational-autoencoders(2acaa483-98b6-4f41-b403-63cbdd3a1504).html
https://dare.uva.nl/personal/pure/en/publications/spectral-smoothing-unveils-phase-transitions-in-hierarchical-variational-autoencoders(2acaa483-98b6-4f41-b403-63cbdd3a1504).html