Zobrazeno 1 - 10
of 122
pro vyhledávání: '"Kate Saenko"'
Autor:
Shoumik Sovan Majumdar, Shubhangi Jain, Isidora Chara Tourni, Arsenii Mustafin, Diala Lteif, Stan Sclaroff, Kate Saenko, Sarah Adel Bargal
Publikováno v:
Frontiers in Computer Science, Vol 4 (2022)
Deep learning models perform remarkably well for the same task under the assumption that data is always coming from the same distribution. However, this is generally violated in practice, mainly due to the differences in data acquisition techniques a
Externí odkaz:
https://doaj.org/article/5828719423a9478eac6b79c08033ef8a
Autor:
Olivia Watkins, Sandy Huang, Julius Frost, Kush Bhatia, Eric Weiner, Pieter Abbeel, Trevor Darrell, Bryan Plummer, Kate Saenko, Anca Dragan
Publikováno v:
Applied AI Letters, Vol 2, Iss 4, Pp n/a-n/a (2021)
Abstract In order to interact with a robot or make wise decisions about where and how to deploy it in the real world, humans need to have an accurate mental model of how the robot acts in different situations. We propose to improve users' mental mode
Externí odkaz:
https://doaj.org/article/4a45e57a3c934206a2404bdbc0f3f702
Publikováno v:
Applied AI Letters, Vol 2, Iss 4, Pp n/a-n/a (2021)
Abstract In complex inferential tasks like question answering, machine learning models must confront two challenges: the need to implement a compositional reasoning process, and, in many applications, the need for this reasoning process to be interpr
Externí odkaz:
https://doaj.org/article/70c88cb6375c4fac84d305ef5a8207ea
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::69cbce1a6038ec16e771abe171e590a0
https://doi.org/10.1109/wacv56688.2023.00245
https://doi.org/10.1109/wacv56688.2023.00245
Publikováno v:
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 31
Label-efficient scene segmentation aims to achieve effective per-pixel classification with reduced labeling effort. Recent approaches for this task focus on leveraging unlabelled images by formulating consistency regularization or pseudo labels for i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4549bb7fa8e0b1204a8c9b496fde41d
https://pubmed.ncbi.nlm.nih.gov/36194704
https://pubmed.ncbi.nlm.nih.gov/36194704
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200526
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::adb37a7244dd85124f75169f401089a3
https://doi.org/10.1007/978-3-031-20053-3_16
https://doi.org/10.1007/978-3-031-20053-3_16
Autor:
Jack Hessel, Jena D. Hwang, Jae Sung Park, Rowan Zellers, Chandra Bhagavatula, Anna Rohrbach, Kate Saenko, Yejin Choi
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200588
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f039a20cbc68ecea512b1e7d14928f04
https://doi.org/10.1007/978-3-031-20059-5_32
https://doi.org/10.1007/978-3-031-20059-5_32
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200588
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0cd3238c842dab4505702877bbb206f0
https://doi.org/10.1007/978-3-031-20059-5_37
https://doi.org/10.1007/978-3-031-20059-5_37
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198267
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cc76c55027afea0a731b75da14c8178e
https://doi.org/10.1007/978-3-031-19827-4_36
https://doi.org/10.1007/978-3-031-19827-4_36
Autor:
Sarah Adel Bargal, Andrea Zunino, Vitali Petsiuk, Jianming Zhang, Vittorio Murino, Stan Sclaroff, Kate Saenko
Publikováno v:
xxAI-Beyond Explainable AI ISBN: 9783031040825
Increased explainability in machine learning is traditionally associated with lower performance, e.g. a decision tree is more explainable, but less accurate than a deep neural network. We argue that, in fact, increasing the explainability of a deep c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56522f7f8eddf6e30808ba3ebc3338eb
https://doi.org/10.1007/978-3-031-04083-2_13
https://doi.org/10.1007/978-3-031-04083-2_13