Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Xie, Hanchen"'
Accurate estimation of counterfactual outcomes in high-dimensional data is crucial for decision-making and understanding causal relationships and intervention outcomes in various domains, including healthcare, economics, and social sciences. However,
Externí odkaz:
http://arxiv.org/abs/2407.20553
Autor:
Li, Jiazhi, Khayatkhoei, Mahyar, Zhu, Jiageng, Xie, Hanchen, Hussein, Mohamed E., AbdAlmageed, Wael
Ensuring a neural network is not relying on protected attributes (e.g., race, sex, age) for prediction is crucial in advancing fair and trustworthy AI. While several promising methods for removing attribute bias in neural networks have been proposed,
Externí odkaz:
http://arxiv.org/abs/2311.07141
Autor:
Li, Jiazhi, Khayatkhoei, Mahyar, Zhu, Jiageng, Xie, Hanchen, Hussein, Mohamed E., AbdAlmageed, Wael
Ensuring a neural network is not relying on protected attributes (e.g., race, sex, age) for predictions is crucial in advancing fair and trustworthy AI. While several promising methods for removing attribute bias in neural networks have been proposed
Externí odkaz:
http://arxiv.org/abs/2310.04955
Autor:
Zhu, Jiageng, Xie, Hanchen, Wu, Jianhua, Li, Jiazhi, Khayatkhoei, Mahyar, Hussein, Mohamed E., AbdAlmageed, Wael
Discovering causal relations among semantic factors is an emergent topic in representation learning. Most causal representation learning (CRL) methods are fully supervised, which is impractical due to costly labeling. To resolve this restriction, wea
Externí odkaz:
http://arxiv.org/abs/2308.05707
Autor:
Xie, Hanchen, Zhu, Jiageng, Khayatkhoei, Mahyar, Li, Jiazhi, Hussein, Mohamed E., AbdAlmageed, Wael
Dynamics prediction, which is the problem of predicting future states of scene objects based on current and prior states, is drawing increasing attention as an instance of learning physics. To solve this problem, Region Proposal Convolutional Interac
Externí odkaz:
http://arxiv.org/abs/2305.07648
Representation disentanglement is an important goal of representation learning that benefits various downstream tasks. To achieve this goal, many unsupervised learning representation disentanglement approaches have been developed. However, the traini
Externí odkaz:
http://arxiv.org/abs/2209.10623
Disentangled and invariant representations are two critical goals of representation learning and many approaches have been proposed to achieve either one of them. However, those two goals are actually complementary to each other so that we propose a
Externí odkaz:
http://arxiv.org/abs/2209.06827
Causal representation learning has been proposed to encode relationships between factors presented in the high dimensional data. However, existing methods suffer from merely using a large amount of labeled data and ignore the fact that samples genera
Externí odkaz:
http://arxiv.org/abs/2206.01802
Few-shot Learning has been studied to mimic human visual capabilities and learn effective models without the need of exhaustive human annotation. Even though the idea of meta-learning for adaptation has dominated the few-shot learning methods, how to
Externí odkaz:
http://arxiv.org/abs/2109.07607
Deep neural networks are powerful, massively parameterized machine learning models that have been shown to perform well in supervised learning tasks. However, very large amounts of labeled data are usually needed to train deep neural networks. Severa
Externí odkaz:
http://arxiv.org/abs/2012.00150