Zobrazeno 1 - 10
of 61 014
pro vyhledávání: '"Machine Learning (stat.ML)"'
Autor:
Lukasik, Michal, Nagarajan, Vaishnavh, Rawat, Ankit Singh, Menon, Aditya Krishna, Kumar, Sanjiv
The success of modern neural networks has prompted study of the connection between memorisation and generalisation: overparameterised models generalise well, despite being able to perfectly fit (memorise) completely random labels. To carefully study
Externí odkaz:
http://arxiv.org/abs/2310.05337
Autor:
Bian, Weijie, Wu, Kailun, Ren, Lejian, Pi, Qi, Zhang, Yujing, Xiao, Can, Sheng, Xiang-Rong, Zhu, Yong-Nan, Chan, Zhangming, Mou, Na, Luo, Xinchen, Xiang, Shiming, Zhou, Guorui, Zhu, Xiaoqiang, Deng, Hongbo
Feature interaction has been recognized as an important problem in machine learning, which is also very essential for click-through rate (CTR) prediction tasks. In recent years, Deep Neural Networks (DNNs) can automatically learn implicit nonlinear i
Externí odkaz:
http://arxiv.org/abs/2011.05625
Autor:
Jiang, Biye, Zhang, Pengye, Chen, Rihan, Dai, Binding, Luo, Xinchen, Yang, Yin, Wang, Guan, Zhou, Guorui, Zhu, Xiaoqiang, Gai, Kun
Modern large-scale systems such as recommender system and online advertising system are built upon computation-intensive infrastructure. The typical objective in these applications is to maximize the total revenue, e.g. GMV~(Gross Merchandise Volume)
Externí odkaz:
http://arxiv.org/abs/2006.09684
Autor:
Qi, Pi, Zhu, Xiaoqiang, Zhou, Guorui, Zhang, Yujing, Wang, Zhe, Ren, Lejian, Fan, Ying, Gai, Kun
Rich user behavior data has been proven to be of great value for click-through rate prediction tasks, especially in industrial applications such as recommender systems and online advertising. Both industry and academy have paid much attention to this
Externí odkaz:
http://arxiv.org/abs/2006.05639
Autor:
Shao, Han, Kugelstadt, Tassilo, Hädrich, Torsten, Pałubicki, Wojciech, Bender, Jan, Pirk, Sören, Michels, Dominik L.
Iterative solvers are widely used to accurately simulate physical systems. These solvers require initial guesses to generate a sequence of improving approximate solutions. In this contribution, we introduce a novel method to accelerate iterative solv
Externí odkaz:
http://arxiv.org/abs/2006.03897
Publikováno v:
Statistica Sinica.
Blockwise missing data occurs frequently when we integrate multisource or multimodality data where different sources or modalities contain complementary information. In this paper, we consider a high-dimensional linear regression model with blockwise
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9f72698355050bd5a8103146b0bb61e
https://doi.org/10.5705/ss.202022.0104
https://doi.org/10.5705/ss.202022.0104
Publikováno v:
European Journal of Operational Research. 310:737-750
In this paper, we consider the contextual variant of the MNL-Bandit problem. More specifically, we consider a dynamic set optimization problem, where a decision-maker offers a subset (assortment) of products to a consumer and observes the response in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0090a0c348b7357ee89bdde93257164a
https://doi.org/10.1016/j.ejor.2023.02.036
https://doi.org/10.1016/j.ejor.2023.02.036
Publikováno v:
Applied and Computational Harmonic Analysis. 66:1-17
This paper proposes a novel kernel-based optimization scheme to handle tasks in the analysis, e.g., signal spectral estimation and single-channel source separation of 1D non-stationary oscillatory data. The key insight of our optimization scheme for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54f0981791bc49800a932000cec0b284
https://doi.org/10.1016/j.acha.2023.04.002
https://doi.org/10.1016/j.acha.2023.04.002
Publikováno v:
Neural Networks. 164:606-616
Catastrophic forgetting (CF) happens whenever a neural network overwrites past knowledge while being trained on new tasks. Common techniques to handle CF include regularization of the weights (using, e.g., their importance on past tasks), and rehears
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72977a0dcc42008cab35be1d6d6ccbfb
https://doi.org/10.1016/j.neunet.2023.05.020
https://doi.org/10.1016/j.neunet.2023.05.020
Publikováno v:
Applied and Computational Harmonic Analysis. 65:279-295
We introduce an extension to local principal component analysis for learning symmetric manifolds. In particular, we use a spectral method to approximate the Lie algebra corresponding to the symmetry group of the underlying manifold. We derive the sam
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::894f6d72dced847abb93302bca9ee535
https://doi.org/10.1016/j.acha.2023.03.001
https://doi.org/10.1016/j.acha.2023.03.001