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Found 8 papers, 3 papers with code
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components
In this work, we study the problem of common and unique feature extraction from noisy data.
Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data
We propose personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets.
Adam Can Converge Without Any Modification On Update Rules
We point out there is a mismatch between the settings of theory and practice: Reddi et al. 2018 pick the problem after picking the hyperparameters of Adam, i. e., $(\beta_1, \beta_2)$; while practical applications often fix the problem first and then tune $(\beta_1, \beta_2)$.
Personalized PCA: Decoupling Shared and Unique Features
In this paper, we tackle a significant challenge in PCA: heterogeneity.
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
The Internet of Things (IoT) is on the verge of a major paradigm shift.
Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning
In this paper we propose Fed-ensemble: a simple approach that bringsmodel ensembling to federated learning (FL).
ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep Reinforcement Learning
Since trick-taking game requires high level of not only reasoning, but also inference to excel, it can be a new milestone for imperfect information game AI.
RMSprop can converge with proper hyper-parameter
Removing this assumption allows us to establish a phase transition from divergence to non-divergence for RMSProp.
