Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Jiayu Zhou"'
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 33:2764-2775
Predictive modeling of large-scale spatio-temporal data is an important but challenging problem as it requires training models that can simultaneously predict the target variables of interest at multiple locations while preserving the spatial and tem
Publikováno v:
ACM Transactions on Sensor Networks; Feb2023, Vol. 19 Issue 1, p1-32, 32p
Publikováno v:
Neurocomputing. 364:95-107
There are many major diseases that remain incurable, such as cancer and Alzheimer’s disease (AD). Therefore, the prevention for these diseases has more impact than diagnosis and treatment. Survival analysis aims at predicting the time of occurrence
Publikováno v:
SenSys
Deep learning has been increasingly applied to improve human activity recognition (HAR) accuracy and reduce the human efforts of handcrafted feature extractions. Federated Learning (FL) is an emerging learning paradigm that enables the collaborative
Publikováno v:
MobiSys
Federated Learning (FL) has recently received significant interests thanks to its capability of protecting data privacy. However, existing FL paradigms yield unsatisfactory performance for a wide class of human activity recognition (HAR) applications
Publikováno v:
AAAI
Traditional online multitask learning only utilizes the firstorder information of the datastream. To remedy this issue, we propose a confidence weighted multitask learning algorithm, which maintains a Gaussian distribution over each task model to gui
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 29:1268-1280
Ensemble forecasting is a widely-used numerical prediction method for modeling the evolution of nonlinear dynamic systems. To predict the future state of such systems, a set of ensemble member forecasts is generated from multiple runs of computer mod
Publikováno v:
KDD
Knowledge transfer has been of great interest in current machine learning research, as many have speculated its importance in modeling the human ability to rapidly generalize learned models to new scenarios. Particularly in cases where training sampl
Publikováno v:
IEEE Trans Pattern Anal Mach Intell
Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together. In contrast, in single-task learning (STL) each individual task is learned independently. MTL often leads to better trained models because they can leverage
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad97dda6bc721ff987f358067c68eb32
http://arxiv.org/abs/1809.06546
http://arxiv.org/abs/1809.06546
Publikováno v:
KDD
Multilevel modeling and multi-task learning are two widely used approaches for modeling nested (multi-level) data, which contain observations that can be clustered into groups, characterized by their group-level features. Despite the similarity of th