no code implementations • 5 Feb 2023 • Daniel D Kim, Rajat S Chandra, Jian Peng, Jing Wu, Xue Feng, Michael Atalay, Chetan Bettegowda, Craig Jones, Haris Sair, Wei-Hua Liao, Chengzhang Zhu, Beiji Zou, Li Yang, Anahita Fathi Kazerooni, Ali Nabavizadeh, Harrison X Bai, Zhicheng Jiao
We investigated uncertainty sampling, annotation redundancy restriction, and initial dataset selection techniques.
no code implementations • 3 Dec 2021 • Longbing Cao, Chengzhang Zhu
The learned universal representation of each customer is all-round, representative and benchmarkable to support both enterprise-wide and domain-specific learning goals and tasks in enterprise data science.
no code implementations • 19 Aug 2021 • Longbing Cao, Chengzhang Zhu
CRN represents multiple coupled dynamic sequences of a customer's historical and current states, responses to decision-makers' actions, decision rewards to actions, and learns long-term multi-sequence interactions between parties (customer and decision-maker).
no code implementations • 21 Jul 2020 • Chengzhang Zhu, Longbing Cao, Jianping Yin
This work introduces a shallow but powerful UNsupervised heTerogeneous couplIng lEarning (UNTIE) approach for representing coupled categorical data by untying the interactions between couplings and revealing heterogeneous distributions embedded in each type of couplings.