1.2k citations · 2.1k across the 35 of their papers we have counts for
5 papers · 2 filters
Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations
Wenlei Shi, Xinran Wei, Jia Zhang +4
Multi-agent reinforcement learning (MARL) has been increasingly explored to learn the cooperative policy towards maximizing a certain global reward. Many existing studies take adva…
ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting
Hongshun Tang, Lijun Wu, Weiqing Liu +1
Stock trend forecasting has become a popular research direction that attracts widespread attention in the financial field. Though deep learning methods have achieved promising resu…
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
Zhining Liu, Pengfei Wei, Jing Jiang +3
Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed…
Learning to Reweight with Deep Interactions
Yang Fan, Yingce Xia, Lijun Wu +5
Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real t…
Measuring Model Complexity of Neural Networks with Curve Activation Functions
Xia Hu, Weiqing Liu, Jiang Bian +1
It is fundamental to measure model complexity of deep neural networks. The existing literature on model complexity mainly focuses on neural networks with piecewise linear activatio…