7 citations · 18 across the 8 of their papers we have counts for
8 papers
A Simple Decentralized Cross-Entropy Method
Zichen Zhang, Jun Jin, Martin Jagersand +2
Cross-Entropy Method (CEM) is commonly used for planning in model-based reinforcement learning (MBRL) where a centralized approach is typically utilized to update the sampling dist…
Build generally reusable agent-environment interaction models
Jun Jin, Hongming Zhang, Jun Luo
This paper tackles the problem of how to pre-train a model and make it generally reusable backbones for downstream task learning. In pre-training, we propose a method that builds a…
Drawing Inductor Layout with a Reinforcement Learning Agent: Method and Application for VCO Inductors
Cameron Haigh, Zichen Zhang, Negar Hassanpour +5
Design of Voltage-Controlled Oscillator (VCO) inductors is a laborious and time-consuming task that is conventionally done manually by human experts. In this paper, we propose a fr…
Deep Curriculum Learning in Task Space for Multi-Class Based Mammography Diagnosis
Jun Luo, Dooman Arefan, Margarita Zuley +2
Mammography is used as a standard screening procedure for the potential patients of breast cancer. Over the past decade, it has been shown that deep learning techniques have succee…
FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification
Jun Luo, Shandong Wu
Machine learning in medical research, by nature, needs careful attention on obeying the regulations of data privacy, making it difficult to train a machine learning model over gath…
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning
Jun Luo, Shandong Wu
Conventional federated learning (FL) trains one global model for a federation of clients with decentralized data, reducing the privacy risk of centralized training. However, the di…