Yi Ma

A method for evaluating the rescue priority level of power line post-disaster based on AHP

2017

In recent years, various natural disasters occurred frequently, such as earthquake, typhoon, etc. These disasters constitute a great threat to the safe and stable operation of the power grid, at the same time, it also …

On Deep Learning Solutions for Joint Transmitter and Noncoherent Receiver Design in MU-MIMO Systems

2020 · arXiv (Cornell University)

This paper aims to handle the joint transmitter and noncoherent receiver design for multiuser multiple-input multiple-output (MU-MIMO) systems through deep learning. Given the deep neural network (DNN) based noncoherent receiver, the novelty of this work …

Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction

2020 · arXiv (Cornell University)

To learn intrinsic low-dimensional structures from high-dimensional data that most discriminate between classes, we propose the principle of Maximal Coding Rate Reduction ($\text{MCR}^2$), an information-theoretic measure that maximizes the coding rate difference between the whole …

Robust Calibration with Multi-domain Temperature Scaling

2022 · arXiv (Cornell University)

Uncertainty quantification is essential for the reliable deployment of machine learning models to high-stakes application domains. Uncertainty quantification is all the more challenging when training distribution and test distribution are different, even the distribution shifts …

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning

2023 · arXiv (Cornell University)

A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interaction. However, existing offline RL methods tend to behave poorly …

Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction

2024 · arXiv (Cornell University)

The attention operator is arguably the key distinguishing factor of transformer architectures, which have demonstrated state-of-the-art performance on a variety of tasks. However, transformer attention operators often impose a significant computational burden, with the computational …

Importance-Aware Source-Channel Coding for Multi-Modal Task-Oriented Semantic Communication

2025 · arXiv (Cornell University)

This paper explores the concept of information importance in multi-modal task-oriented semantic communication systems, emphasizing the need for high accuracy and efficiency to fulfill task-specific objectives. At the transmitter, generative AI (GenAI) is employed to …