Eric C. Yeats

According to our database, Eric C. Yeats authored at least 9 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Min-K%++: Improved Baseline for Pre-Training Data Detection from Large Language Models.

Jingyang Zhang, Jingwei Sun, Eric C. Yeats, Yang Ouyang, Martin Kuo, Jianyi Zhang, Hao (Frank) Yang, Hai Li

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Do Counterfactual Examples Complicate Adversarial Training?

Eric C. Yeats, Cameron Darwin, Eduardo Ortega, Frank Liu, Hai Li

CoRR, 2024

Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models.

Jingyang Zhang, Jingwei Sun, Eric C. Yeats, Yang Ouyang, Martin Kuo, Jianyi Zhang, Hao Yang, Hai Helen Li

CoRR, 2024

MulPi: A Multi-Class and Patient-Independent Computing-in-SRAM Seizure Classifier.

Bokyung Kim, Qijia Huang, Brady Taylor, Qilin Zheng, Jonathan Ku, Nicky Ramos, Eric C. Yeats, Yiran Chen, Hai Helen Li

Proceedings of the IEEE Biomedical Circuits and Systems Conference, 2024

2023

Adversarial Estimation of Topological Dimension with Harmonic Score Maps.

Eric C. Yeats, Cameron Darwin, Frank Liu, Hai Li

CoRR, 2023

Disentangling Learning Representations with Density Estimation.

Eric C. Yeats, Frank Y. Liu, Hai Helen Li

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

NashAE: Disentangling Representations Through Adversarial Covariance Minimization.

Eric C. Yeats, Frank Liu, David Womble, Hai Helen Li

Proceedings of the Computer Vision - ECCV 2022, 2022

CMOS Implementation of Spiking Equilibrium Propagation for Real-Time Learning.

Brady Taylor, Nicky Ramos, Eric C. Yeats, Hai Li

Proceedings of the 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, 2022

2021

Improving Gradient Regularization using Complex-Valued Neural Networks.

Eric C. Yeats, Yiran Chen, Hai Li

Proceedings of the 38th International Conference on Machine Learning, 2021