Ziming Zhang

BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning

2017 · arXiv (Cornell University)

Understanding the global optimality in deep learning (DL) has been attracting more and more attention recently. Conventional DL solvers, however, have not been developed intentionally to seek for such global optimality. In this paper we …

Deep Incremental RNN for Learning Sequential Data: A Lyapunov Stable Dynamical System

2021

With the recent advances in mobile sensing technologies, large amounts of sequential data are collected, such as vehicle GPS records, stock prices, sensor data from air quality detectors. Recurrent neural networks (RNNs) have been studied …

Understanding Hyperbolic Metric Learning through Hard Negative Sampling

2024

In recent years, there has been a growing trend of incorporating hyperbolic geometry methods into computer vision. While these methods have achieved state-of-the-art performance on various metric learning tasks using hyperbolic distance measurements, the underlying …