89 citations · 163 across the 5 of their papers we have counts for
15 papers
How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers
Yuanhao Xiong, Xuanqing Liu, Li-Cheng Lan +3
Many optimizers have been proposed for training deep neural networks, and they often have multiple hyperparameters, which make it tricky to benchmark their performance. In this wor…
Improving the Speed and Quality of GAN by Adversarial Training
Jiachen Zhong, Xuanqing Liu, Cho-Jui Hsieh
Generative adversarial networks (GAN) have shown remarkable results in image generation tasks. High fidelity class-conditional GAN methods often rely on stabilization techniques by…
Provably Robust Metric Learning
Lu Wang, Xuanqing Liu, Jinfeng Yi +2
Metric learning is an important family of algorithms for classification and similarity search, but the robustness of learned metrics against small adversarial perturbations is less…
Evaluations and Methods for Explanation through Robustness Analysis
Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu +4
Feature based explanations, that provide importance of each feature towards the model prediction, is arguably one of the most intuitive ways to explain a model. In this paper, we e…
Learning to Encode Position for Transformer with Continuous Dynamical Model
Xuanqing Liu, Hsiang-Fu Yu, Inderjit Dhillon +1
We introduce a new way of learning to encode position information for non-recurrent models, such as Transformer models. Unlike RNN and LSTM, which contain inductive bias by loading…
Gradient Boosting Neural Networks: GrowNet
Sarkhan Badirli, Xuanqing Liu, Zhengming Xing +3
A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''. General loss functions are considered under this unified framework…