49 citations · 120 across the 10 of their papers we have counts for
6 papers · 1 filter
Learning Representations Robust to Group Shifts and Adversarial Examples
Ming-Chang Chiu, Xuezhe Ma
Despite the high performance achieved by deep neural networks on various tasks, extensive studies have demonstrated that small tweaks in the input could fail the model predictions.…
Examining and Combating Spurious Features under Distribution Shift
Chunting Zhou, Xuezhe Ma, Paul Michel +1
A central goal of machine learning is to learn robust representations that capture the causal relationship between inputs features and output labels. However, minimizing empirical…
Luna: Linear Unified Nested Attention
Xuezhe Ma, Xiang Kong, Sinong Wang +4
The quadratic computational and memory complexities of the Transformer's attention mechanism have limited its scalability for modeling long sequences. In this paper, we propose Lun…
MaCow: Masked Convolutional Generative Flow
Xuezhe Ma, Xiang Kong, Shanghang Zhang +1
Flow-based generative models, conceptually attractive due to tractability of both the exact log-likelihood computation and latent-variable inference, and efficiency of both trainin…
MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders
Xuezhe Ma, Chunting Zhou, Eduard Hovy
Variational Autoencoder (VAE), a simple and effective deep generative model, has led to a number of impressive empirical successes and spawned many advanced variants and theoretica…
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
Xuezhe Ma, Eduard Hovy
State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing. In this pape…