12 citations · 19 across the 7 of their papers we have counts for
7 papers
On the Universal Adversarial Perturbations for Efficient Data-free Adversarial Detection
Songyang Gao, Shihan Dou, Qi Zhang +3
Detecting adversarial samples that are carefully crafted to fool the model is a critical step to socially-secure applications. However, existing adversarial detection methods requi…
CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing
Songyang Gao, Shihan Dou, Junjie Shan +2
Dataset bias, i.e., the over-reliance on dataset-specific literal heuristics, is getting increasing attention for its detrimental effect on the generalization ability of NLU models…
Kernel-Whitening: Overcome Dataset Bias with Isotropic Sentence Embedding
Songyang Gao, Shihan Dou, Qi Zhang +1
Dataset bias has attracted increasing attention recently for its detrimental effect on the generalization ability of fine-tuned models. The current mainstream solution is designing…
Distributional Drift Adaptation with Temporal Conditional Variational Autoencoder for Multivariate Time Series Forecasting
Hui He, Qi Zhang, Kun Yi +3
Due to the non-stationary nature, the distribution of real-world multivariate time series (MTS) changes over time, which is known as distribution drift. Most existing MTS forecasti…
Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning
Minlong Peng, Xiaoyu Xing, Qi Zhang +2
In this work, we explore the way to perform named entity recognition (NER) using only unlabeled data and named entity dictionaries. To this end, we formulate the task as a positive…
Learning Task-specific Representation for Novel Words in Sequence Labeling
Minlong Peng, Qi Zhang, Xiaoyu Xing +3
Word representation is a key component in neural-network-based sequence labeling systems. However, representations of unseen or rare words trained on the end task are usually poor…
Long Short-Term Memory with Dynamic Skip Connections
Tao Gui, Qi Zhang, Lujun Zhao +4
In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing lo…