145 citations · 424 across the 41 of their papers we have counts for
8 papers · 1 filter
A Hypergradient Approach to Robust Regression without Correspondence
Yujia Xie, Yixiu Mao, Simiao Zuo +4
We consider a variant of regression problem, where the correspondence between input and output data is not available. Such shuffled data is commonly observed in many real world pro…
On the Dynamics of Training Attention Models
Haoye Lu, Yongyi Mao, Amiya Nayak
The attention mechanism has been widely used in deep neural networks as a model component. By now, it has become a critical building block in many state-of-the-art natural language…
Parallel Interactive Networks for Multi-Domain Dialogue State Generation
Junfan Chen, Richong Zhang, Yongyi Mao +1
The dependencies between system and user utterances in the same turn and across different turns are not fully considered in existing multidomain dialogue state tracking (MDST) mode…
Neural Dialogue State Tracking with Temporally Expressive Networks
Junfan Chen, Richong Zhang, Yongyi Mao +1
Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to exp…
Regularizing Neural Networks via Adversarial Model Perturbation
Yaowei Zheng, Richong Zhang, Yongyi Mao
Effective regularization techniques are highly desired in deep learning for alleviating overfitting and improving generalization. This work proposes a new regularization scheme, ba…
On SkipGram Word Embedding Models with Negative Sampling: Unified Framework and Impact of Noise Distributions
Dezhi Liu, Richong Zhang, Ziqiao Wang
SkipGram word embedding models with negative sampling, or SGN in short, is an elegant family of word embedding models. In this paper, we formulate a framework for word embedding, r…
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
Kai Sun, Richong Zhang, Samuel Mensah +2
The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the…
Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers
Masoumeh Soflaei, Hongyu Guo, Ali Al-Bashabsheh +2
We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation lea…