185 citations · 617 across the 26 of their papers we have counts for
8 papers · 1 filter
Sequence Generation with Guider Network
Ruiyi Zhang, Changyou Chen, Zhe Gan +5
Sequence generation with reinforcement learning (RL) has received significant attention recently. However, a challenge with such methods is the sparse-reward problem in the RL trai…
Distilled Wasserstein Learning for Word Embedding and Topic Modeling
Hongteng Xu, Wenlin Wang, Wei Liu +1
We propose a novel Wasserstein method with a distillation mechanism, yielding joint learning of word embeddings and topics. The proposed method is based on the fact that the Euclid…
Certified Adversarial Robustness with Additive Noise
Bai Li, Changyou Chen, Wenlin Wang +1
The existence of adversarial data examples has drawn significant attention in the deep-learning community; such data are seemingly minimally perturbed relative to the original data…
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Dinghan Shen, Guoyin Wang, Wenlin Wang +6
Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations. Howeve…
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing
Dinghan Shen, Qinliang Su, Paidamoyo Chapfuwa +4
Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems. While fairly successful, previous techniques generally require two…
Joint Embedding of Words and Labels for Text Classification
Guoyin Wang, Chunyuan Li, Wenlin Wang +5
Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences. We propose to vie…