699 citations · 3.7k across the 65 of their papers we have counts for
8 papers · 1 filter
Robust Navigation with Language Pretraining and Stochastic Sampling
Xiujun Li, Chunyuan Li, Qiaolin Xia +5
Core to the vision-and-language navigation (VLN) challenge is building robust instruction representations and action decoding schemes, which can generalize well to previously unsee…
Implicit Deep Latent Variable Models for Text Generation
Le Fang, Chunyuan Li, Jianfeng Gao +2
Deep latent variable models (LVM) such as variational auto-encoder (VAE) have recently played an important role in text generation. One key factor is the exploitation of smooth lat…
Twin Auxiliary Classifiers GAN
Mingming Gong, Yanwu Xu, Chunyuan Li +2
Conditional generative models enjoy remarkable progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly…
DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain
Yichong Xu, Xiaodong Liu, Chunyuan Li +2
This paper describes our competing system to enter the MEDIQA-2019 competition. We use a multi-source transfer learning approach to transfer the knowledge from MT-DNN and SciBERT t…
Towards Amortized Ranking-Critical Training for Collaborative Filtering
Sam Lobel, Chunyuan Li, Jianfeng Gao +1
Collaborative filtering is widely used in modern recommender systems. Recent research shows that variational autoencoders (VAEs) yield state-of-the-art performance by integrating f…
Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing
Hao Fu, Chunyuan Li, Xiaodong Liu +3
Variational autoencoders (VAEs) with an auto-regressive decoder have been applied for many natural language processing (NLP) tasks. The VAE objective consists of two terms, (i) rec…
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
Ruqi Zhang, Chunyuan Li, Jianyi Zhang +2
The posteriors over neural network weights are high dimensional and multimodal. Each mode typically characterizes a meaningfully different representation of the data. We develop Cy…
Adversarial Learning of a Sampler Based on an Unnormalized Distribution
Chunyuan Li, Ke Bai, Jianqiao Li +3
We investigate adversarial learning in the case when only an unnormalized form of the density can be accessed, rather than samples. With insights so garnered, adversarial learning…