1 code implementation • 3 Jun 2021 • Hao liu, Qian Gao, Jiang Li, Xiaochao Liao, Hao Xiong, Guangxing Chen, Wenlin Wang, Guobao Yang, Zhiwei Zha, daxiang dong, Dejing Dou, Haoyi Xiong
In this work, we present JIZHI - a Model-as-a-Service system - that per second handles hundreds of millions of online inference requests to huge deep models with more than trillions of sparse parameters, for over twenty real-time recommendation services at Baidu, Inc.
no code implementations • EMNLP 2020 • Guoyin Wang, Chunyuan Li, Jianqiao Li, Hao Fu, Yuh-Chen Lin, Liqun Chen, Yizhe Zhang, Chenyang Tao, Ruiyi Zhang, Wenlin Wang, Dinghan Shen, Qian Yang, Lawrence Carin
An extension is further proposed to improve the OT learning, based on the structural and contextual information of the text sequences.
no code implementations • ACL 2020 • Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Dinghan Shen, Guoyin Wang, Zheng Wen, Lawrence Carin
Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation.
no code implementations • 20 Jan 2020 • Ruiyi Zhang, Changyou Chen, Zhe Gan, Zheng Wen, Wenlin Wang, Lawrence Carin
Reinforcement learning (RL) has been widely studied for improving sequence-generation models.
no code implementations • 20 Nov 2019 • Wenlin Wang, Hongteng Xu, Zhe Gan, Bai Li, Guoyin Wang, Liqun Chen, Qian Yang, Wenqi Wang, Lawrence Carin
We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework.
no code implementations • IJCNLP 2019 • Qian Yang, Zhouyuan Huo, Dinghan Shen, Yong Cheng, Wenlin Wang, Guoyin Wang, Lawrence Carin
Generating high-quality paraphrases is a fundamental yet challenging natural language processing task.
no code implementations • ICLR 2020 • Wenlin Wang, Hongteng Xu, Ruiyi Zhang, Wenqi Wang, Piyush Rai, Lawrence Carin
To address this, we propose a learning framework that improves collaborative filtering with a synthetic feedback loop (CF-SFL) to simulate the user feedback.
no code implementations • 20 Oct 2019 • Wenlin Wang, Hongteng Xu, Guoyin Wang, Wenqi Wang, Lawrence Carin
{Specifically, we build a conditional generative model to generate features from seen-class attributes, and establish an optimal transport between the distribution of the generated features and that of the real features.}
1 code implementation • NeurIPS 2019 • Wenlin Wang, Chenyang Tao, Zhe Gan, Guoyin Wang, Liqun Chen, Xinyuan Zhang, Ruiyi Zhang, Qian Yang, Ricardo Henao, Lawrence Carin
This paper considers a novel variational formulation of network embeddings, with special focus on textual networks.
1 code implementation • NeurIPS 2019 • Qian Yang, Zhouyuan Huo, Wenlin Wang, Heng Huang, Lawrence Carin
Model parallelism is required if a model is too large to fit in a single computing device.
no code implementations • ACL 2019 • Liqun Chen, Guoyin Wang, Chenyang Tao, Dinghan Shen, Pengyu Cheng, Xinyuan Zhang, Wenlin Wang, Yizhe Zhang, Lawrence Carin
Constituting highly informative network embeddings is an important tool for network analysis.
no code implementations • NAACL 2019 • Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, Lawrence Carin
We propose a topic-guided variational auto-encoder (TGVAE) model for text generation.
no code implementations • 15 May 2019 • Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin
Adversarial examples are carefully perturbed in-puts for fooling machine learning models.
no code implementations • ICLR 2019 • Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin
In this paper, we propose a powerful second-order attack method that reduces the accuracy of the defense model by Madry et al. (2017).
no code implementations • 17 Mar 2019 • Wenlin Wang, Zhe Gan, Hongteng Xu, Ruiyi Zhang, Guoyin Wang, Dinghan Shen, Changyou Chen, Lawrence Carin
We propose a topic-guided variational autoencoder (TGVAE) model for text generation.
no code implementations • 2 Nov 2018 • Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Liqun Chen, Dinghan Shen, Guoyin Wang, Lawrence Carin
Sequence generation with reinforcement learning (RL) has received significant attention recently.
no code implementations • NeurIPS 2018 • Hongteng Xu, Wenlin Wang, Wei Liu, Lawrence Carin
When learning the topic model, we leverage a distilled underlying distance matrix to update the topic distributions and smoothly calculate the corresponding optimal transports.
3 code implementations • NeurIPS 2019 • Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin
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, but lead to very different outputs from a deep-learning algorithm.
no code implementations • 29 May 2018 • Changyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen
There has been recent interest in developing scalable Bayesian sampling methods such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) for big-data analysis.
2 code implementations • ACL 2018 • Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Chunyuan Li, Ricardo Henao, Lawrence Carin
Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations.
Ranked #1 on Named Entity Recognition (NER) on CoNLL 2000
1 code implementation • ACL 2018 • Dinghan Shen, Qinliang Su, Paidamoyo Chapfuwa, Wenlin Wang, Guoyin Wang, Lawrence Carin, Ricardo Henao
Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems.
2 code implementations • ACL 2018 • Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin
Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences.
Ranked #11 on Text Classification on DBpedia
no code implementations • CVPR 2018 • Wenqi Wang, Yifan Sun, Brian Eriksson, Wenlin Wang, Vaneet Aggarwal
Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications.
no code implementations • ICLR 2018 • Dinghan Shen, Guoyin Wang, Wenlin Wang, Martin Renqiang Min, Qinliang Su, Yizhe Zhang, Ricardo Henao, Lawrence Carin
In this paper, we conduct an extensive comparative study between Simple Word Embeddings-based Models (SWEMs), with no compositional parameters, relative to employing word embeddings within RNN/CNN-based models.
no code implementations • 28 Dec 2017 • Wenlin Wang, Zhe Gan, Wenqi Wang, Dinghan Shen, Jiaji Huang, Wei Ping, Sanjeev Satheesh, Lawrence Carin
The TCNLM learns the global semantic coherence of a document via a neural topic model, and the probability of each learned latent topic is further used to build a Mixture-of-Experts (MoE) language model, where each expert (corresponding to one topic) is a recurrent neural network (RNN) that accounts for learning the local structure of a word sequence.
no code implementations • 1 Dec 2017 • Kai Fan, Qi Wei, Wenlin Wang, Amit Chakraborty, Katherine Heller
We propose a new method that uses deep learning techniques to solve the inverse problems.
no code implementations • 15 Nov 2017 • Wenlin Wang, Yunchen Pu, Vinay Kumar Verma, Kai Fan, Yizhe Zhang, Changyou Chen, Piyush Rai, Lawrence Carin
We present a deep generative model for learning to predict classes not seen at training time.
no code implementations • ICML 2018 • Changyou Chen, Chunyuan Li, Liqun Chen, Wenlin Wang, Yunchen Pu, Lawrence Carin
Distinct from normalizing flows and GANs, CTFs can be adopted to achieve the above two goals in one framework, with theoretical guarantees.
no code implementations • 4 Sep 2017 • Changyou Chen, Wenlin Wang, Yizhe Zhang, Qinliang Su, Lawrence Carin
However, there has been little theoretical analysis of the impact of minibatch size to the algorithm's convergence rate.
no code implementations • 14 Nov 2016 • Wenlin Wang, Changyou Chen, Wenqi Wang, Piyush Rai, Lawrence Carin
Unlike most existing methods for early classification of time series data, that are designed to solve this problem under the assumption of the availability of a good set of pre-defined (often hand-crafted) features, our framework can jointly perform feature learning (by learning a deep hierarchy of \emph{shapelets} capturing the salient characteristics in each time series), along with a dynamic truncation model to help our deep feature learning architecture focus on the early parts of each time series.