Search Results for author: Li Deng

Found 53 papers, 19 papers with code

MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System

1 code implementation17 Nov 2021 Rhys Compton, Ilya Valmianski, Li Deng, Costa Huang, Namit Katariya, Xavier Amatriain, Anitha Kannan

We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module.

Multimodal Intelligence: Representation Learning, Information Fusion, and Applications

no code implementations10 Nov 2019 Chao Zhang, Zichao Yang, Xiaodong He, Li Deng

This review provides a comprehensive analysis of recent works on multimodal deep learning from three perspectives: learning multimodal representations, fusing multimodal signals at various levels, and multimodal applications.

Multimodal Deep Learning Question Answering +5

From Caesar Cipher to Unsupervised Learning: A New Method for Classifier Parameter Estimation

no code implementations6 Jun 2019 Yu Liu, Li Deng, Jianshu Chen, Chang Wen Chen

To remove the need for the parallel training corpora has practical significance for real-world applications, and it is one of the main goals of unsupervised learning.

General Classification Machine Translation +1

Attentive Tensor Product Learning

no code implementations20 Feb 2018 Qiuyuan Huang, Li Deng, Dapeng Wu, Chang Liu, Xiaodong He

This paper proposes a new architecture - Attentive Tensor Product Learning (ATPL) - to represent grammatical structures in deep learning models.

Constituency Parsing Image Captioning +2

Now I Remember! Episodic Memory For Reinforcement Learning

no code implementations ICLR 2018 Ricky Loynd, Matthew Hausknecht, Lihong Li, Li Deng

Humans rely on episodic memory constantly, in remembering the name of someone they met 10 minutes ago, the plot of a movie as it unfolds, or where they parked the car.

reinforcement-learning

Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes

no code implementations NeurIPS 2017 Jianshu Chen, Chong Wang, Lin Xiao, Ji He, Lihong Li, Li Deng

In sequential decision making, it is often important and useful for end users to understand the underlying patterns or causes that lead to the corresponding decisions.

Decision Making Q-Learning +2

BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems

no code implementations15 Nov 2017 Zachary Lipton, Xiujun Li, Jianfeng Gao, Lihong Li, Faisal Ahmed, Li Deng

We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems.

Efficient Exploration Q-Learning +2

A Neural-Symbolic Approach to Design of CAPTCHA

no code implementations29 Oct 2017 Qiuyuan Huang, Paul Smolensky, Xiaodong He, Li Deng, Dapeng Wu

To address this, this paper promotes image/visual captioning based CAPTCHAs, which is robust against machine-learning-based attacks.

Image Captioning

Tensor Product Generation Networks for Deep NLP Modeling

2 code implementations NAACL 2018 Qiuyuan Huang, Paul Smolensky, Xiaodong He, Li Deng, Dapeng Wu

We present a new approach to the design of deep networks for natural language processing (NLP), based on the general technique of Tensor Product Representations (TPRs) for encoding and processing symbol structures in distributed neural networks.

StyleNet: Generating Attractive Visual Captions With Styles

no code implementations CVPR 2017 Chuang Gan, Zhe Gan, Xiaodong He, Jianfeng Gao, Li Deng

We propose a novel framework named StyleNet to address the task of generating attractive captions for images and videos with different styles.

Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension

2 code implementations EMNLP 2017 David Golub, Po-Sen Huang, Xiaodong He, Li Deng

We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network (SynNet).

Reading Comprehension Transfer Learning

Question-Answering with Grammatically-Interpretable Representations

no code implementations23 May 2017 Hamid Palangi, Paul Smolensky, Xiaodong He, Li Deng

In our application of TPRN, internal representations learned by end-to-end optimization in a deep neural network performing a textual question-answering (QA) task can be interpreted using basic concepts from linguistic theory.

Question Answering

Scaffolding Networks: Incremental Learning and Teaching Through Questioning

no code implementations28 Feb 2017 Asli Celikyilmaz, Li Deng, Lihong Li, Chong Wang

We introduce a new paradigm of learning for reasoning, understanding, and prediction, as well as the scaffolding network to implement this paradigm.

Incremental Learning

Unsupervised Sequence Classification using Sequential Output Statistics

no code implementations NeurIPS 2017 Yu Liu, Jianshu Chen, Li Deng

Although it is harder to optimize in its functional form, a stochastic primal-dual gradient method is developed to effectively solve the problem.

Classification General Classification

Sequence Modeling via Segmentations

2 code implementations ICML 2017 Chong Wang, Yining Wang, Po-Sen Huang, Abdel-rahman Mohamed, Dengyong Zhou, Li Deng

The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment is modeled using existing tools such as recurrent neural networks.

Speech Recognition Text Segmentation

Character-level Deep Conflation for Business Data Analytics

2 code implementations8 Feb 2017 Zhe Gan, P. D. Singh, Ameet Joshi, Xiaodong He, Jianshu Chen, Jianfeng Gao, Li Deng

Connecting different text attributes associated with the same entity (conflation) is important in business data analytics since it could help merge two different tables in a database to provide a more comprehensive profile of an entity.

End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager

1 code implementation3 Dec 2016 Xuesong Yang, Yun-Nung Chen, Dilek Hakkani-Tur, Paul Crook, Xiujun Li, Jianfeng Gao, Li Deng

Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance.

Natural Language Understanding

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

11 code implementations28 Nov 2016 Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang

The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering.

Machine Reading Comprehension Question Answering

Semantic Compositional Networks for Visual Captioning

1 code implementation CVPR 2017 Zhe Gan, Chuang Gan, Xiaodong He, Yunchen Pu, Kenneth Tran, Jianfeng Gao, Lawrence Carin, Li Deng

The degree to which each member of the ensemble is used to generate an image caption is tied to the image-dependent probability of the corresponding tag.

Image Captioning Semantic Composition +1

Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access

1 code implementation ACL 2017 Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng

In this paper, we address this limitation by replacing symbolic queries with an induced "soft" posterior distribution over the KB that indicates which entities the user is interested in.

reinforcement-learning

Bi-directional Attention with Agreement for Dependency Parsing

1 code implementation EMNLP 2016 Hao Cheng, Hao Fang, Xiaodong He, Jianfeng Gao, Li Deng

We develop a novel bi-directional attention model for dependency parsing, which learns to agree on headword predictions from the forward and backward parsing directions.

Dependency Parsing

Unsupervised Learning of Predictors from Unpaired Input-Output Samples

no code implementations15 Jun 2016 Jianshu Chen, Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng

In particular, we show that with regularization via a generative model, learning with the proposed unsupervised objective function converges to an optimal solution.

Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads

1 code implementation EMNLP 2016 Ji He, Mari Ostendorf, Xiaodong He, Jianshu Chen, Jianfeng Gao, Lihong Li, Li Deng

We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space.

reinforcement-learning

Basic Reasoning with Tensor Product Representations

no code implementations12 Jan 2016 Paul Smolensky, Moontae Lee, Xiaodong He, Wen-tau Yih, Jianfeng Gao, Li Deng

In this paper we present the initial development of a general theory for mapping inference in predicate logic to computation over Tensor Product Representations (TPRs; Smolensky (1990), Smolensky & Legendre (2006)).

Question Answering

Deep Reinforcement Learning with a Natural Language Action Space

3 code implementations ACL 2016 Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, Mari Ostendorf

This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games.

Q-Learning reinforcement-learning +1

Recurrent Reinforcement Learning: A Hybrid Approach

no code implementations10 Sep 2015 Xiujun Li, Lihong Li, Jianfeng Gao, Xiaodong He, Jianshu Chen, Li Deng, Ji He

Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states.

reinforcement-learning

Distributed Compressive Sensing: A Deep Learning Approach

no code implementations20 Aug 2015 Hamid Palangi, Rabab Ward, Li Deng

As the proposed method is a data driven method, it is only applicable when training data is available.

Compressive Sensing

End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture

1 code implementation NeurIPS 2015 Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, Li Deng

We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i. e., BP-sLDA), which maximizes the posterior probability of the prediction variable given the input document.

General Classification Topic Models

A Deep Embedding Model for Co-occurrence Learning

no code implementations11 Apr 2015 Yelong Shen, Ruoming Jin, Jianshu Chen, Xiaodong He, Jianfeng Gao, Li Deng

Co-occurrence Data is a common and important information source in many areas, such as the word co-occurrence in the sentences, friends co-occurrence in social networks and products co-occurrence in commercial transaction data, etc, which contains rich correlation and clustering information about the items.

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

7 code implementations20 Dec 2014 Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng

We consider learning representations of entities and relations in KBs using the neural-embedding approach.

Link Prediction

Learning Multi-Relational Semantics Using Neural-Embedding Models

no code implementations14 Nov 2014 Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, Li Deng

In this paper we present a unified framework for modeling multi-relational representations, scoring, and learning, and conduct an empirical study of several recent multi-relational embedding models under the framework.

Knowledge Base Completion

Learning Semantic Representations for the Phrase Translation Model

no code implementations28 Nov 2013 Jianfeng Gao, Xiaodong He, Wen-tau Yih, Li Deng

The results show that the new semantic-based phrase translation model significantly improves the performance of a state-of-the-art phrase-based statistical machine translation sys-tem, leading to a gain of 0. 7-1. 0 BLEU points.

Learning Semantic Representations Machine Translation +1

A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property

no code implementations24 Nov 2013 Jianshu Chen, Li Deng

We present an architecture of a recurrent neural network (RNN) with a fully-connected deep neural network (DNN) as its feature extractor.

Learning Input and Recurrent Weight Matrices in Echo State Networks

no code implementations13 Nov 2013 Hamid Palangi, Li Deng, Rabab K. Ward

In this paper, we devise a special technique that take advantage of this linearity in the output units of an ESN, to learn the input and recurrent matrices.

Learning deep structured semantic models for web search using clickthrough data

2 code implementations CIKM 2013 Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, Larry Heck

The proposed deep structured semantic models are discriminatively trained by maximizing the conditional likelihood of the clicked documents given a query using the clickthrough data.

Document Ranking

Learning with Recursive Perceptual Representations

no code implementations NeurIPS 2012 Oriol Vinyals, Yangqing Jia, Li Deng, Trevor Darrell

The use of random projections is key to our method, as we show in the experiments section, in which we observe a consistent improvement over previous --often more complicated-- methods on several vision and speech benchmarks.

Image Classification Object Recognition

Deep Neural Networks for Acoustic Modeling in Speech Recognition

no code implementations Signal Processing Magazine 2012 Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, Brian Kingsbury

Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input.

Frame Speech Recognition

Cannot find the paper you are looking for? You can Submit a new open access paper.