105 citations · 439 across the 24 of their papers we have counts for
25 papers
Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture Search
Keith G. Mills, Fred X. Han, Jialin Zhang +6
Neural architecture search automates neural network design and has achieved state-of-the-art results in many deep learning applications. While recent literature has focused on desi…
LNAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning
Keith G. Mills, Fred X. Han, Mohammad Salameh +6
Neural architecture search (NAS) has achieved remarkable results in deep neural network design. Differentiable architecture search converts the search over discrete architectures i…
Generative Adversarial Neural Architecture Search
Seyed Saeed Changiz Rezaei, Fred X. Han, Di Niu +5
Despite the empirical success of neural architecture search (NAS) in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to assess. In t…
QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications
Mingjun Zhao, Shengli Yan, Bang Liu +6
Query-based document summarization aims to extract or generate a summary of a document which directly answers or is relevant to the search query. It is an important technique that…
Neural Architecture Search For Keyword Spotting
Tong Mo, Yakun Yu, Mohammad Salameh +2
Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice. In this paper, we apply neural archit…
Reinforced Curriculum Learning on Pre-trained Neural Machine Translation Models
Mingjun Zhao, Haijiang Wu, Di Niu +1
The competitive performance of neural machine translation (NMT) critically relies on large amounts of training data. However, acquiring high-quality translation pairs requires expe…
GIANT: Scalable Creation of a Web-scale Ontology
Bang Liu, Weidong Guo, Di Niu +4
Understanding what online users may pay attention to is key to content recommendation and search services. These services will benefit from a highly structured and web-scale ontolo…
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
Bang Liu, Haojie Wei, Di Niu +2
The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, improves question-answering and machine rea…
Learning Privately over Distributed Features: An ADMM Sharing Approach
Yaochen Hu, Peng Liu, Linglong Kong +1
Distributed machine learning has been widely studied in order to handle exploding amount of data. In this paper, we study an important yet less visited distributed learning problem…
A User-Centered Concept Mining System for Query and Document Understanding at Tencent
Bang Liu, Weidong Guo, Di Niu +5
Concepts embody the knowledge of the world and facilitate the cognitive processes of human beings. Mining concepts from web documents and constructing the corresponding taxonomy ar…
Multiresolution Graph Attention Networks for Relevance Matching
Ting Zhang, Bang Liu, Di Niu +2
A large number of deep learning models have been proposed for the text matching problem, which is at the core of various typical natural language processing (NLP) tasks. However, e…
Learning to Generate Questions by Learning What not to Generate
Bang Liu, Mingjun Zhao, Di Niu +4
Automatic question generation is an important technique that can improve the training of question answering, help chatbots to start or continue a conversation with humans, and prov…
Stochastic Distributed Optimization for Machine Learning from Decentralized Features
Yaochen Hu, Di Niu, Jianming Yang +1
Distributed machine learning has been widely studied in the literature to scale up machine learning model training in the presence of an ever-increasing amount of data. We study di…
A Model Parallel Proximal Stochastic Gradient Algorithm for Partially Asynchronous Systems
Rui Zhu, Di Niu
Large models are prevalent in modern machine learning scenarios, including deep learning, recommender systems, etc., which can have millions or even billions of parameters. Paralle…
Android Malware Detection using Large-scale Network Representation Learning
Rui Zhu, Chenglin Li, Di Niu +2
With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, which calls for the development of advanced…
Android Malware Detection based on Factorization Machine
Chenglin Li, Keith Mills, Rui Zhu +3
As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications. Due to the potential for data theft mobile phone users fac…
House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis
Bang Liu, Borislav Mavrin, Di Niu +1
Online real-estate information systems such as Zillow and Trulia have gained increasing popularity in recent years. One important feature offered by these systems is the online hom…
Recover Fine-Grained Spatial Data from Coarse Aggregation
Bang Liu, Borislav Mavrin, Linglong Kong +1
In this paper, we study a new type of spatial sparse recovery problem, that is to infer the fine-grained spatial distribution of certain density data in a region only based on the…
Growing Story Forest Online from Massive Breaking News
Bang Liu, Di Niu, Kunfeng Lai +2
We describe our experience of implementing a news content organization system at Tencent that discovers events from vast streams of breaking news and evolves news story structures…
Matching Natural Language Sentences with Hierarchical Sentence Factorization
Bang Liu, Ting Zhang, Fred X. Han +3
Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Depending o…
A Block-wise, Asynchronous and Distributed ADMM Algorithm for General Form Consensus Optimization
Rui Zhu, Di Niu, Zongpeng Li
Many machine learning models, including those with non-smooth regularizers, can be formulated as consensus optimization problems, which can be solved by the alternating direction m…
Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth Optimization
Rui Zhu, Di Niu, Zongpeng Li
We study stochastic algorithms for solving nonconvex optimization problems with a convex yet possibly nonsmooth regularizer, which find wide applications in many practical machine…
Matching Article Pairs with Graphical Decomposition and Convolutions
Bang Liu, Di Niu, Haojie Wei +4
Identifying the relationship between two articles, e.g., whether two articles published from different sources describe the same breaking news, is critical to many document underst…
Advanced Algorithms for Penalized Quantile and Composite Quantile Regression
Matthew Pietrosanu, Jueyu Gao, Linglong Kong +2
In this paper, we discuss a family of robust, high-dimensional regression models for quantile and composite quantile regression, both with and without an adaptive lasso penalty for…