Search Results for author: Di Niu

Found 51 papers, 19 papers with code

SIFiD: Reassess Summary Factual Inconsistency Detection with LLM

no code implementations12 Mar 2024 Jiuding Yang, Hui Liu, Weidong Guo, Zhuwei Rao, Yu Xu, Di Niu

Ensuring factual consistency between the summary and the original document is paramount in summarization tasks.

Natural Language Inference Semantic Similarity +1

Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models

no code implementations17 Feb 2024 Sijia Chen, Baochun Li, Di Niu

The reasoning performance of Large Language Models (LLMs) on a wide range of problems critically relies on chain-of-thought prompting, which involves providing a few chain of thought demonstrations as exemplars in prompts.

GOAt: Explaining Graph Neural Networks via Graph Output Attribution

1 code implementation26 Jan 2024 Shengyao Lu, Keith G. Mills, Jiao He, Bang Liu, Di Niu

Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability.

Attribute Decision Making

Instruction Fusion: Advancing Prompt Evolution through Hybridization

no code implementations25 Dec 2023 Weidong Guo, Jiuding Yang, Kaitong Yang, Xiangyang Li, Zhuwei Rao, Yu Xu, Di Niu

The fine-tuning of Large Language Models (LLMs) specialized in code generation has seen notable advancements through the use of open-domain coding queries.

Code Generation

AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness

1 code implementation15 Sep 2023 Liyao Jiang, Chenglin Li, Haolan Chen, Xiaodong Gao, Xinwang Zhong, Yang Qiu, Shani Ye, Di Niu

Online advertisements are important elements in e-commerce sites, social media platforms, and search engines.

iHAS: Instance-wise Hierarchical Architecture Search for Deep Learning Recommendation Models

no code implementations14 Sep 2023 Yakun Yu, Shi-ang Qi, Jiuding Yang, Liyao Jiang, Di Niu

The searching stage identifies optimal instance-wise embedding dimensions across different field features via carefully designed Bernoulli gates with stochastic selection and regularizers.

Clustering feature selection +2

TCR: Short Video Title Generation and Cover Selection with Attention Refinement

no code implementations25 Apr 2023 Yakun Yu, Jiuding Yang, Weidong Guo, Hui Liu, Yu Xu, Di Niu

In this paper, we first collect and present a real-world dataset named Short Video Title Generation (SVTG) that contains videos with appealing titles and covers.

Video Captioning

LA3: Efficient Label-Aware AutoAugment

1 code implementation20 Apr 2023 Mingjun Zhao, Shan Lu, Zixuan Wang, Xiaoli Wang, Di Niu

Automated augmentation is an emerging and effective technique to search for data augmentation policies to improve generalizability of deep neural network training.

Bayesian Optimization Data Augmentation

Search-Map-Search: A Frame Selection Paradigm for Action Recognition

no code implementations CVPR 2023 Mingjun Zhao, Yakun Yu, Xiaoli Wang, Lei Yang, Di Niu

To overcome the limitations of existing methods, we propose a Search-Map-Search learning paradigm which combines the advantages of heuristic search and supervised learning to select the best combination of frames from a video as one entity.

Action Recognition Video Understanding

CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering

no code implementations20 Apr 2023 Mingjun Zhao, Mengzhen Wang, Yinglong Ma, Di Niu, Haijiang Wu

To address this issue, we propose CEIL, a novel Classification-Enhanced Iterative Learning framework for short text clustering, which aims at generally promoting the clustering performance by introducing a classification objective to iteratively improve feature representations.

Clustering Deep Clustering +3

GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation

3 code implementations31 Mar 2023 Jian Ma, Mingjun Zhao, Chen Chen, Ruichen Wang, Di Niu, Haonan Lu, Xiaodong Lin

Recent breakthroughs in the field of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user instructions. Although the synthesis performance is fascinating, one significant limitation of current image generation models is their insufficient ability to generate text coherently within images, particularly for complex glyph structures like Chinese characters.

Optical Character Recognition (OCR) Text-to-Image Generation

Reparameterization through Spatial Gradient Scaling

1 code implementation5 Mar 2023 Alexander Detkov, Mohammad Salameh, Muhammad Fetrat Qharabagh, Jialin Zhang, Wei Lui, Shangling Jui, Di Niu

Reparameterization aims to improve the generalization of deep neural networks by transforming convolutional layers into equivalent multi-branched structures during training.

A General-Purpose Transferable Predictor for Neural Architecture Search

no code implementations21 Feb 2023 Fred X. Han, Keith G. Mills, Fabian Chudak, Parsa Riahi, Mohammad Salameh, Jialin Zhang, Wei Lu, Shangling Jui, Di Niu

In this paper, we propose a general-purpose neural predictor for NAS that can transfer across search spaces, by representing any given candidate Convolutional Neural Network (CNN) with a Computation Graph (CG) that consists of primitive operators.

Contrastive Learning Graph Representation Learning +1

GENNAPE: Towards Generalized Neural Architecture Performance Estimators

1 code implementation30 Nov 2022 Keith G. Mills, Fred X. Han, Jialin Zhang, Fabian Chudak, Ali Safari Mamaghani, Mohammad Salameh, Wei Lu, Shangling Jui, Di Niu

In this paper, we propose GENNAPE, a Generalized Neural Architecture Performance Estimator, which is pretrained on open neural architecture benchmarks, and aims to generalize to completely unseen architectures through combined innovations in network representation, contrastive pretraining, and fuzzy clustering-based predictor ensemble.

Contrastive Learning Image Classification +1

One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation

1 code implementation22 Nov 2022 Chenglin Li, Yuanzhen Xie, Chenyun Yu, Bo Hu, Zang Li, Guoqiang Shu, XiaoHu Qie, Di Niu

CAT-ART boosts the recommendation performance in any target domain through the combined use of the learned global user representation and knowledge transferred from other domains, in addition to the original user embedding in the target domain.

Multi-Domain Recommender Systems Recommendation Systems +1

Mulco: Recognizing Chinese Nested Named Entities Through Multiple Scopes

no code implementations20 Nov 2022 Jiuding Yang, Jinwen Luo, Weidong Guo, Jerry Chen, Di Niu, Yu Xu

Nested Named Entity Recognition (NNER) has been a long-term challenge to researchers as an important sub-area of Named Entity Recognition.

named-entity-recognition Named Entity Recognition +1

R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning

1 code implementation ICLR 2022 Shengyao Lu, Bang Liu, Keith G. Mills, Shangling Jui, Di Niu

Systematicity, i. e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence.

Relation Relational Reasoning

RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation

1 code implementation19 Nov 2021 Chenglin Li, Mingjun Zhao, Huanming Zhang, Chenyun Yu, Lei Cheng, Guoqiang Shu, Beibei Kong, Di Niu

The learned GUR captures the overall preferences and characteristics of a user and thus can be used to augment the behavior data and improve recommendations in any single domain in which the user is involved.

Sequential Recommendation

TAG: Toward Accurate Social Media Content Tagging with a Concept Graph

no code implementations13 Oct 2021 Jiuding Yang, Weidong Guo, Bang Liu, Yakun Yu, Chaoyue Wang, Jinwen Luo, Linglong Kong, Di Niu, Zhen Wen

Although conceptualization has been widely studied in semantics and knowledge representation, it is still challenging to find the most accurate concept phrases to characterize the main idea of a text snippet on the fast-growing social media.

Dependency Parsing Graph Matching +4

L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning

no code implementations25 Sep 2021 Keith G. Mills, Fred X. Han, Mohammad Salameh, SEYED SAEED CHANGIZ REZAEI, Linglong Kong, Wei Lu, Shuo Lian, Shangling Jui, Di Niu

In this paper, we propose L$^{2}$NAS, which learns to intelligently optimize and update architecture hyperparameters via an actor neural network based on the distribution of high-performing architectures in the search history.

Hyperparameter Optimization Neural Architecture Search +2

Profiling Neural Blocks and Design Spaces for Mobile Neural Architecture Search

1 code implementation25 Sep 2021 Keith G. Mills, Fred X. Han, Jialin Zhang, SEYED SAEED CHANGIZ REZAEI, Fabian Chudak, Wei Lu, Shuo Lian, Shangling Jui, Di Niu

Neural architecture search automates neural network design and has achieved state-of-the-art results in many deep learning applications.

Neural Architecture Search

LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization

1 code implementation Findings (ACL) 2021 Weidong Guo, Mingjun Zhao, Lusheng Zhang, Di Niu, Jinwen Luo, Zhenhua Liu, Zhenyang Li, Jianbo Tang

Language model pre-training based on large corpora has achieved tremendous success in terms of constructing enriched contextual representations and has led to significant performance gains on a diverse range of Natural Language Understanding (NLU) tasks.

Language Modelling Natural Language Understanding

Meta-HAR: Federated Representation Learning for Human Activity Recognition

1 code implementation31 May 2021 Chenglin Li, Di Niu, Bei Jiang, Xiao Zuo, Jianming Yang

However, the effectiveness of federated learning for HAR is affected by the fact that each user has different activity types and even a different signal distribution for the same activity type.

Activity Prediction Federated Learning +3

Similarity Embedding Networks for Robust Human Activity Recognition

no code implementations31 May 2021 Chenglin Li, Carrie Lu Tong, Di Niu, Bei Jiang, Xiao Zuo, Lei Cheng, Jian Xiong, Jianming Yang

Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently.

Human Activity Recognition

Verdi: Quality Estimation and Error Detection for Bilingual Corpora

1 code implementation31 May 2021 Mingjun Zhao, Haijiang Wu, Di Niu, Zixuan Wang, Xiaoli Wang

Verdi adopts two word predictors to enable diverse features to be extracted from a pair of sentences for subsequent quality estimation, including a transformer-based neural machine translation (NMT) model and a pre-trained cross-lingual language model (XLM).

Language Modelling Machine Translation +3

Generative Adversarial Neural Architecture Search

no code implementations19 May 2021 SEYED SAEED CHANGIZ REZAEI, Fred X. Han, Di Niu, Mohammad Salameh, Keith Mills, Shuo Lian, Wei Lu, Shangling Jui

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.

Neural Architecture Search

Generative Adversarial Neural Architecture Search with Importance Sampling

no code implementations1 Jan 2021 SEYED SAEED CHANGIZ REZAEI, Fred X. Han, Di Niu, Mohammad Salameh, Keith G Mills, Shangling Jui

Despite the empirical success of neural architecture search (NAS) algorithms in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to be assessed.

Neural Architecture Search

QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications

no code implementations27 Oct 2020 Mingjun Zhao, ShengLi Yan, Bang Liu, Xinwang Zhong, Qian Hao, Haolan Chen, Di Niu, Bowei Long, Weidong Guo

In this paper, we present QBSUM, a high-quality large-scale dataset consisting of 49, 000+ data samples for the task of Chinese query-based document summarization.

Document Summarization Machine Reading Comprehension

Neural Architecture Search For Keyword Spotting

no code implementations1 Sep 2020 Tong Mo, Yakun Yu, Mohammad Salameh, Di Niu, Shangling Jui

Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice.

 Ranked #1 on Keyword Spotting on Google Speech Commands (Google Speech Commands V1 6 metric)

Keyword Spotting Neural Architecture Search

Reinforced Curriculum Learning on Pre-trained Neural Machine Translation Models

no code implementations13 Apr 2020 Mingjun Zhao, Haijiang Wu, Di Niu, Xiaoli Wang

Specifically, we propose a data selection framework based on Deterministic Actor-Critic, in which a critic network predicts the expected change of model performance due to a certain sample, while an actor network learns to select the best sample out of a random batch of samples presented to it.

Machine Translation NMT +1

GIANT: Scalable Creation of a Web-scale Ontology

1 code implementation5 Apr 2020 Bang Liu, Weidong Guo, Di Niu, Jinwen Luo, Chaoyue Wang, Zhen Wen, Yu Xu

These services will benefit from a highly structured and web-scale ontology of entities, concepts, events, topics and categories.

News Recommendation

Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

2 code implementations27 Jan 2020 Bang Liu, Haojie Wei, Di Niu, Haolan Chen, Yancheng He

In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions.

Answer Generation Chatbot +5

Learning Privately over Distributed Features: An ADMM Sharing Approach

no code implementations17 Jul 2019 Yaochen Hu, Peng Liu, Linglong Kong, Di Niu

Distributed machine learning has been widely studied in order to handle exploding amount of data.

A User-Centered Concept Mining System for Query and Document Understanding at Tencent

no code implementations21 May 2019 Bang Liu, Weidong Guo, Di Niu, Chaoyue Wang, Shunnan Xu, Jinghong Lin, Kunfeng Lai, Yu Xu

We further present our techniques to tag documents with user-centered concepts and to construct a topic-concept-instance taxonomy, which has helped to improve search as well as news feeds recommendation in Tencent QQ Browser.

document understanding TAG

Multiresolution Graph Attention Networks for Relevance Matching

no code implementations27 Feb 2019 Ting Zhang, Bang Liu, Di Niu, Kunfeng Lai, Yu Xu

In this paper, we are especially interested in relevance matching between a piece of short text and a long document, which is critical to problems like query-document matching in information retrieval and web searching.

Graph Attention Information Retrieval +4

Learning to Generate Questions by Learning What not to Generate

no code implementations27 Feb 2019 Bang Liu, Mingjun Zhao, Di Niu, Kunfeng Lai, Yancheng He, Haojie Wei, Yu Xu

In CGC-QG, we design a multi-task labeling strategy to identify whether a question word should be copied from the input passage or be generated instead, guiding the model to learn the accurate boundaries between copying and generation.

Multi-Task Learning Question Answering +2

Stochastic Distributed Optimization for Machine Learning from Decentralized Features

no code implementations16 Dec 2018 Yaochen Hu, Di Niu, Jianming Yang, Shengping Zhou

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.

BIG-bench Machine Learning Distributed Optimization

A Model Parallel Proximal Stochastic Gradient Algorithm for Partially Asynchronous Systems

no code implementations19 Oct 2018 Rui Zhu, Di Niu

We prove that AsyB-ProxSGD achieves a convergence rate of $O(1/\sqrt{K})$ to stationary points for nonconvex problems under \emph{constant} minibatch sizes, where $K$ is the total number of block updates.

BIG-bench Machine Learning Recommendation Systems

Android Malware Detection using Large-scale Network Representation Learning

no code implementations13 Jun 2018 Rui Zhu, Chenglin Li, Di Niu, Hongwen Zhang, Husam Kinawi

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 and effective malware detection approaches.

Cryptography and Security

Android Malware Detection based on Factorization Machine

no code implementations30 May 2018 Chenglin Li, Keith Mills, Rui Zhu, Di Niu, Hongwen Zhang, Husam Kinawi

As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications.

Cryptography and Security

Growing Story Forest Online from Massive Breaking News

1 code implementation1 Mar 2018 Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu

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 in an online fashion.

Graph Generation Information Threading

Matching Natural Language Sentences with Hierarchical Sentence Factorization

no code implementations1 Mar 2018 Bang Liu, Ting Zhang, Fred X. Han, Di Niu, Kunfeng Lai, Yu Xu

The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies.

Paraphrase Identification Sentence

A Block-wise, Asynchronous and Distributed ADMM Algorithm for General Form Consensus Optimization

no code implementations24 Feb 2018 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 method of multipliers (ADMM).

Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth Optimization

no code implementations24 Feb 2018 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 learning applications.

Matching Article Pairs with Graphical Decomposition and Convolutions

1 code implementation ACL 2019 Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu

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 understanding tasks.

document understanding Question Answering +2

Expectile Matrix Factorization for Skewed Data Analysis

no code implementations7 Jun 2016 Rui Zhu, Di Niu, Linglong Kong, Zongpeng Li

Matrix factorization is a popular approach to solving matrix estimation problems based on partial observations.

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