1 code implementation • 20 Mar 2024 • Keith G. Mills, Fred X. Han, Mohammad Salameh, Shengyao Lu, Chunhua Zhou, Jiao He, Fengyu Sun, Di Niu
Neural Architecture Search is a costly practice.
no code implementations • 12 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.
no code implementations • 17 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.
no code implementations • 26 Jan 2024 • Amirhosein Ghasemabadi, Mohammad Salameh, Muhammad Kamran Janjua, Chunhua Zhou, Fengyu Sun, Di Niu
Image restoration tasks traditionally rely on convolutional neural networks.
Ranked #1 on Image Denoising on SIDD
1 code implementation • 26 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.
no code implementations • 25 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.
1 code implementation • 15 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.
no code implementations • 14 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.
no code implementations • 27 Jun 2023 • Yakun Yu, Mingjun Zhao, Shi-ang Qi, Feiran Sun, Baoxun Wang, Weidong Guo, Xiaoli Wang, Lei Yang, Di Niu
Multimodal Sentiment Analysis leverages multimodal signals to detect the sentiment of a speaker.
no code implementations • 25 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.
no code implementations • 20 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.
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.
1 code implementation • 20 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.
3 code implementations • 31 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
1 code implementation • 5 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.
no code implementations • 21 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.
1 code implementation • 30 Nov 2022 • Keith G. Mills, Di Niu, Mohammad Salameh, Weichen Qiu, Fred X. Han, Puyuan Liu, Jialin Zhang, Wei Lu, Shangling Jui
Evaluating neural network performance is critical to deep neural network design but a costly procedure.
1 code implementation • 30 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.
1 code implementation • 22 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.
no code implementations • 20 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.
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.
1 code implementation • 19 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.
no code implementations • 13 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.
no code implementations • 29 Sep 2021 • Fred X. Han, Fabian Chudak, Keith G Mills, Mohammad Salameh, Parsa Riahi, Jialin Zhang, Wei Lu, Shangling Jui, Di Niu
Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS).
no code implementations • 25 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.
1 code implementation • 25 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.
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.
no code implementations • 31 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.
1 code implementation • 31 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).
1 code implementation • 31 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.
no code implementations • 19 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.
no code implementations • 1 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.
no code implementations • 27 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.
no code implementations • 1 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)
no code implementations • 13 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.
1 code implementation • 5 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.
2 code implementations • 27 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.
no code implementations • 17 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.
no code implementations • 21 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.
no code implementations • 27 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.
no code implementations • 27 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.
no code implementations • 16 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.
no code implementations • 19 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.
no code implementations • 13 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
no code implementations • 30 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
no code implementations • 1 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.
1 code implementation • 1 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.
Ranked #3 on Information Threading on NewSHead
no code implementations • 24 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).
no code implementations • 24 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.
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.
no code implementations • 7 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.