Hui Jiang

TCS Journal 2026 Journal Article

A quantum game designed for property partitioning with implementation on superconducting quantum processors

• Hui Jiang
• Jianling Fu
• Ming Xu
• Ji Guan
• Shenggang Ying

This paper considers the partitioning property problem, which is a game between two players that determines the winner through communications. Our quantum solution seeks three goals — fairness, binding, and sealing. The latter two demonstrate the superiority of quantum computing over classical counterpart. Inspired by the BB84 protocol, we first achieve fairness by independent quantum measures. Binding or sealing follows, not both, which results in a quantum bit escrow. We implement the game respectively in a simple and in a parametric setting with the quantum programming language isQ on a quantum simulator, whose results are the benchmarks to those on quantum processors. From the experimental results, the optimal policies are synthesized for the two players in the game. Besides, an interactive game named PPT is designed to enhance the enjoyment of quantum computing. Finally, we run the simple game on two superconducting quantum processors ARCLIGHT and Quafu, by which the experimental results validate the benchmarks well. With quantum error correction in the NISQ era, the proposed quantum protocols are believed to be promising in future social and economic fields.

AAAI Conference 2026 Conference Paper

MUSE: Multi-Scale Dense Self-Distillation for Nucleus Detection and Classification

• Zijiang Yang
• Hanqing Chao
• Bokai Zhao
• Yelin Yang
• Yunshuo Zhang
• Dongmei Fu
• Junping Zhang
• Le Lu

Nucleus detection and classification (NDC) in histopathology analysis is a fundamental task that underpins a wide range of high-level pathology applications. However, existing methods heavily rely on labor-intensive nucleus-level annotations and struggle to fully exploit large-scale unlabeled data for learning discriminative nucleus representations. In this work, we propose MUSE (MUlti-scale denSE self-distillation), a novel self-supervised learning method tailored for NDC. At its core is NuLo (Nucleus-based Local self-distillation), a coordinate-guided mechanism that enables flexible local self-distillation based on predicted nucleus positions. By removing the need for strict spatial alignment between augmented views, NuLo allows critical cross-scale alignment, thus unlocking the capacity of models for fine-grained nucleus-level representation. To support MUSE, we design a simple yet effective encoder-decoder architecture and a large field-of-view semi-supervised fine-tuning strategy that together maximize the value of unlabeled pathology images. Extensive experiments on three widely used benchmarks demonstrate that MUSE effectively addresses the core challenges of histopathological NDC. The resulting models not only surpass state-of-the-art supervised baselines but also outperform generic pathology foundation models.

AAAI Conference 2026 Conference Paper

TOP-RL: Task-Optimized Progressive Token Pruning with Reinforcement Learning for Vision Language Models

• Hengyi Wang
• Weiying Xie
• Hui Jiang
• Yaotao Wei
• Kai Jiang
• Mingxiang Cao
• Chenhe Hao
• Leyuan Fang

In recent years, Large Vision-Language Models (LVLMs) have significantly advanced multimodal tasks. However, their inference requires intensive processing of numerous visual tokens and incurs substantial computational overhead. Existing methods typically compress visual tokens either at the input stage or in early model layers, ignoring variations across tasks and depths. To address these limitations, we introduce TOP-RL, a Task-Optimized Progressive token pruning framework based on Reinforcement Learning. TOP-RL formulates visual token pruning as a multi-stage Markov Decision Process (MDP). It employs an agent trained with dense and fine-grained reward signals to progressively generate differentiable binary masks. This enables TOP-RL to adaptively select crucial visual tokens tailored to each task, effectively balancing accuracy and computational efficiency. Extensive experiments on leading multimodal datasets and advanced LVLMs validate that TOP-RL effectively learns task-optimized pruning policies, significantly boosting inference efficiency while preserving robust performance. For instance, LLaVA-NeXT equipped with TOP-RL achieves a 1.9x speedup in inference time and a 9.3x reduction in FLOPs, with 96% performance preserved.

EAAI Journal 2024 Journal Article

Monitoring of simultaneous saccharification and fermentation of ethanol by multi-source data deep fusion strategy based on near-infrared spectra and electronic nose signals

• Hui Jiang
• Jihong Deng
• Quansheng Chen

IJCAI Conference 2021 Conference Paper

A Structure Self-Aware Model for Discourse Parsing on Multi-Party Dialogues

• Ante Wang
• Linfeng Song
• Hui Jiang
• Shaopeng Lai
• Junfeng Yao
• Min Zhang
• Jinsong Su

Conversational discourse structures aim to describe how a dialogue is organized, thus they are helpful for dialogue understanding and response generation. This paper focuses on predicting discourse dependency structures for multi-party dialogues. Previous work adopts incremental methods that take the features from the already predicted discourse relations to help generate the next one. Although the inter-correlations among predictions considered, we find that the error propagation is also very serious and hurts the overall performance. To alleviate error propagation, we propose a Structure Self-Aware (SSA) model, which adopts a novel edge-centric Graph Neural Network (GNN) to update the information between each Elementary Discourse Unit (EDU) pair layer by layer, so that expressive representations can be learned without historical predictions. In addition, we take auxiliary training signals (e. g. structure distillation) for better representation learning. Our model achieves the new state-of-the-art performances on two conversational discourse parsing benchmarks, largely outperforming the previous methods.

AIJ Journal 2021 Journal Article

Enhanced aspect-based sentiment analysis models with progressive self-supervised attention learning

• Jinsong Su
• Jialong Tang
• Hui Jiang
• Ziyao Lu
• Yubin Ge
• Linfeng Song
• Deyi Xiong
• Le Sun

In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction. However, such a mechanism suffers from one drawback: only a few frequent words with sentiment polarities are tended to be taken into consideration for final sentiment decision while abundant infrequent sentiment words are ignored by models. To deal with this issue, we propose a progressive self-supervised attention learning approach for attentional ABSA models. In this approach, we iteratively perform sentiment prediction on all training instances, and continually learn useful attention supervision information in the meantime. During training, at each iteration, context words with the highest impact on sentiment prediction, identified based on their attention weights or gradients, are extracted as words with active/misleading influence on the correct/incorrect prediction for each instance. Words extracted in this way are masked for subsequent iterations. To exploit these extracted words for refining ABSA models, we augment the conventional training objective with a regularization term that encourages ABSA models to not only take full advantage of the extracted active context words but also decrease the weights of those misleading words. We integrate the proposed approach into three state-of-the-art neural ABSA models. Experiment results and in-depth analyses show that our approach yields better attention results and significantly enhances the performance of all three models. We release the source code and trained models at https: //github. com/DeepLearnXMU/PSSAttention.

JBHI Journal 2020 Journal Article

miRTMC: A miRNA Target Prediction Method Based on Matrix Completion Algorithm

• Hui Jiang
• Mengyun Yang
• Xiang Chen
• Min Li
• Yaohang Li
• Jianxin Wang

microRNAs (miRNAs) are small non-coding RNAs which modulate the stability of gene targets and their rates of translation into proteins at transcriptional level and post-transcriptional level. miRNA dysfunctions can lead to human diseases because of dysregulation of their targets. Correct miRNA target prediction will lead to better understanding of the mechanisms of human diseases and provide hints on curing them. In recent years, computational miRNA target prediction methods have been proposed according to the interaction rules between miRNAs and targets. However, these methods suffer from high false positive rates due to the complicated relationship between miRNAs and their targets. The rapidly growing number of experimentally validated miRNA targets enables predicting miRNA targets with high precision via accurate data analysis. Taking advantage of these known miRNA targets, a novel recommendation system model (miRTMC) for miRNA target prediction is established using a new matrix completion algorithm. In miRTMC, a heterogeneous network is constructed by integrating the miRNA similarity network, the gene similarity network, and the miRNA-gene interaction network. Our assumption is that the latent factors determining whether a gene is the target of miRNA or not are highly correlated, i. e. , the adjacency matrix of the heterogeneous network is low-rank, which is then completed by using a nuclear norm regularized linear least squares model under non-negative constraints. Alternating direction method of multipliers (ADMM) is adopted to numerically solve the matrix completion problem. Our results show that miRTMC outperforms the competing methods in terms of various evaluation metrics. Our software package is available at https://github.com/hjiangcsu/miRTMC.

IJCAI Conference 2017 Conference Paper

Cause-Effect Knowledge Acquisition and Neural Association Model for Solving A Set of Winograd Schema Problems

• Quan Liu
• Hui Jiang
• Andrew Evdokimov
• Zhen-Hua Ling
• Xiaodan Zhu
• Si Wei
• Yu Hu

This paper focuses on the investigations in Winograd Schema (WS), a challenging problem which has been proposed for measuring progress in commonsense reasoning. Due to the lack of commonsense knowledge and training data, very little work has been found on the WS problems in recent years. Actually, there is no shortcut to solve this problem except to collect more commonsense knowledge and design suitable models. Therefore, this paper addresses a set of WS problems by proposing a knowledge acquisition method and a general neural association model. To avoid the sparseness issue, the knowledge we aim to collect is the cause-effect relationships between thousands of commonly used words. The knowledge acquisition method supports us to extract hundreds of thousands of cause-effect pairs from large text corpus automatically. Meanwhile, a neural association model (NAM) is proposed to encode the association relationships between any two discrete events. Based on the extracted knowledge and the NAM models, in this paper, we successfully build a system for solving WS problems from scratch and achieve 70. 0% accuracy. Most importantly, this paper provides a flexible framework to solve WS problems based on event association and neural network methods.

IJCAI Conference 2016 Conference Paper

Distraction-Based Neural Networks for Modeling Document

• Qian Chen
• Xiaodan Zhu
• ZhenHua Ling
• Si Wei
• Hui Jiang

Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences. Whether and how such an approach can be extended to help model larger spans of text, e. g. , documents, is intriguing, and further investigation would still be desirable. This paper aims to enhance neural network models for such a purpose. A typical problem of document-level modeling is automatic summarization, which aims to model documents in order to generate summaries. In this paper, we propose neural models to train computers not just to pay attention to specific regions and content of input documents with attention models, but also distract them to traverse between different content of a document so as to better grasp the overall meaning for summarization. Without engineering any features, we train the models on two large datasets. The models achieve the state-of-the-art performance, and they significantly benefit from the distraction modeling, particularly when input documents are long.

JMLR Journal 2016 Journal Article

Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks

• Shiliang Zhang
• Hui Jiang
• Lirong Dai

In this paper, we propose a novel model for high-dimensional data, called the Hybrid Orthogonal Projection and Estimation (HOPE) model, which combines a linear orthogonal projection and a finite mixture model under a unified generative modeling framework. The HOPE model itself can be learned unsupervised from unlabelled data based on the maximum likelihood estimation as well as discriminatively from labelled data. More interestingly, we have shown the proposed HOPE models are closely related to neural networks (NNs) in a sense that each hidden layer can be reformulated as a HOPE model. As a result, the HOPE framework can be used as a novel tool to probe why and how NNs work, more importantly, to learn NNs in either supervised or unsupervised ways. In this work, we have investigated the HOPE framework to learn NNs for several standard tasks, including image recognition on MNIST and speech recognition on TIMIT. Experimental results have shown that the HOPE framework yields significant performance gains over the current state-of-the-art methods in various types of NN learning problems, including unsupervised feature learning, supervised or semi-supervised learning. [abs] [ pdf ][ bib ] &copy JMLR 2016. ( edit, beta )

UAI Conference 2015 Conference Paper

Annealed Gradient Descent for Deep Learning

• Hengyue Pan
• Hui Jiang

Stochastic gradient descent (SGD) has been regarded as a successful optimization algorithm in machine learning. In this paper, we propose a novel annealed gradient descent (AGD) method for non-convex optimization in deep learning. AGD optimizes a sequence of gradually improved smoother mosaic functions that approximate the original non-convex objective function according to an annealing schedule during the optimization process. We present a theoretical analysis on its convergence properties and learning speed. The proposed AGD algorithm is applied to learning deep neural networks (DNNs) for image recognition on MNIST and speech recognition on Switchboard. Experimental results have shown that AGD can yield comparable performance as SGD but it can significantly expedite training of DNNs in big data sets (by about 40% faster).