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Guoyin Wang

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23 papers

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EAAI Journal 2026 Journal Article

Adaptive weighted disentangling variational autoencoder with fine-grained feedback

Zhenyao Yu
Yue Liu
Zitu Liu
Zhengwei Yang
Yike Guo
Qun Liu
Guoyin Wang

In the realm of machine learning, the challenge of extracting meaningful low-dimensional structures from high-dimensional data is paramount. Deep learning techniques, particularly Variational Autoencoders (VAE), have proven adept at this task yet often lack semantic interpretability in their representations. To address this issue, disentangled representation learning has been proposed and utilized to learn interpretable representations from data. However, existing methods often rely on heuristic constraints that are manually set and fixed, hindering adaptability and optimization. In this paper, the Adaptive Weighted Disentangling Variational Autoencoder (AwingVAE) is proposed, which introduces a feedback mechanism into the VAE framework, allowing for dynamic parameter optimization and adaptive dimension weighting based on Kullback-Leibler divergence. This feedback mechanism effectively enhances the model's disentanglement, generation, and robustness, with maximum gains of 17. 4%, 7. 565, and 15. 8%, respectively. The proposed method thus offers a new perspective on VAE utilization for representation learning, with extensive evaluations on benchmark datasets supporting its effectiveness. Implementation available at https: //github. com/YuSanTu/AwingVAE.

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EAAI Journal 2026 Journal Article

Granular-ball based robust representation learning for social recommendation

Xiaofei Zhu
Shiyan Wu
Li Liu
Shuyin Xia
Yi Wang
Guoyin Wang

Social recommendation systems seek to leverage social relationships to mitigate data sparsity and cold-start issues by augmenting user–item interactions. However, existing methods encounter two critical limitations: (1) They predominantly model user–item interactions at a fine-grained granular level of user/item nodes, neglecting the potential coarse-grained collaborative patterns; and (2) They usually suppress noisy edges in social graphs from a single granular perspective, failing to adjust the denoising granularity according to the actual strength of relationships between users. To address these challenges, we propose GBRSR, a novel Granular-ball based Robust Representation Learning framework. Inspired by the “Global-first” cognitive principle, Granular-ball Computing (GBC), which represents data as granular-ball units with geometric significance, has garnered significant attention due to its outstanding performance in many fields. We leverage GBC theory for representation distillation, transferring coarse-grained knowledge to enhance fine-grained node-level representations. In addition, we employs a granular-ball based structure denoising strategy to prune noisy user relationships, while simultaneously alleviating noise in user representations through a diffusion process. Extensive experiments on three real-world benchmark datasets validate the superiority of GBRSR in recommendation accuracy and robustness, particularly under noisy and sparse conditions.

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JBHI Journal 2025 Journal Article

A Rule-Guided Community Detection Method for Identifying Subpopulations in Medical Data

Hanyue Liu
Hong Yu
Hao Wu
Guoyin Wang

Precisely identifying and explaining subpopulations in heterogeneous populations is essential to understanding the disease subtype. Using community detection to identify subpopulations is a promising way. However, there remains an issue in the existing community detection: Current methods for identifying subpopulations in medical data rely solely on separate attribute values, ignoring the important association rules between attribute values. Association rules are crucial in medical diagnosis to determine disease subtypes. Thus, We propose a rule-guided community detection (RGCD) method for precisely identifying homogeneous subpopulations. Specifically, the RGCD incorporates association rules into the original network, thereby constructing an augmented network. It proves that decomposing the embedding vectors obtained from biased random walks on the augmented network is equivalent to decomposing the transition probability matrix. Based on this proof, we enhance the transition probability matrix through rule-guided biased random walks, resulting in the rule-augmented matrix. By performing matrix decomposition and clustering on this matrix, we achieve precise identification of subpopulations. To the best of our knowledge, this is the first work that introduces the incorporation of association rules into community detection. Extensive experiments on 10 real-world datasets from medical fields fully show that the RGCD is more competitive than six state-of-the-art community detection methods. The weighted F1 of RGCD increases by up to 22. 62%, compared to the best existing community detection methods. Furthermore, We provide a qualitative depiction of the subpopulations obtained through RGCD and acquire medically significant insights.

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JBHI Journal 2025 Journal Article

Cross-Interaction of Chinese Characters Structures and Boundary Features for Improving Clinical Named Entity Recognition

Ye Wang
Qi Wei
Hong Yu
Guoyin Wang
Chunmeng Shi
Dajiang Lei

In the natural language processing task of clinical named entity recognition (CNER), accurately identifying the boundaries and categories of medical entities is crucial. However, traditional methods struggle to recognize a large number of clinical terms and symbols that have never been encountered before, ultimately limiting the performance of CNER. Besides, there exist some easy-to-confuse Chinese clinical entities that are semantically similar but belong to quite different categories, such as “ 肺结节 ” (pulmonary nodules, a symptom entity) and “ 肺结核 ” (pulmonary tuberculosis, a disease entity), which can lead to entity misidentification. To address these problems, we propose a novel NER model called Cross-Interaction of Chinese characters structures and Boundary Features (CCS). The proposed model leverages Chinese character structural features and boundary information to comprehensively and accurately identify confusing entities. We further design a Cross-Attention mechanism to capture dependency relationships between different entities and radicals of characters, enhancing the model's semantic understanding of specialized terms and symbols, as well as improving its ability to recognize boundaries. Our experimental results show that our proposed model outperforms other state-of-the-art models on various public medical datasets, achieving significant improvements on the CCKS2020, CMeEE, CMI, and IMCS datasets, respectively.

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JBHI Journal 2025 Journal Article

DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding

Ke Liu
Xin Xing
Tao Yang
Zhuliang Yu
Bin Xiao
Guoyin Wang
Wei Wu

Objective: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods. Methods: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features. Main results: DMSACNN achieves impressive accuracies of 78. 20%, 96. 34% and 70. 90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods. Conclusion and significance: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems.

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IJCAI Conference 2025 Conference Paper

From Sparse to Complete: Semantic Understanding Based on Stroke Evolution in On-the-fly Sketch-based Image Retrieval

Yingge Liu
Dawei Dai
Xiangling Hou
Shilin Zhao
Guoyin Wang

In contrast with human sketching, which pre-conceptualizes outlines and features, conventional sketch retrieval models rely primarily rely on pixel-level processing and feature extraction, limiting their ability to capture early sketch intent. Consequently, these models are susceptible to subjective stroke noise, reducing retrieval accuracy. To address this issue, we propose a novel on-the-fly noise stroke retrieval framework designed to align with human sketch-drawing cognition. The proposed framework introduces two core innovations. (i) A stroke consistency detection module that effectively discriminates and suppresses noise strokes by quantifying the structural similarity between the current stroke and the target image, as well as its alignment with key skeletal components. (ii) An adaptive gated mixture of experts module that dynamically selects and integrates features from multiple expert networks during the early, sparse stages of sketching, thereby capturing relevant information with greater precision. Experimental results across diverse sketch datasets demonstrate that the proposed method effectively identifies and suppresses early noise strokes, significantly enhances sketch retrieval performance, and exhibits strong robustness across varying sketch styles.

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IJCAI Conference 2025 Conference Paper

Fusion of Granular-Ball Visual Spatial Representations for Enhanced Facial Expression Recognition

Shuaiyu Liu
Qiyao Shen
Yunxi Wang
Yazhou Ren
Guoyin Wang

Facial Expression Recognition (FER) is a fundamental problem in computer vision. Despite recent advances, significant challenges remain. Current methods primarily focus on extracting visual representations while overlooking other valuable information. To address this limitation, we propose a novel method called Component Separation and Granular-ball Space Bootstrap Fusion (CS-GBSBF), which leverages granular balls to transform visual images to spatial graphs, thereby enlarging the spatial information embedded in images. Our method separates the face into different components and utilizes the spatial information to bootstrap the fusion. More specifically, CS-GBSBF mainly consists of three crucial networks: Represent Extraction Network (REN), Represent Separation Network (RSN) and Represent Fusion Network (RFN). First, granular balls are used to represent expression images as graphs, which are fed into REN along with images. Then, RSN separates basic visual/spatial representations extracted from REN into a set of component visual/spatial representations. Next, RFN utilizes spatial representations to bootstrap component visual integration. A significant challenge in two-stream models is feature alignment, for which we have developed Attention Guidance Module (AGM) and Bootstrap Alignment Loss (L_BA) in REN and RFN, respectively. Results of experiment on eight databases show that CS-GBSBF consistently achieves higher recognition accuracy than several state-of-the-art methods. The code is available at https: //github. com/Lsy235/CS-GBSBF.

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IJCAI Conference 2025 Conference Paper

GBGC: Efficient and Adaptive Graph Coarsening via Granular-ball Computing

Shuyin Xia
Guan Wang
Gaojie Xu
Sen Zhao
Guoyin Wang

The objective of graph coarsening is to generate smaller, more manageable graphs while preserving key information of the original graph. Previous work were mainly based on the perspective of spectrum-preserving, using some predefined coarsening rules to make the eigenvalues of the Laplacian matrix of the original graph and the coarsened graph match as much as possible. However, they largely overlooked the fact that the original graph is composed of subregions at different levels of granularity, where highly connected and similar nodes should be more inclined to be aggregated together as nodes in the coarsened graph. By combining the multi-granularity characteristics of the graph structure, we can generate coarsened graph at the optimal granularity. To this end, inspired by the application of granular-ball computing in multi-granularity, we propose a new multi-granularity, efficient, and adaptive coarsening method via granular-ball (GBGC), which significantly improves the coarsening results and efficiency. Specifically, GBGC introduces an adaptive granular-ball graph refinement mechanism, which adaptively splits the original graph from coarse to fine into granular-balls of different sizes and optimal granularity, and constructs the coarsened graph using these granular-balls as supernodes. In addition, compared with other state-of-the-art graph coarsening methods, the processing speed of this method can be increased by tens to hundreds of times and has lower time complexity. The accuracy of GBGC is almost always higher than that of the original graph due to the good robustness and generalization of the granular-ball computing, so it has the potential to become a standard graph data preprocessing method.

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IJCAI Conference 2025 Conference Paper

Granular-Ball-Induced Multiple Kernel K-Means

Shuyin Xia
Yifan Wang
Lifeng Shen
Guoyin Wang

Most existing multi-kernel clustering algorithms, such as multi-kernel K-means, often struggle with computational efficiency and robustness when faced with complex data distributions. These challenges stem from their dependence on point-to-point relationships for optimization, which can lead to difficulty in accurately capturing data sets' inherent structure and diversity. Additionally, the intricate interplay between multiple kernels in such algorithms can further exacerbate these issues, effectively impacting their ability to cluster data points in high-dimensional spaces. In this paper, we leverage granular-ball computing to improve the multi-kernel clustering framework. The core of granular-ball computing is to adaptively fit data distribution by balls from coarse to acceptable levels. Each ball can enclose data points based on a density consistency measurement. Such ball-based data description thus improves the computational efficiency and the robustness to unknown noises. Specifically, based on granular-ball representations, we introduce the granular-ball kernel (GBK) and its corresponding granular-ball multi-kernel K-means framework (GB-MKKM) for efficient clustering. Using granular-ball relationships in multiple kernel spaces, the proposed GB-MKKM framework shows its superiority in efficiency and clustering performance in the empirical evaluation of various clustering tasks.

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AAAI Conference 2025 Conference Paper

Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training

Shuyin Xia
Xinjun Ma
Zhiyuan Liu
Cheng Liu
Sen Zhao
Guoyin Wang

Graph Neural Networks (GNNs) have demonstrated significant achievements in processing graph data, yet scalability remains a substantial challenge. To address this, numerous graph coarsening methods have been developed. However, most existing coarsening methods are training-dependent, leading to lower efficiency, and they all require a predefined coarsening rate, lacking an adaptive approach. In this paper, we employ granular-ball computing to effectively compress graph data. We construct a coarsened graph network by iteratively splitting the graph into granular-balls based on a purity threshold and using these granular-balls as super vertices. This granulation process significantly reduces the size of the original graph, thereby greatly enhancing the training efficiency and scalability of GNNs. Additionally, our algorithm can adaptively perform splitting without requiring a predefined coarsening rate. Experimental results demonstrate that our method achieves accuracy comparable to training on the original graph. Noise injection experiments further indicate that our method exhibits robust performance. Moreover, our approach can reduce the graph size by up to 20 times without compromising test accuracy, substantially enhancing the scalability of GNNs.

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EAAI Journal 2025 Journal Article

Inter-layer explainable variational autoencoder model for multivariate time series anomaly detection

Xiaoxia Zhang
Guangyao Wang
Yi Chen
Wenzhi Yang
Guoyin Wang

The rapid expansion of big data and artificial intelligence has significantly increased the dimensionality and quantity of monitoring data across various industries. Multivariate time series data, one of the most frequently used data types in various industries, often contains anomalies caused by human error or electromagnetic interference. Detecting these anomalies is essential, the volume of data makes manual labeling impractical, and supervised methods are limited by their inability to address unknown anomalies. Additionally, existing deep learning detection methods often function as black-box models, reducing trust in the results and obscuring the testing process. To address these challenges, we propose Shapley Attention Variational Autoencoder (SHAPAttenVAE), an inter-layer explainable variational autoencoder (VAE) model that combines shapley values with a multi-head attention mechanism. SHAPAttenVAE leverages shapley values to quantify the contribution of nodes from previous layers to their successors, helping to identify root causes of anomalies across dimensions. The model is optimized using a two-phase training method inspired by adversarial network, where random Gaussian noise is introduced to reduce noise interference within the data. Experimental results on several large datasets demonstrate that SHAPAttenVAE outperforms existing models in both accuracy and robustness. These findings establish SHAPAttenVAE as an effective and interpretable framework for anomaly detection in multivariate time series, making it a promising tool for widespread industry application.

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AAAI Conference 2025 Conference Paper

Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundary

Yanhua Li
Xiaocao Ouyang
Chaofan Pan
Jie Zhang
Sen Zhao
Shuyin Xia
Xin Yang
Guoyin Wang

Open intent classification is critical for the development of dialogue systems, aiming to accurately classify known intents into their corresponding classes while identifying unknown intents. Prior boundary-based methods assumed known intents fit within compact spherical regions, focusing on coarse-grained representation and precise spherical decision boundaries. However, these assumptions are often violated in practical scenarios, making it difficult to distinguish known intent classes from unknowns using a single spherical boundary. To tackle these issues, we propose a Multi-granularity Open intent classification method via adaptive Granular-Ball decision boundary (MOGB). Our MOGB method consists of two modules: representation learning and decision boundary acquiring. To effectively represent the intent distribution, we design a hierarchical representation learning method. This involves iteratively alternating between adaptive granular-ball clustering and nearest sub-centroid classification to capture fine-grained semantic structures within known intent classes. Furthermore, multi-granularity decision boundaries are constructed for open intent classification by employing granular-balls with varying centroids and radii. Extensive experiments conducted on three public datasets demonstrate the effectiveness of our proposed method.

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JBHI Journal 2025 Journal Article

Multi-Scale Dynamic Sparse Token Multi-Instance Learning for Pathology Image Classification

Dajiang Lei
Yuqi Zhang
Haodong Wang
Xiaomin Xiong
Bo Xu
Guoyin Wang

In many challenging breast cancer pathology images, the proportion of truly informative tumor regions is extremely limited. The disparity between the essential information required for clinical diagnosis (Tumor area less than 10 $\%$ ) and the vast amount of data within Whole Slide Images (WSIs) makes it exceedingly difficult for pathologists to identify subtle lesions. To address the labor-intensive task imposed by this information gap, this paper proposes a dynamic sparse token based multi-instance learning framework. This framework incorporates a dynamic sparse layer into the transformer architecture, gradually adapting to selectively filter key instances beneficial for the task. Furthermore, to tackle complex scenarios in pathology image tasks, we introduce a weakly supervised cross-scale contrastive learning framework. This framework leverages pathology image features at different scales to perform contrastive learning at the bag-level representation to overcome existing challenges in multi-scale feature fusion in pathology image tasks. To validate the effectiveness and transferability of the model, we conducted various single-scale and multi-scale experiments across four cancer datasets and conducted interpretable analyses. Compared to other state-of-the-art methods, our classification model demonstrates superior performance across six evaluation metrics.

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NeurIPS Conference 2025 Conference Paper

SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Xeron Du
Yifan Yao
Kaijing Ma
Bingli Wang
Tianyu Zheng
Minghao Liu
Yiming Liang
Xiaolong Jin

Large language models (LLMs) have demonstrated remarkable proficiency in mainstream academic disciplines such as mathematics, physics, and computer science. However, human knowledge encompasses over 200 specialized disciplines, far exceeding the scope of existing benchmarks. The capabilities of LLMs in many of these specialized fields-particularly in light industry, agriculture, and service-oriented disciplines-remain inadequately evaluated. To address this gap, we present SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines. Our benchmark employs a novel Human-LLM collaborative filtering mechanism to eliminate trivial or ambiguous questions through iterative refinement based on both LLM responses and expert feedback. Our experimental results reveal significant room for improvement in the performance of current state-of-the-art LLMs across diverse knowledge domains (e. g. , the reasoning-focused model Gemini-2. 5-Pro achieved the highest accuracy of 63. 56% on SuperGPQA), highlighting the considerable gap between current model capabilities and artificial general intelligence. Additionally, we present comprehensive insights from our management of a large-scale annotation process, involving over 80 expert annotators and an interactive Human-LLM collaborative system, offering valuable methodological guidance for future research initiatives of comparable scope.

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JBHI Journal 2024 Journal Article

MSVTNet: Multi-Scale Vision Transformer Neural Network for EEG-Based Motor Imagery Decoding

Ke Liu
Tao Yang
Zhuliang Yu
Weibo Yi
Hong Yu
Guoyin Wang
Wei Wu

Object: Transformer-based neural networks have been applied to the electroencephalography (EEG) decoding for motor imagery (MI). However, most networks focus on applying the self-attention mechanism to extract global temporal information, while the cross-frequency coupling features between different frequencies have been neglected. Additionally, effectively integrating different neural networks poses challenges for the advanced design of decoding algorithms. Methods: This study proposes a novel end-to-end Multi-Scale Vision Transformer Neural Network (MSVTNet) for MI-EEG classification. MSVTNet first extracts local spatio-temporal features at different filtered scales through convolutional neural networks (CNNs). Then, these features are concatenated along the feature dimension to form local multi-scale spatio-temporal feature tokens. Finally, Transformers are utilized to capture cross-scale interaction information and global temporal correlations, providing more distinguishable feature embeddings for classification. Moreover, auxiliary branch loss is leveraged for intermediate supervision to ensure the effective integration of CNNs and Transformers. Results: The performance of MSVTNet was assessed through subject-dependent (session-dependent and session-independent) and subject-independent experiments on three MI datasets, i. e. , the BCI competition IV 2a, 2b and OpenBMI datasets. The experimental results demonstrate that MSVTNet achieves state-of-the-art performance in all analyses. Conclusion: MSVTNet shows superiority and robustness in enhancing MI decoding performance.

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AAAI Conference 2024 Conference Paper

Multi-Granularity Causal Structure Learning

Jiaxuan Liang
Jun Wang
Guoxian Yu
Shuyin Xia
Guoyin Wang

Unveiling, modeling, and comprehending the causal mechanisms underpinning natural phenomena stand as fundamental endeavors across myriad scientific disciplines. Meanwhile, new knowledge emerges when discovering causal relationships from data. Existing causal learning algorithms predominantly focus on the isolated effects of variables, overlook the intricate interplay of multiple variables and their collective behavioral patterns. Furthermore, the ubiquity of high-dimensional data exacts a substantial temporal cost for causal algorithms. In this paper, we develop a novel method called MgCSL (Multi-granularity Causal Structure Learning), which first leverages sparse auto-encoder to explore coarse-graining strategies and causal abstractions from micro-variables to macro-ones. MgCSL then takes multi-granularity variables as inputs to train multilayer perceptrons and to delve the causality between variables. To enhance the efficacy on high-dimensional data, MgCSL introduces a simplified acyclicity constraint to adeptly search the directed acyclic graph among variables. Experimental results show that MgCSL outperforms competitive baselines, and finds out explainable causal connections on fMRI datasets.

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AAAI Conference 2020 Conference Paper

Graph-Driven Generative Models for Heterogeneous Multi-Task Learning

Wenlin Wang
Hongteng Xu
Zhe Gan
Bai Li
Guoyin Wang
Liqun Chen
Qian Yang
Wenqi Wang

We propose a novel graph-driven generative model, that uniﬁes multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogeneous learning tasks, which correspond to different generative processes, often rely on data with a shared graph structure. Accordingly, our model combines a graph convolutional network (GCN) with multiple variational autoencoders, thus embedding the nodes of the graph (i. e. , samples for the tasks) in a uniform manner, while specializing their organization and usage to different tasks. With a focus on healthcare applications (tasks), including clinical topic modeling, procedure recommendation and admission-type prediction, we demonstrate that our method successfully leverages information across different tasks, boosting performance in all tasks and outperforming existing state-of-the-art approaches.

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AAAI Conference 2020 Conference Paper

Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning

Liqun Chen
Ke Bai
Chenyang Tao
Yizhe Zhang
Guoyin Wang
Wenlin Wang
Ricardo Henao
Lawrence Carin

Reinforcement learning (RL) has been widely used to aid training in language generation. This is achieved by enhancing standard maximum likelihood objectives with userspeciﬁed reward functions that encourage global semantic consistency. We propose a principled approach to address the difﬁculties associated with RL-based solutions, namely, highvariance gradients, uninformative rewards and brittle training. By leveraging the optimal transport distance, we introduce a regularizer that signiﬁcantly alleviates the above issues. Our formulation emphasizes the preservation of semantic features, enabling end-to-end training instead of ad-hoc ﬁne-tuning, and when combined with RL, it controls the exploration space for more efﬁcient model updates. To validate the effectiveness of the proposed solution, we perform a comprehensive evaluation covering a wide variety of NLP tasks: machine translation, abstractive text summarization and image caption, with consistent improvements over competing solutions.

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NeurIPS Conference 2019 Conference Paper

Improving Textual Network Learning with Variational Homophilic Embeddings

Wenlin Wang
Chenyang Tao
Zhe Gan
Guoyin Wang
Liqun Chen
Xinyuan Zhang
Ruiyi Zhang
Qian Yang

The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensional vertex-based vector representations. This paper considers a novel variational formulation of network embeddings, with special focus on textual networks. Different from most existing methods that optimize a discriminative objective, we introduce Variational Homophilic Embedding (VHE), a fully generative model that learns network embeddings by modeling the semantic (textual) information with a variational autoencoder, while accounting for the structural (topology) information through a novel homophilic prior design. Homophilic vertex embeddings encourage similar embedding vectors for related (connected) vertices. The VHE encourages better generalization for downstream tasks, robustness to incomplete observations, and the ability to generalize to unseen vertices. Extensive experiments on real-world networks, for multiple tasks, demonstrate that the proposed method achieves consistently superior performance relative to competing state-of-the-art approaches.

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NeurIPS Conference 2019 Conference Paper

Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods

Kevin Liang
Guoyin Wang
Yitong Li
Ricardo Henao
Lawrence Carin

We investigate time-dependent data analysis from the perspective of recurrent kernel machines, from which models with hidden units and gated memory cells arise naturally. By considering dynamic gating of the memory cell, a model closely related to the long short-term memory (LSTM) recurrent neural network is derived. Extending this setup to $n$-gram filters, the convolutional neural network (CNN), Gated CNN, and recurrent additive network (RAN) are also recovered as special cases. Our analysis provides a new perspective on the LSTM, while also extending it to $n$-gram convolutional filters. Experiments are performed on natural language processing tasks and on analysis of local field potentials (neuroscience). We demonstrate that the variants we derive from kernels perform on par or even better than traditional neural methods. For the neuroscience application, the new models demonstrate significant improvements relative to the prior state of the art.

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NeurIPS Conference 2018 Conference Paper

Adversarial Text Generation via Feature-Mover's Distance

Liqun Chen
Shuyang Dai
Chenyang Tao
Haichao Zhang
Zhe Gan
Dinghan Shen
Yizhe Zhang
Guoyin Wang

Generative adversarial networks (GANs) have achieved significant success in generating real-valued data. However, the discrete nature of text hinders the application of GAN to text-generation tasks. Instead of using the standard GAN objective, we propose to improve text-generation GAN via a novel approach inspired by optimal transport. Specifically, we consider matching the latent feature distributions of real and synthetic sentences using a novel metric, termed the feature-mover's distance (FMD). This formulation leads to a highly discriminative critic and easy-to-optimize objective, overcoming the mode-collapsing and brittle-training problems in existing methods. Extensive experiments are conducted on a variety of tasks to evaluate the proposed model empirically, including unconditional text generation, style transfer from non-parallel text, and unsupervised cipher cracking. The proposed model yields superior performance, demonstrating wide applicability and effectiveness.

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NeurIPS Conference 2017 Conference Paper

Deconvolutional Paragraph Representation Learning

Yizhe Zhang
Dinghan Shen
Guoyin Wang
Zhe Gan
Ricardo Henao
Lawrence Carin

Learning latent representations from long text sequences is an important first step in many natural language processing applications. Recurrent Neural Networks (RNNs) have become a cornerstone for this challenging task. However, the quality of sentences during RNN-based decoding (reconstruction) decreases with the length of the text. We propose a sequence-to-sequence, purely convolutional and deconvolutional autoencoding framework that is free of the above issue, while also being computationally efficient. The proposed method is simple, easy to implement and can be leveraged as a building block for many applications. We show empirically that compared to RNNs, our framework is better at reconstructing and correcting long paragraphs. Quantitative evaluation on semi-supervised text classification and summarization tasks demonstrate the potential for better utilization of long unlabeled text data.

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IS Journal 2015 Journal Article

Brain Informatics-Based Big Data and the Wisdom Web of Things

Ning Zhong
Stephen S. Yau
Jianhua Ma
Shinsuke Shimojo
Marcel Just
Bin Hu
Guoyin Wang
Kazuhiro Oiwa

The authors summarize the main aspects of brain informatics based big data interacting in the social-cyber-physical space of the Wisdom Web of Things (W2T). In particular, they focus on how to realize human-level collective intelligence as a big data sharing mind--a harmonized collectivity of consciousness on the W2T that uses brain-inspired intelligent technologies to provide wisdom services. Finally, the authors propose five guiding principles to deeper understanding the nature of the vigorous interaction and interdependence of brain-body-environment.

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