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Yang Meng

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6 papers

1 author row

EAAI Journal 2026 Journal Article

Research on multivariate time series prediction method for upper motion intention perception

Yang Meng
Shuhao Liang
Jinda Wang
Fei Niu
Wendong Wang
Zelin Ci

To address the limitations of relying on a single information source and the low accuracy in upper limb motion intention perception during exoskeleton-based rehabilitation training, a multivariate time-series prediction method that integrates a cross-graph convolution module with a stochastic synthetic attention mechanism is proposed. Specifically, a cross-graph convolution module based on Spatial Node Encoding (SNE) is developed to fuse data from the Inertial Measurement Unit (IMU) and visual signals, thereby capturing spatial relationships among variables. A multi - view topology mapping network with a stochastic synthetic attention mechanism is introduced to extract temporal features, and a Graph Convolutional Network - Long Short - Term Memory (GCN - LSTM) model is constructed. The proposed GCN - LSTM model is compared with the 1 - Dimensional Convolution - Long Short - Term Memory (1DConv - LSTM) and Bidirectional Long Short - Term Memory (Bi - LSTM) models through experiments. The results show that the GCN - LSTM achieves a joint trajectory fitting degree, R2, of 0. 9417. It represents an approximate 9 % improvement over 1DConv–LSTM and a 10 % improvement over Bi–LSTM, effectively enhancing the accuracy of upper limb motion intention perception and contributing to the improvement of rehabilitation training effects.

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NeurIPS Conference 2025 Conference Paper

Transformers for Mixed-type Event Sequences

Felix Draxler
Yang Meng
Kai Nelson
Lukas Laskowski
Yibo Yang
Theofanis Karaletsos
Stephan Mandt

Event sequences appear widely in domains such as medicine, finance, and remote sensing, yet modeling them is challenging due to their heterogeneity: sequences often contain multiple event types with diverse structures—for example, electronic health records that mix discrete events like medical procedures with continuous lab measurements. Existing approaches either tokenize all entries, violating natural inductive biases, or ignore parts of the data to enforce a consistent structure. In this work, we propose a simple yet powerful Marked Temporal Point Process (MTPP) framework for modeling event sequences with flexible structure, using a single unified model. Our approach employs a single autoregressive transformer with discrete and continuous prediction heads, capable of modeling variable-length, mixed-type event sequences. The continuous head leverages an expressive normalizing flow to model continuous event attributes, avoiding the numerical integration required for inter-event times in most competing methods. Empirically, our model excels on both discrete-only and mixed-type sequences, improving prediction quality and enabling interpretable uncertainty quantification. We make our code public at https: //github. com/czi-ai/FlexTPP.

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TMLR Journal 2024 Journal Article

Scaling Up Bayesian Neural Networks with Neural Networks

Zahra Moslemi
Yang Meng
Shiwei Lan
Babak Shahbaba

Bayesian Neural Networks (BNNs) offer a principled and natural framework for proper uncertainty quantification in the context of deep learning. They address the typical challenges associated with conventional deep learning methods, such as data insatiability, ad-hoc nature, and susceptibility to overfitting. However, their implementation typically either relies on Markov chain Monte Carlo (MCMC) methods, which are characterized by their computational intensity and inefficiency in a high-dimensional space, or variational inference methods, which tend to underestimate uncertainty. To address this issue, we propose a novel Calibration-Emulation-Sampling (CES) strategy to significantly enhance the computational efficiency of BNN. In this framework, during the initial calibration stage, we collect a small set of samples from the parameter space. These samples serve as training data for the emulator, which approximates the map between parameters and posterior probability. The trained emulator is then used for sampling from the posterior distribution at substantially higher speed compared to the standard BNN. Using simulated and real data, we demonstrate that our proposed method improves computational efficiency of BNN, while maintaining similar performance in terms of prediction accuracy and uncertainty quantification.

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NeurIPS Conference 2024 Conference Paper

Unity by Diversity: Improved Representation Learning for Multimodal VAEs

Thomas M. Sutter
Yang Meng
Andrea Agostini
Daphné Chopard
Norbert Fortin
Julia E. Vogt
Babak Shahbaba
Stephan Mandt

Variational Autoencoders for multimodal data hold promise for many tasks in data analysis, such as representation learning, conditional generation, and imputation. Current architectures either share the encoder output, decoder input, or both across modalities to learn a shared representation. Such architectures impose hard constraints on the model. In this work, we show that a better latent representation can be obtained by replacing these hard constraints with a soft constraint. We propose a new mixture-of-experts prior, softly guiding each modality's latent representation towards a shared aggregate posterior. This approach results in a superior latent representation and allows each encoding to preserve information better from its uncompressed original features. In extensive experiments on multiple benchmark datasets and two challenging real-world datasets, we show improved learned latent representations and imputation of missing data modalities compared to existing methods.

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EAAI Journal 2021 Journal Article

Graph Convolutional Neural Networks with Geometric and Discrimination information

Ronghua Shang
Yang Meng
Weitong Zhang
Fanhua Shang
Licheng Jiao
Shuyuan Yang

In recent years, geometric deep learning methods have been proposed, which are called Graph Convolutional Neural Networks (GCNNs). GCNNs not only can extract effective features like the classical CNN, but also can effectively reflect the true geometric structure of original data. Although GCNNs consider the geometric structure of original data, they construct the same feature graph to perform graph convolution, and ignore the difference between the local structures of different samples. Therefore, a novel Graph Convolutional Neural Network with Geometric and Discrimination information (GDGCNN) is proposed, which integrates traditional machine learning ideas to further improve the performance of feature extraction. In order to exploit differences between the local structures of different samples and make full use of the geometric structure of original data, GDGCNN constructs different feature graphs for different training batches to fully exploit the local geometry of data. Moreover, the discriminant regularization is introduced into GDGCNN to effectively utilize the discriminant information contained in original data. Therefore, GDGCNN has good discriminative ability and robustness. The experimental results show that GDGCNN can perform feature extraction tasks very well, and it is superior to some existing methods for classification in terms of accuracy and F1-Score.

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EAAI Journal 2018 Journal Article

Dual-graph regularized non-negative matrix factorization with sparse and orthogonal constraints

Yang Meng
Ronghua Shang
Licheng Jiao
Wenya Zhang
Shuyuan Yang

Semi-supervised Non-negative Matrix Factorization (NMF) can not only utilize a fraction of label information, but also effectively learn local information of the objectives, such as documents and faces. Semi-supervised NMF is an efficient technique for dimensionality reduction of high dimensional data. In this paper, we propose a novel semi-supervised NMF, called Dual-graph regularized Non-negative Matrix Factorization with Sparse and Orthogonal constraints (SODNMF). Dual-graph model is added into semi-supervised NMF, and the manifold structures of the data space and the feature space are taken into account simultaneously. In addition, the sparse constraint is used in SODNMF, which can simplify the calculation and accelerate the processing speed. The most important is that SODNMF makes use of bi-orthogonal constraints, which can avoid the non-correspondence between images and basic vectors. Therefore, it can effectively enhance the discrimination and the exclusivity of clustering, and improve the clustering performance. We give the objective function, the iterative updating rules and the convergence proof. Empirical experiments demonstrate encouraging results of our novel algorithm in comparison to four algorithms within some state-of-the-art algorithms through a set of evaluations based on three real datasets.

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