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Yujie Li

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13 papers

2 author rows

AAAI Conference 2026 Conference Paper

APT: Affine Prototype-Timestamp for Time Series Forecasting Under Distribution Shift

Yujie Li
Zezhi Shao
Chengqing Yu
Yisong Fu
Tao Sun
Yongjun Xu
Fei Wang

Time series forecasting under distribution shift remains challenging, as existing deep learning models often rely on local statistical normalization (e.g., mean and variance) that fails to capture global distribution shift. Methods like RevIN and its variants attempt to decouple distribution and pattern but still struggle with missing values, noisy observations, and invalid channel-wise affine transformation. To address these limitations, we propose Affine Prototype-Timestamp(APT), a lightweight and flexible plug-in module that injects global distribution features into the normalization–forecasting pipeline. By leveraging timestamp-conditioned prototype learning, APT dynamically generates affine parameters that modulate both input and output series, enabling the backbone to learn from self-supervised, distribution-aware clustered instances. APT is compatible with arbitrary forecasting backbones and normalization strategies while introducing minimal computational overhead. Extensive experiments across six benchmark datasets and multiple backbone-normalization combinations demonstrate that APT significantly improves forecasting performance under distribution shift.

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EAAI Journal 2026 Journal Article

Enhancing vision-and-language transformers through two-stage generative alignment pre-training

Yang Qin
Huiming Xie
Shuxue Ding
Yujie Li
Benying Tan

Vision-language pre-training (VLP) models based on transformer architectures have achieved significant success in bridging the gap between natural language processing and computer vision. However, the alignment between visual and textual semantic objects remains a major challenge, particularly when noisy image–text pairs are used for training, which can lead to incorrect semantic associations and degrade model performance. In this paper, we propose a novel two-stage generative alignment pre-training framework, called VL-GAP (Vision-Language Generative-Alignment Pre-training), designed to improve object alignment quality in vision-language models. In the first stage, we utilize high-quality annotated datasets to perform supervised learning, introducing a center-point strategy for automated object alignment and optimizing multiple loss functions to achieve precise visual-text alignment. The second stage leverages large-scale noisy datasets for self-supervised learning, where momentum models and confidence-based pseudo-label filtering are employed to enhance the model’s robustness to noise. Experimental results demonstrate that VL-GAP outperforms state-of-the-art models in various downstream tasks, highlighting the importance of object alignment quality over data scale in improving VLP model performance. Our approach provides new insights into effective handling of noisy data and advances the capability of vision-language models to understand and generate coherent multimodal descriptions.

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ICML Conference 2025 Conference Paper

Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions

Eray Erturk
Fahad Kamran
Salar Abbaspourazad
Sean Jewell
Harsh Sharma
Yujie Li
Sinead A. Williamson
Nicholas J. Foti

Wearable devices record physiological and behavioral signals that can improve health predictions. While foundation models are increasingly used for such predictions, they have been primarily applied to low-level sensor data, despite behavioral data often being more informative due to their alignment with physiologically relevant timescales and quantities. We develop foundation models of such behavioral signals using over 2. 5B hours of wearable data from 162K individuals, systematically optimizing architectures and tokenization strategies for this unique dataset. Evaluated on 57 health-related tasks, our model shows strong performance across diverse real-world applications including individual-level classification and time-varying health state prediction. The model excels in behavior-driven tasks like sleep prediction, and improves further when combined with representations of raw sensor data. These results underscore the importance of tailoring foundation model design to wearables and demonstrate the potential to enable new health applications.

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NeurIPS Conference 2025 Conference Paper

On the Integration of Spatial-Temporal Knowledge: A Lightweight Approach to Atmospheric Time Series Forecasting

Yisong Fu
Fei Wang
Zezhi Shao
Boyu Diao
Lin Wu
Zhulin An
Chengqing Yu
Yujie Li

Transformers have gained attention in atmospheric time series forecasting (ATSF) for their ability to capture global spatial-temporal correlations. However, their complex architectures lead to excessive parameter counts and extended training times, limiting their scalability to large-scale forecasting. In this paper, we revisit ATSF from a theoretical perspective of atmospheric dynamics and uncover a key insight: spatial-temporal position embedding (STPE) can inherently model spatial-temporal correlations even without attention mechanisms. Its effectiveness arises from integrating geographical coordinates and temporal features, which are intrinsically linked to atmospheric dynamics. Based on this, we propose STELLA, a S patial- T emporal knowledge E mbedded L ightweight mode L for ASTF, utilizing only STPE and an MLP architecture in place of Transformer layers. With 10k parameters and one hour of training, STELLA achieves superior performance on five datasets compared to other advanced methods. The paper emphasizes the effectiveness of spatial-temporal knowledge integration over complex architectures, providing novel insights for ATSF.

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NeurIPS Conference 2025 Conference Paper

Selective Learning for Deep Time Series Forecasting

Yisong Fu
Zezhi Shao
Chengqing Yu
Yujie Li
Zhulin An
Qi Wang
Yongjun Xu
Fei Wang

Benefiting from high capacity for capturing complex temporal patterns, deep learning (DL) has significantly advanced time series forecasting (TSF). However, deep models tend to suffer from severe overfitting due to the inherent vulnerability of time series to noise and anomalies. The prevailing DL paradigm uniformly optimizes all timesteps through the MSE loss and learns those uncertain and anomalous timesteps without difference, ultimately resulting in overfitting. To address this, we propose a novel selective learning strategy for deep TSF. Specifically, selective learning screens a subset of the whole timesteps to calculate the MSE loss in optimization, guiding the model to focus on generalizable timesteps while disregarding non-generalizable ones. Our framework introduces a dual-mask mechanism to target timesteps: (1) an uncertainty mask leveraging residual entropy to filter uncertain timesteps, and (2) an anomaly mask employing residual lower bound estimation to exclude anomalous timesteps. Extensive experiments across eight real-world datasets demonstrate that selective learning can significantly improve the predictive performance for typical state-of-the-art deep models, including 37. 4% MSE reduction for Informer, 8. 4% for TimesNet, and 6. 5% for iTransformer.

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AAAI Conference 2024 Conference Paper

Learning to Prompt Knowledge Transfer for Open-World Continual Learning

Yujie Li
Xin Yang
Hao Wang
Xiangkun Wang
Tianrui Li

This paper studies the problem of continual learning in an open-world scenario, referred to as Open-world Continual Learning (OwCL). OwCL is increasingly rising while it is highly challenging in two-fold: i) learning a sequence of tasks without forgetting knowns in the past, and ii) identifying unknowns (novel objects/classes) in the future. Existing OwCL methods suffer from the adaptability of task-aware boundaries between knowns and unknowns, and do not consider the mechanism of knowledge transfer. In this work, we propose Pro-KT, a novel prompt-enhanced knowledge transfer model for OwCL. Pro-KT includes two key components: (1) a prompt bank to encode and transfer both task-generic and task-specific knowledge, and (2) a task-aware open-set boundary to identify unknowns in the new tasks. Experimental results using two real-world datasets demonstrate that the proposed Pro-KT outperforms the state-of-the-art counterparts in both the detection of unknowns and the classification of knowns markedly. Code released at https://github.com/YujieLi42/Pro-KT.

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JBHI Journal 2024 Journal Article

MVF-SleepNet: Multi-View Fusion Network for Sleep Stage Classification

Yujie Li
Jingrui Chen
Wenjun Ma
Gansen Zhao
Xiaomao Fan

Sleep stage classification is of great importance in human health monitoring and disease diagnosing. Clinically, visual-inspected classifying sleep into different stages is quite time consuming and highly relies on the expertise of sleep specialists. Many automated models for sleep stage classification have been proposed in previous studies but their performances still exist a gap to the real clinical application. In this work, we propose a novel multi-view fusion network named MVF-SleepNet based on multi-modal physiological signals of electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and electromyography (EMG). To capture the relationship representation among multi-modal physiological signals, we construct two views of Time-frequency images (TF images) and Graph-learned graphs (GL graphs). To learn the spectral-temporal representation from sequentially timed TF images, the combination of VGG-16 and GRU networks is utilized. To learn the spatial-temporal representation from sequentially timed GL graphs, the combination of Chebyshev graph convolution and temporal convolution networks is employed. Fusing the spectral-temporal representation and spatial-temporal representation can further boost the performance of sleep stage classification. A large number of experiment results on the publicly available datasets of ISRUC-S1 and ISRUC-S3 show that the MVF-SleepNet achieves overall accuracy of 0. 821, $F_{1}$ score of 0. 802 and Kappa of 0. 768 on ISRUC-S1 dataset, and accuracy of 0. 841, $F_{1}$ score of 0. 828 and Kappa of 0. 795 on ISRUC-S3 dataset. The MVF-SleepNet achieves competitive results on both datasets of ISRUC-S1 and ISRUC-S3 for sleep stage classification compared to the state-of-the-art baselines. The source code of MVF-SleepNet is available on Github ( https://github.com/YJPai65/MVF-SleepNet ).

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NeurIPS Conference 2024 Conference Paper

Ordering-Based Causal Discovery for Linear and Nonlinear Relations

Zhuopeng Xu
Yujie Li
Cheng Liu
Ning Gui

Identifying causal relations from purely observational data typically requires additional assumptions on relations and/or noise. Most current methods restrict their analysis to datasets that are assumed to have pure linear or nonlinear relations, which is often not reflective of real-world datasets that contain a combination of both. This paper presents CaPS, an ordering-based causal discovery algorithm that effectively handles linear and nonlinear relations. CaPS introduces a novel identification criterion for topological ordering and incorporates the concept of "parent score" during the post-processing optimization stage. These scores quantify the strength of the average causal effect, helping to accelerate the pruning process and correct inaccurate predictions in the pruning step. Experimental results demonstrate that our proposed solutions outperform state-of-the-art baselines on synthetic data with varying ratios of linear and nonlinear relations. The results obtained from real-world data also support the competitiveness of CaPS. Code and datasets are available at https: //github. com/E2real/CaPS.

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AIIM Journal 2024 Journal Article

Value function assessment to different RL algorithms for heparin treatment policy of patients with sepsis in ICU

Jiang Liu
Yihao Xie
Xin Shu
Yuwen Chen
Yizhu Sun
Kunhua Zhong
Hao Liang
Yujie Li

Heparin is a critical aspect of managing sepsis after abdominal surgery, which can improve microcirculation, protect organ function, and reduce mortality. However, there is no clinical evidence to support decision-making for heparin dosage. This paper proposes a model called SOFA-MDP, which utilizes SOFA scores as states of MDP, to investigate clinic policies. Different algorithms provide different value functions, making it challenging to determine which value function is more reliable. Due to ethical restrictions, we cannot test all policies on patients. To address this issue, we proposed two value function assessment methods: action similarity rate and relative gain. We experimented with heparin treatment policies for sepsis patients after abdominal surgery using MIMIC-IV. In the experiments, TD ( 0 ) shows the most reliable performance. Using the action similarity rate and relative gain to assess AI policy from TD ( 0 ), the agreement rates between AI policy and “good” physician’s actual treatment are 64. 6% and 73. 2%, while the agreement rates between AI policy and “bad” physician’s actual treatment are 44. 1% and 35. 8%, the gaps are 20. 5% and 37. 4%, respectively. External validation using action similarity rate and relative gain based on eICU resulted in agreement rates of 61. 5% and 69. 1% with the “good” physician’s treatment, and 45. 2% and 38. 3% with the “bad” physician’s treatment, with gaps of 16. 3% and 30. 8%, respectively. In conclusion, the model provides instructive support for clinical decisions, and the evaluation methods accurately distinguish reliable and unreasonable outcomes.

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AAAI Conference 2023 Short Paper

Cross-Regional Fraud Detection via Continual Learning (Student Abstract)

Yujie Li
Yuxuan Yang
Qiang Gao
Xin Yang

Detecting fraud is an urgent task to avoid transaction risks. Especially when expanding a business to new cities or new countries, developing a totally new model will bring the cost issue and result in forgetting previous knowledge. This study proposes a novel solution based on heterogeneous trade graphs, namely HTG-CFD, to prevent knowledge forgetting of cross-regional fraud detection. Specifically, a novel heterogeneous trade graph is meticulously constructed from original transactions to explore the complex semantics among different types of entities and relationships. Motivated by continual learning, we present a practical and task-oriented forgetting prevention method to alleviate knowledge forgetting in the context of cross-regional detection. Extensive experiments demonstrate that HTG-CFD promotes performance in both cross-regional and single-regional scenarios.

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EAAI Journal 2019 Journal Article

Railway track fastener defect detection based on image processing and deep learning techniques: A comparative study

Xiukun Wei
Ziming Yang
Yuxin Liu
Dehua Wei
Limin Jia
Yujie Li

The railway track fasteners play a critical role in fixing the track on the ballast bed. Achieving full automation of the fastener defect detection is significant in terms of ensuring track safety, and reducing maintains cost. In this paper, innovative and intelligent methods using image processing technologies and deep learning networks are proposed. In the first part, the traditional fastener positioning method based on image processing is reconsidered. In addition, a novel fastener defect detection and identification method using Dense-SIFT features is proposed which can achieve a better performance than the methods available in the literature. In the second part, VGG16 is trained for fastener defect detection and recognition. The result demonstrates that it is possible to carry out the defect detection of fasteners with CNN. Finally, Faster R-CNN is used for fastener defect detection to advance detection rate and efficiency. The fastener positioning and recognition can be carried out simultaneously. The time for the defect detection and classification is only one-tenth of the other methods mentioned above.

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YNICL Journal 2016 Journal Article

Connectome-scale group-wise consistent resting-state network analysis in autism spectrum disorder

Yu Zhao
Hanbo Chen
Yujie Li
Jinglei Lv
Xi Jiang
Fangfei Ge
Tuo Zhang
Shu Zhang

Understanding the organizational architecture of human brain function and its alteration patterns in diseased brains such as Autism Spectrum Disorder (ASD) patients are of great interests. In-vivo functional magnetic resonance imaging (fMRI) offers a unique window to investigate the mechanism of brain function and to identify functional network components of the human brain. Previously, we have shown that multiple concurrent functional networks can be derived from fMRI signals using whole-brain sparse representation. Yet it is still an open question to derive group-wise consistent networks featured in ASD patients and controls. Here we proposed an effective volumetric network descriptor, named connectivity map, to compactly describe spatial patterns of brain network maps and implemented a fast framework in Apache Spark environment that can effectively identify group-wise consistent networks in big fMRI dataset. Our experiment results identified 144 group-wisely common intrinsic connectivity networks (ICNs) shared between ASD patients and healthy control subjects, where some ICNs are substantially different between the two groups. Moreover, further analysis on the functional connectivity and spatial overlap between these 144 common ICNs reveals connectomics signatures characterizing ASD patients and controls. In particular, the computing time of our Spark-enabled functional connectomics framework is significantly reduced from 240 hours (C ++ code, single core) to 20 hours, exhibiting a great potential to handle fMRI big data in the future.

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YNIMG Journal 2015 Journal Article

Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data

Hanbo Chen
Tao Liu
Yu Zhao
Tuo Zhang
Yujie Li
Meng Li
Hongmiao Zhang
Hui Kuang

Tractography based on diffusion tensor imaging (DTI) data has been used as a tool by a large number of recent studies to investigate structural connectome. Despite its great success in offering unique 3D neuroanatomy information, DTI is an indirect observation with limited resolution and accuracy and its reliability is still unclear. Thus, it is essential to answer this fundamental question: how reliable is DTI tractography in constructing large-scale connectome? To answer this question, we employed neuron tracing data of 1772 experiments on the mouse brain released by the Allen Mouse Brain Connectivity Atlas (AMCA) as the ground-truth to assess the performance of DTI tractography in inferring white matter fiber pathways and inter-regional connections. For the first time in the neuroimaging field, the performance of whole brain DTI tractography in constructing a large-scale connectome has been evaluated by comparison with tracing data. Our results suggested that only with the optimized tractography parameters and the appropriate scale of brain parcellation scheme, can DTI produce relatively reliable fiber pathways and a large-scale connectome. Meanwhile, a considerable amount of errors were also identified in optimized DTI tractography results, which we believe could be potentially alleviated by efforts in developing better DTI tractography approaches. In this scenario, our framework could serve as a reliable and quantitative test bed to identify errors in tractography results which will facilitate the development of such novel tractography algorithms and the selection of optimal parameters.

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