Sen Li

EAAI Journal 2026 Journal Article

A multi-task spatiotemporal deep neural network for predicting penetration depth and morphology in laser welding

• Sen Li
• Haichao Cui
• Chendong Shao
• Yaqi Wang
• Xinhua Tang

AAAI Conference 2025 Conference Paper

Temporal Action Localization with Cross Layer Task Decoupling and Refinement

• Qiang Li
• Di Liu
• Jun Kong
• Sen Li
• Hui Xu
• Jianzhong Wang

Temporal action localization (TAL) involves dual tasks to classify and localize actions within untrimmed videos. However, the two tasks often have conflicting requirements for features. Existing methods typically employ separate heads for classification and localization tasks but share the same input feature, leading to suboptimal performance. To address this issue, we propose a novel TAL method with Cross Layer Task Decoupling and Refinement (CLTDR). Based on the feature pyramid of video, CLTDR strategy integrates semantically strong features from higher pyramid layers and detailed boundary-aware boundary features from lower pyramid layers to effectively disentangle the action classification and localization tasks. Moreover, the multiple features from cross layers are also employed to refine and align the disentangled classification and regression results. At last, a lightweight Gated Multi-Granularity (GMG) module is proposed to comprehensively extract and aggregate video features at instant, local, and global temporal granularities. Benefiting from the CLTDR and GMG modules, our method achieves state-of-the-art performance on five challenging benchmarks: THUMOS14, MultiTHUMOS, EPIC-KITCHENS-100, ActivityNet-1.3, and HACS. Code：https://github.com/LiQiang0307/CLTDR-GMG

EAAI Journal 2024 Journal Article

Machinery degradation trend prediction considering temporal distribution discrepancy between degradation stages

• Shudong Ou
• Ming Zhao
• Hao Wu
• Yue Zhang
• Sen Li

IROS Conference 2024 Conference Paper

Task-Space Riccati Feedback based Whole Body Control for Underactuated Legged Locomotion

• Shunpeng Yang
• Zejun Hong
• Sen Li
• Patrick M. Wensing
• Wei Zhang 0013
• Hua Chen 0007

This manuscript primarily aims to enhance the performance of whole-body controllers(WBC) for underactuated legged locomotion. We introduce a systematic parameter design mechanism for the floating-base feedback control within the WBC. The proposed approach involves utilizing the linearized model of unactuated dynamics to formulate a Linear Quadratic Regulator(LQR) and solving a Riccati gain while accounting for potential physical constraints through a second-order approximation of the log-barrier function. And then the user-tuned feedback gain for the floating base task is replaced by a new one constructed from the solved Riccati gain. Extensive simulations conducted in MuJoCo with a point bipedal robot, as well as real-world experiments performed on a quadruped robot, demonstrate the effectiveness of the proposed method. In the different bipedal locomotion tasks, compared with the user-tuned method, the proposed approach is at least 12% better and up to 50% better at linear velocity tracking, and at least 7% better and up to 47% better at angular velocity tracking. In the quadruped experiment, linear velocity tracking is improved by at least 3% and angular velocity tracking is improved by at least 23% using the proposed method.

ICLR Conference 2021 Conference Paper

Interpreting and Boosting Dropout from a Game-Theoretic View

• Hao Zhang 0063
• Sen Li
• Yinchao Ma
• Mingjie Li
• Yichen Xie 0002
• Quanshi Zhang

This paper aims to understand and improve the utility of the dropout operation from the perspective of game-theoretical interactions. We prove that dropout can suppress the strength of interactions between input variables of deep neural networks (DNNs). The theoretical proof is also verified by various experiments. Furthermore, we find that such interactions were strongly related to the over-fitting problem in deep learning. So, the utility of dropout can be regarded as decreasing interactions to alleviating the significance of over-fitting. Based on this understanding, we propose the interaction loss to further improve the utility of dropout. Experimental results on various DNNs and datasets have shown that the interaction loss can effectively improve the utility of dropout and boost the performance of DNNs.

AAAI Conference 2021 Conference Paper

Joint-Label Learning by Dual Augmentation for Time Series Classification

• Qianli Ma
• Zhenjing Zheng
• Jiawei Zheng
• Sen Li
• Wanqing Zhuang
• Garrison W. Cottrell

Recently, deep neural networks (DNNs) have achieved excellent performance on time series classification. However, DNNs require large amounts of labeled data for supervised training. Although data augmentation can alleviate this problem, the standard approach assigns the same label to all augmented samples from the same source. This leads to the expansion of the data distribution such that the classification boundaries may be even harder to determine. In this paper, we propose Joint-label learning by Dual Augmentation (JobDA), which can enrich the training samples without expanding the distribution of the original data. Instead, we apply simple transformations to the time series and give these modified time series new labels, so that the model has to distinguish between these and the original data, as well as separating the original classes. This approach sharpens the boundaries around the original time series, and results in superior classification performance. We use Time Series Warping for our transformations: We shrink and stretch different regions of the original time series, like a fun-house mirror. Experiments conducted on extensive time-series datasets show that JobDA can improve the model performance on small datasets. Moreover, we verify that JobDA has better generalization ability compared with conventional data augmentation, and the visualization analysis further demonstrates that JobDA can learn more compact clusters.

AAAI Conference 2021 Conference Paper

Learning Representations for Incomplete Time Series Clustering

• Qianli Ma
• Chuxin Chen
• Sen Li
• Garrison W. Cottrell

Time-series clustering is an essential unsupervised technique for data analysis, applied to many real-world fields, such as medical analysis and DNA microarray. Existing clustering methods are usually based on the assumption that the data is complete. However, time series in real-world applications often contain missing values. Traditional strategy (imputing first and then clustering) does not optimize the imputation and clustering process as a whole, which not only makes performance dependent on the combination of imputation and clustering methods but also fails to achieve satisfactory results. How to best improve the clustering performance on incomplete time series remains a challenge. This paper proposes a novel unsupervised temporal representation learning model, named Clustering Representation Learning on Incomplete time-series data (CRLI). CRLI jointly optimizes the imputation and clustering process to impute more discriminative values for clustering and make the learned representations possessed good clustering property. Also, to reduce the error propagation from imputation to clustering, we introduce a discriminator to make the distribution of imputation values close to the true one and train CRLI in an alternating training manner. An experiment conducted on eight real-world incomplete time-series datasets shows that CRLI outperforms existing methods. We demonstrate the effectiveness of the learned representations and the convergence of the model through visualization analysis. Moreover, we reveal that the joint training strategy can impute values close to the true ones in those important sub-sequences, and impute more discriminative values in those less important sub-sequences at the same time, making the imputed sequence cluster-friendly.

AAAI Conference 2020 Conference Paper

Adversarial Dynamic Shapelet Networks

• Qianli Ma
• Wanqing Zhuang
• Sen Li
• Desen Huang
• Garrison Cottrell

Shapelets are discriminative subsequences for time series classification. Recently, learning time-series shapelets (LTS) was proposed to learn shapelets by gradient descent directly. Although learning-based shapelet methods achieve better results than previous methods, they still have two shortcomings. First, the learned shapelets are fixed after training and cannot adapt to time series with deformations at the testing phase. Second, the shapelets learned by back-propagation may not be similar to any real subsequences, which is contrary to the original intention of shapelets and reduces model interpretability. In this paper, we propose a novel shapelet learning model called Adversarial Dynamic Shapelet Networks (AD- SNs). An adversarial training strategy is employed to prevent the generated shapelets from diverging from the actual subsequences of a time series. During inference, a shapelet generator produces sample-specific shapelets, and a dynamic shapelet transformation uses the generated shapelets to extract discriminative features. Thus, ADSN can dynamically generate shapelets that are similar to the real subsequences rather than having arbitrary shapes. The proposed model has high modeling flexibility while retaining the interpretability of shapelet-based methods. Experiments conducted on extensive time series data sets show that ADSN is state-of-the-art compared to existing shapelet-based methods. The visualization analysis also shows the effectiveness of dynamic shapelet generation and adversarial training.

NeurIPS Conference 2019 Conference Paper

Learning Representations for Time Series Clustering

• Qianli Ma
• Jiawei Zheng
• Sen Li
• Gary Cottrell

Time series clustering is an essential unsupervised technique in cases when category information is not available. It has been widely applied to genome data, anomaly detection, and in general, in any domain where pattern detection is important. Although feature-based time series clustering methods are robust to noise and outliers, and can reduce the dimensionality of the data, they typically rely on domain knowledge to manually construct high-quality features. Sequence to sequence (seq2seq) models can learn representations from sequence data in an unsupervised manner by designing appropriate learning objectives, such as reconstruction and context prediction. When applying seq2seq to time series clustering, obtaining a representation that effectively represents the temporal dynamics of the sequence, multi-scale features, and good clustering properties remains a challenge. How to best improve the ability of the encoder is still an open question. Here we propose a novel unsupervised temporal representation learning model, named Deep Temporal Clustering Representation (DTCR), which integrates the temporal reconstruction and K-means objective into the seq2seq model. This approach leads to improved cluster structures and thus obtains cluster-specific temporal representations. Also, to enhance the ability of encoder, we propose a fake-sample generation strategy and auxiliary classification task. Experiments conducted on extensive time series datasets show that DTCR is state-of-the-art compared to existing methods. The visualization analysis not only shows the effectiveness of cluster-specific representation but also shows the learning process is robust, even if K-means makes mistakes.

AAAI Conference 2017 Conference Paper

Multi-Kernel Low-Rank Dictionary Pair Learning for Multiple Features Based Image Classification

• Xiaoke Zhu
• Xiao-Yuan Jing
• Fei Wu
• Di Wu
• Li Cheng
• Sen Li
• Ruimin Hu

Dictionary learning (DL) is an effective feature learning technique, and has led to interesting results in many classification tasks. Recently, by combining DL with multiple kernel learning (which is a crucial and effective technique for combining different feature representation information), a few multi-kernel DL methods have been presented to solve the multiple feature representations based classification problem. However, how to improve the representation capability and discriminability of multi-kernel dictionary has not been well studied. In this paper, we propose a novel multi-kernel DL approach, named multi-kernel low-rank dictionary pair learning (MKLDPL). Specifically, MKLDPL jointly learns a kernel synthesis dictionary and a kernel analysis dictionary by exploiting the class label information. The learned synthesis and analysis dictionaries work together to implement the coding and reconstruction of samples in the kernel space. To enhance the discriminability of the learned multi-kernel dictionaries, MKLDPL imposes the low-rank regularization on the analysis dictionary, which can make samples from the same class have similar representations. We apply MKLDPL for multiple features based image classification task. Experimental results demonstrate the effectiveness of the proposed approach.