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Junru Chen

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6 papers

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NeurIPS Conference 2024 Conference Paper

Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification

Junru Chen
Tianyu Cao
Jing Xu
Jiahe Li
Zhilong Chen
Tao Xiao
Yang Yang

Time Series Classification (TSC) encompasses two settings: classifying entire sequences or classifying segmented subsequences. The raw time series for segmented TSC usually contain Multiple classes with Varying Duration of each class (MVD). Therefore, the characteristics of MVD pose unique challenges for segmented TSC, yet have been largely overlooked by existing works. Specifically, there exists a natural temporal dependency between consecutive instances (segments) to be classified within MVD. However, mainstream TSC models rely on the assumption of independent and identically distributed (i. i. d. ), focusing on independently modeling each segment. Additionally, annotators with varying expertise may provide inconsistent boundary labels, leading to unstable performance of noise-free TSC models. To address these challenges, we first formally demonstrate that valuable contextual information enhances the discriminative power of classification instances. Leveraging the contextual priors of MVD at both the data and label levels, we propose a novel consistency learning framework Con4m, which effectively utilizes contextual information more conducive to discriminating consecutive segments in segmented TSC tasks, while harmonizing inconsistent boundary labels for training. Extensive experiments across multiple datasets validate the effectiveness of Con4m in handling segmented TSC tasks on MVD. The source code is available at https: //github. com/MrNobodyCali/Con4m.

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NeurIPS Conference 2024 Conference Paper

DMNet: Self-comparison Driven Model for Subject-independent Seizure Detection

Shihao Tu
Linfeng Cao
Daoze Zhang
Junru Chen
Lvbin Ma
Yin Zhang
Yang Yang

Automated seizure detection (ASD) using intracranial electroencephalography (iEEG) is critical for effective epilepsy treatment. However, the significant domain shift of iEEG signals across subjects poses a major challenge, limiting their applicability in real-world clinical scenarios. In this paper, we address this issue by analyzing the primary cause behind the failure of existing iEEG models for subject-independent seizure detection, and identify a critical universal seizure pattern: seizure events consistently exhibit higher average amplitude compared to adjacent normal events. To mitigate the domain shifts and preserve the universal seizure patterns, we propose a novel self-comparison mechanism. This mechanism effectively aligns iEEG signals across subjects and time intervals. Building upon these findings, we propose Difference Matrix-based Neural Network (DMNet), a subject-independent seizure detection model, which leverages self-comparison based on two constructed (contextual, channel-level) references to mitigate shifts of iEEG, and utilize a simple yet effective difference matrix to encode the universal seizure patterns. Extensive experiments show that DMNet significantly outperforms previous SOTAs while maintaining high efficiency on a real-world clinical dataset collected by us and two public datasets for subject-independent seizure detection. Moreover, the visualization results demonstrate that the generated difference matrix can effectively capture the seizure activity changes during the seizure evolution process. Additionally, we deploy our method in an online diagnosis system to illustrate its effectiveness in real clinical applications.

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NeurIPS Conference 2024 Conference Paper

Octopus: A Multi-modal LLM with Parallel Recognition and Sequential Understanding

Chuyang Zhao
YuXin Song
Junru Chen
Kang Rong
Haocheng Feng
Gang Zhang
Shufan Ji
Jingdong Wang

A mainstream of Multi-modal Large Language Models (MLLMs) have two essential functions, i. e. , visual recognition (e. g. , grounding) and understanding (e. g. , visual question answering). Presently, all these MLLMs integrate visual recognition and understanding in a same sequential manner in the LLM head, i. e. , generating the response token-by-token for both recognition and understanding. We think unifying them in the same sequential manner is not optimal for two reasons: 1) parallel recognition is more efficient than sequential recognition and is actually prevailing in deep visual recognition, and 2) the recognition results can be integrated to help high-level cognition (while the current manner does not). Such motivated, this paper proposes a novel “parallel recognition → sequential understanding” framework for MLLMs. The bottom LLM layers are utilized for parallel recognition and the recognition results are relayed into the top LLM layers for sequential understanding. Specifically, parallel recognition in the bottom LLM layers is implemented via object queries, a popular mechanism in DEtection TRansformer, which we find to harmonize well with the LLM layers. Empirical studies show our MLLM named Octopus improves accuracy on popular MLLM tasks and is up to 5× faster on visual grounding tasks.

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NeurIPS Conference 2023 Conference Paper

Brant: Foundation Model for Intracranial Neural Signal

Daoze Zhang
Zhizhang Yuan
Yang Yang
Junru Chen
Jingjing Wang
Yafeng Li

We propose a foundation model named Brant for modeling intracranial recordings, which learns powerful representations of intracranial neural signals by pre-training, providing a large-scale, off-the-shelf model for medicine. Brant is the largest model in the field of brain signals and is pre-trained on a large corpus of intracranial data collected by us. The design of Brant is to capture long-term temporal dependency and spatial correlation from neural signals, combining the information in both time and frequency domains. As a foundation model, Brant achieves SOTA performance on various downstream tasks (i. e. neural signal forecasting, frequency-phase forecasting, imputation and seizure detection), showing the generalization ability to a broad range of tasks. The low-resource label analysis and representation visualization further illustrate the effectiveness of our pre-training strategy. In addition, we explore the effect of model size to show that a larger model with a higher capacity can lead to performance improvements on our dataset. The source code and pre-trained weights are available at: https: //zju-brainnet. github. io/Brant. github. io/.

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NeurIPS Conference 2023 Conference Paper

PPi: Pretraining Brain Signal Model for Patient-independent Seizure Detection

Zhizhang Yuan
Daoze Zhang
Yang Yang
Junru Chen
Yafeng Li

Automated seizure detection is of great importance to epilepsy diagnosis and treatment. An emerging method used in seizure detection, stereoelectroencephalography (SEEG), can provide detailed and stereoscopic brainwave information. However, modeling SEEG in clinical scenarios will face challenges like huge domain shift between different patients and dramatic pattern evolution among different brain areas. In this study, we propose a Pretraining-based model for Patient-independent seizure detection (PPi) to address these challenges. Firstly, we design two novel self-supervised tasks which can extract rich information from abundant SEEG data while preserving the unique characteristics between brain signals recorded from different brain areas. Then two techniques channel background subtraction and brain region enhancement are proposed to effectively tackle the domain shift problem. Extensive experiments show that PPi outperforms the SOTA baselines on two public datasets and a real-world clinical dataset collected by ourselves, which demonstrates the effectiveness and practicability of PPi. Finally, visualization analysis illustrates the rationality of the two domain generalization techniques.

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AAAI Conference 2022 Conference Paper

Unsupervised Adversarially Robust Representation Learning on Graphs

Jiarong Xu
Yang Yang
Junru Chen
Xin Jiang
Chunping Wang
Jiangang Lu
Yizhou Sun

Unsupervised/self-supervised pre-training methods for graph representation learning have recently attracted increasing research interests, and they can be generalized to various downstream applications. Yet, the adversarial robustness of such pre-trained graph learning models remains largely unexplored. More importantly, most existing defense techniques for endto-end graph representation learning methods require prespecified label definitions, and thus cannot be directly applied to the pre-training methods. In this paper, we propose an unsupervised defense technique to robustify pre-trained deep graph models, so that the perturbations on the input graph can be successfully identified and blocked before the model is applied to different downstream tasks. Specifically, we introduce a mutual information-based measure, graph representation vulnerability (GRV), to quantify the robustness of graph encoders on the representation space. We then formulate an optimization problem to learn the graph representation by carefully balancing the trade-off between the expressive power and the robustness (i. e. , GRV) of the graph encoder. The discrete nature of graph topology and the joint space of graph data make the optimization problem intractable to solve. To handle the above difficulty and to reduce computational expense, we further relax the problem and thus provide an approximate solution. Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks. Extensive experiments demonstrate that even without access to labels and tasks, our model is still able to enhance robustness against adversarial attacks on three downstream tasks (i. e. , node classification, link prediction, and community detection) by an average of +16. 5% compared with existing methods.

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