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Dong Wu

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5 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Domain-Specific Pruning of Large Mixture-of-Experts Models with Few-shot Demonstrations

Zican Dong
Han Peng
Peiyu Liu
Xin Zhao
Dong Wu
Feng Xiao
Zhifeng Wang

Mixture-of-Experts (MoE) models achieve a favorable trade-off between performance and inference efficiency by activating only a subset of experts. However, the memory overhead of storing all experts remains a major limitation, especially in large-scale MoE models such as DeepSeek-R1 (671B). In this study, we investigate domain specialization and expert redundancy in large-scale MoE models and uncover a consistent behavior we term~\emph{few-shot expert localization}, with only a few in-domain demonstrations, the model consistently activates a sparse and stable subset of experts on tasks within the same domain. Building on this observation, we propose a simple yet effective pruning framework, \textbf{EASY-EP}, that leverages a few domain-specific demonstrations to identify and retain only the most relevant experts. EASY-EP comprises two key components: \textbf{output-aware expert importance assessment} and \textbf{expert-level token contribution estimation}. The former evaluates the importance of each expert for the current token by considering the gating scores and L2 norm of the outputs of activated experts, while the latter assesses the contribution of tokens based on representation similarities before and after routed experts. Experiments on DeepSeek-R1 and DeepSeek-V3-0324 show that our method can achieve comparable performances and $2. 99\times$ throughput under the same memory budget as the full model, with only half the experts. Our code is available at https: //github. com/RUCAIBox/EASYEP.

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NeurIPS Conference 2025 Conference Paper

Efficient Utility-Preserving Machine Unlearning with Implicit Gradient Surgery

Shiji Zhou
Tianbai Yu
Zhi Zhang
Heng Chang
Xiao Zhou
Dong Wu
Han Zhao

Machine unlearning (MU) aims to efficiently remove sensitive or harmful memory from a pre-trained model. The key challenge is to balance the potential tradeoff between unlearning efficacy and utility preservation, which involves forgetting undesirable information as defined while maintaining the model's original performance. One potential way to tackle this problem is to use multi-objective optimization to jointly optimize both the unlearning and utility preservation objectives. However, existing multi-objective methods only guarantee finding a Pareto-optimal solution without fine-grained control, which causes under-optimization of the unlearning objective. To this end, we first model MU as a constrained optimization problem, that is, optimizing the unlearning objective under the constraint of a bounded increase for utility loss. We then show that solving this optimization problem is equivalent to unilateral gradient surgery on the unlearning objective. To resolve the additional computational cost brought by gradient surgery, we propose an implicit gradient surgery method, which approximates the solution to the aforementioned constrained optimization problem via only one backpropagation, thereby achieving efficient utility-preserving MU. Theoretically, we provide a tight convergence analysis of the algorithm. Empirically, our extensive experiments show that the proposed algorithm achieves better tradeoff results than existing baselines. Codes are available at https: //github. com/anseryuer/EUPMU-Efficient-Utility-Preserving-Machine-Unlearning.

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AAAI Conference 2024 Conference Paper

Solving Spectrum Unmixing as a Multi-Task Bayesian Inverse Problem with Latent Factors for Endmember Variability

Dong Wu
Mingmin Chi
Xuan Zang
Bo Peng

With the increasing customization of spectrometers, spectral unmixing has become a widely used technique in fields such as remote sensing, textiles, and environmental protection. However, endmember variability is a common issue for unmixing, where changes in lighting, atmospheric, temporal conditions, or the intrinsic spectral characteristics of materials, can all result in variations in the measured spectrum. Recent studies have employed deep neural networks to tackle endmember variability. However, these approaches rely on generic networks to implicitly resolve the issue, which struggles with the ill-posed nature and lack of effective convergence constraints for endmember variability. This paper proposes a streamlined multi-task learning model to rectify this problem, incorporating abundance regression and multi-label classification with Unmixing as a Bayesian Inverse Problem, denoted as BIPU. To address the issue of the ill-posed nature, the uncertainty of unmixing is quantified and minimized through the Laplace approximation in a Bayesian inverse solver. In addition, to improve convergence under the influence of endmember variability, the paper introduces two types of constraints. The first separates background factors of variants from the initial factors for each endmember, while the second identifies and eliminates the influence of non-existent endmembers via multi-label classification during convergence. The effectiveness of this model is demonstrated not only on a self-collected near-infrared spectral textile dataset (FENIR), but also on three commonly used remote sensing hyperspectral image datasets, where it achieves state-of-the-art unmixing performance and exhibits strong generalization capabilities.

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JBHI Journal 2022 Journal Article

Flexible Dual-Channel Digital Auscultation Patch With Active Noise Reduction for Bowel Sound Monitoring and Application

Gang Wang
Yingyun Yang
Siyu Chen
Ji Fu
Dong Wu
Aiming Yang
Yinji Ma
Xue Feng

Bowel sounds (BSs) have important clinical value in the auxiliary diagnosis of digestive diseases, but due to the inconvenience of long-term monitoring and too much interference from environmental noise, they have not been well studied. Most of the current electronic stethoscopes are hard and bulky without the function of noise reduction, and their application for long-term wearable monitoring of BS in noisy clinical environments is very limited. In this paper, a flexible dual-channel digital auscultation patch with active noise reduction is designed and developed, which is wireless, wearable, and conformably attached to abdominal skin to record BS more accurately. The ambient noise can be greatly reduced through active noise reduction based on the adaptive filter. At the same time, some nonstationary noises appearing intermittently (e. g. , frictional noise) can also be removed from BS by the cross validation of multichannel simultaneous acquisition. Then, two kinds of typical BS signals are taken as examples, and the feature parameters of the BS in the time domain and frequency domain are extracted through the time-frequency analysis algorithm. Furthermore, based on the short-term energy ratio between the four channels of dual patches, the two-dimensional localization of BS on the abdomen mapping plane is realized. Finally, the continuous wearable monitoring of BS for patients with postoperative ileus (POI) in the noisy ward from pre-operation (POD0) to postoperative Day 7 (POD7) was carried out. The obtained change curve of the occurrence frequency of BS provides guidance for doctors to choose a reasonable feeding time for patients after surgery and accelerate their recovery. Therefore, flexible dual-channel digital auscultation patches with active noise reduction will have promising applications in the clinical auxiliary diagnosis of digestive diseases.

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IROS Conference 2015 Conference Paper

Effect of vibrotactile cues for guiding simultaneous procedural motion of two joints on upper limbs

Mu Xu
Dangxiao Wang
Yuru Zhang
Dong Wu

Simultaneous motion control of multiple joints has many potential applications such as Tai Chi, Yoga etc. The capability of vibrotactile cues to assist this kind of motor task has not been well explored. In this paper, we studied the effect of vibrotactile cues for guiding procedural motion of two joints on human's upper limbs. By mounting eight vibrotactile motors on two arms, we performed two experiments to measure human's perception and motor performance in response to the vibrotactile commands. In the first experiment, we measured perceptual performance of correctly identifying the location of two active vibrotactile cues. The difference between sustained, pulsed and hybrid vibration conditions was compared. To explore the possible reasons leading to the wrong perception results, the correct rate was ranked among different combinations of cues. In the second experiment, the correct rate of procedural motion control of two joints was measured, while vibrotactile cues were used as guidance signals to produce the motion command. The results showed that average correct rate of two cues localization on upper limbs was as high as 98%, while the average correct rate of procedural motion control of two joints was only 86%. Further analysis revealed that low correct rate of procedural motion control was caused by the unnatural motion pattern, i. e. two joints on a same arm and rotate in opposite directions.

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