Jiaxing Wang

IROS Conference 2025 Conference Paper

Spherical Scissor-Like Reconfigurable Palm Design in Robotic Hands: Insights from Human Hand Functionality

• Jiaxing Wang
• Fang Zhang
• Kai Chen 0026
• Chang Liu 0030
• Guo-Niu Zhu
• Qiujie Lu
• Zhongxue Gan

The human palm demonstrates spatial reconfigurability during the gripping process and forms a spherical grasping envelope. Based on these observations, this study designs a reconfigurable spherical palm that incorporates a spatial scissor mechanism, which only requires a single actuator to reshape the palm into a range of spherical forms. We conduct a kinematic analysis and modelling of the structure, abstracting three key parameters and analysing their influence on the motion characteristics of the palm. Through multi-objective optimisation, a set of dimensional parameters is derived to balance workspace, human-like motion, and mechanical performance. The performance of the reconfigurability and the grasping capability of the proposed palm is compared to a planar folding palm by superquadrics, and the results show that the spherical design and the reconfigurable characteristics provide larger grasping arrangement and stronger grasping capability of the palm on most of the testing surfaces.

NeurIPS Conference 2025 Conference Paper

TANDEM: Bi-Level Data Mixture Optimization with Twin Networks

• Jiaxing Wang
• Deping Xiang
• Jin Xu
• Mingyang Yi
• Guoqiang Gong
• Zicheng Zhang
• Haoran Li
• Pengzhang Liu

The capabilities of large language models (LLMs) significantly depend on training data drawn from various domains. Optimizing domain-specific mixture ratios can be modeled as a bi-level optimization problem, which we simplify into a single-level penalized form and solve with twin networks: a proxy model trained on primary data and a dynamically updated reference model trained with additional data. Our proposed method, Twin Networks for bi-level DatA mixturE optiMization (TANDEM), measures the data efficacy through the difference between the twin models and up-weights domains that benefit more from the additional data. TANDEM provides theoretical guarantees and wider applicability, compared to prior approaches. Furthermore, our bi-level perspective suggests new settings to study domain reweighting such as data-restricted scenarios and supervised fine-tuning, where optimized mixture ratios significantly improve the performance. Extensive experiments validate TANDEM's effectiveness in all scenarios.

NeurIPS Conference 2025 Conference Paper

The Primacy of Magnitude in Low-Rank Adaptation

• Zicheng Zhang
• Haoran Li
• Yifeng Zhang
• Guoqiang Gong
• Jiaxing Wang
• Junxing Hu
• Pengzhang Liu
• Qixia Jiang

Low-Rank Adaptation (LoRA) offers a parameter-efficient paradigm for tuning large models. While recent spectral initialization methods improve convergence and performance over the naive “Noise & Zeros” scheme, their extra computational and storage overhead undermines efficiency. In this paper, we establish update magnitude as the fundamental driver of LoRA performance and propose LoRAM, a magnitude-driven “Basis & Basis” initialization scheme that matches spectral methods without their inefficiencies. Our key contributions are threefold: (i) Magnitude of weight updates determines convergence. We prove low-rank structures intrinsically bound update magnitudes, unifying hyperparameter tuning in learning rate, scaling factor, and initialization as mechanisms to optimize magnitude regulation. (ii) Spectral initialization succeeds via magnitude amplification. We demystify that the presumed knowledge-driven benefit of spectral component essentially arises from the boost in the weight update magnitude. (iii) A novel and compact initialization strategy, LoRAM, scales deterministic orthogonal bases using pretrained weight magnitudes to simulate spectral gains. Extensive experiments show that LoRAM serves as a strong baseline, retaining the full efficiency of LoRA while matching or outperforming spectral initialization across benchmarks.

AAAI Conference 2024 Conference Paper

Patch-Aware Sample Selection for Efficient Masked Image Modeling

• Zhengyang Zhuge
• Jiaxing Wang
• Yong Li
• Yongjun Bao
• Peisong Wang
• Jian Cheng

Nowadays sample selection is drawing increasing attention. By extracting and training only on the most informative subset, sample selection can effectively reduce the training cost. Although sample selection is effective in conventional supervised learning, applying it to Masked Image Modeling (MIM) still poses challenges due to the gap between sample-level selection and patch-level pre-training. In this paper, we inspect the sample selection in MIM pre-training and find the basic selection suffers from performance degradation. We attribute this degradation primarily to 2 factors: the random mask strategy and the simple averaging function. We then propose Patch-Aware Sample Selection (PASS), including a low-cost Dynamic Trained Mask Predictor (DTMP) and Weighted Selection Score (WSS). DTMP consistently masks the informative patches in samples, ensuring a relatively accurate representation of selection score. WSS enhances the selection score using patch-level disparity. Extensive experiments show the effectiveness of PASS in selecting the most informative subset and accelerating pretraining. PASS exhibits superior performance across various datasets, MIM methods, and downstream tasks. Particularly, PASS improves MAE by 0.7% on ImageNet-1K while utilizing only 37% data budget and achieves ~1.7x speedup.

ICLR Conference 2023 Conference Paper

DynaMS: Dyanmic Margin Selection for Efficient Deep Learning

• Jiaxing Wang
• Yong Li
• Jingwei Zhuo
• Xupeng Shi
• Weizhong Zhang
• Lixing Gong
• Tong Tao
• Pengzhang Liu

The great success of deep learning is largely driven by training over-parameterized models on massive datasets. To avoid excessive computation, extracting and training only on the most informative subset is drawing increasing attention. Nevertheless, it is still an open question how to select such a subset on which the model trained generalizes on par with the full data. In this paper, we propose dynamic margin selection (DynaMS). DynaMS leverages the distance from candidate samples to the classification boundary to construct the subset, and the subset is dynamically updated during model training. We show that DynaMS converges with large probability, and for the first time show both in theory and practice that dynamically updating the subset can result in better generalization over previous works. To reduce the additional computation incurred by the selection, a light parameter sharing proxy (PSP) is designed. PSP is able to faithfully evaluate instances with respect to the current model, which is necessary for dynamic selection. Extensive analysis and experiments demonstrate the superiority of the proposed approach in data selection against many state-of-the-art counterparts on benchmark datasets.

AAAI Conference 2022 Conference Paper

DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy

• Anda Cheng
• Jiaxing Wang
• Xi Sheryl Zhang
• Qiang Chen
• Peisong Wang
• Jian Cheng

Training deep neural networks (DNNs) for meaningful differential privacy (DP) guarantees severely degrades model utility. In this paper, we demonstrate that the architecture of DNNs has a significant impact on model utility in the context of private deep learning, whereas its effect is largely unexplored in previous studies. In light of this missing, we propose the very first framework that employs neural architecture search to automatic model design for private deep learning, dubbed as DPNAS. To integrate private learning with architecture search, we delicately design a novel search space and propose a DP-aware method for training candidate models. We empirically certify the effectiveness of the proposed framework. The searched model DPNASNet achieves state-of-theart privacy/utility trade-offs, e. g. , for the privacy budget of (, δ) = (3, 1 × 10−5 ), our model obtains test accuracy of 98. 57% on MNIST, 88. 09% on FashionMNIST, and 68. 33% on CIFAR-10. Furthermore, by studying the generated architectures, we provide several intriguing findings of designing private-learning-friendly DNNs, which can shed new light on model design for deep learning with differential privacy.

IJCAI Conference 2020 Conference Paper

Exploring Parameter Space with Structured Noise for Meta-Reinforcement Learning

• Hui Xu
• Chong Zhang
• Jiaxing Wang
• Deqiang Ouyang
• Yu Zheng
• Jie Shao

Efficient exploration is a major challenge in Reinforcement Learning (RL) and has been studied extensively. However, for a new task existing methods explore either by taking actions that maximize task agnostic objectives (such as information gain) or applying a simple dithering strategy (such as noise injection), which might not be effective enough. In this paper, we investigate whether previous learning experiences can be leveraged to guide exploration of current new task. To this end, we propose a novel Exploration with Structured Noise in Parameter Space (ESNPS) approach. ESNPS utilizes meta-learning and directly uses meta-policy parameters, which contain prior knowledge, as structured noises to perturb the base model for effective exploration in new tasks. Experimental results on four groups of tasks: cheetah velocity, cheetah direction, ant velocity and ant direction demonstrate the superiority of ESNPS against a number of competitive baselines.

IJCAI Conference 2020 Conference Paper

Learning Regional Attention Convolutional Neural Network for Motion Intention Recognition Based on EEG Data

• Zhijie Fang
• Weiqun Wang
• Shixin Ren
• Jiaxing Wang
• Weiguo Shi
• Xu Liang
• Chen-Chen Fan
• Zeng-Guang Hou

Recent deep learning-based Brain-Computer Interface (BCI) decoding algorithms mainly focus on spatial-temporal features, while failing to explicitly explore spectral information which is one of the most important cues for BCI. In this paper, we propose a novel regional attention convolutional neural network (RACNN) to take full advantage of spectral-spatial-temporal features for EEG motion intention recognition. Time-frequency based analysis is adopted to reveal spectral-temporal features in terms of neural oscillations of primary sensorimotor. The basic idea of RACNN is to identify the activated area of the primary sensorimotor adaptively. The RACNN aggregates a varied number of spectral-temporal features produced by a backbone convolutional neural network into a compact fixed-length representation. Inspired by the neuroscience findings that functional asymmetry of the cerebral hemisphere, we propose a region biased loss to encourage high attention weights for the most critical regions. Extensive evaluations on two benchmark datasets and real-world BCI dataset show that our approach significantly outperforms previous methods.

AAAI Conference 2020 Conference Paper

M-NAS: Meta Neural Architecture Search

• Jiaxing Wang
• Jiaxiang Wu
• Haoli Bai
• Jian Cheng

Neural Architecture Search (NAS) has recently outperformed hand-crafted networks in various areas. However, most prevalent NAS methods only focus on a pre-defined task. For a previously unseen task, the architecture is either searched from scratch, which is inefficient, or transferred from the one obtained on some other task, which might be sub-optimal. In this paper, we investigate a previously unexplored problem: whether a universal NAS method exists, such that task-aware architectures can be effectively generated? Towards this problem, we propose Meta Neural Architecture Search (M-NAS). To obtain task-specific architectures, M-NAS adopts a taskaware architecture controller for child model generation. Since optimal weights for different tasks and architectures span diversely, we resort to meta-learning, and learn metaweights that efficiently adapt to a new task on the corresponding architecture with only several gradient descent steps. Experimental results demonstrate the superiority of M-NAS against a number of competitive baselines on both toy regression and few shot classification problems.

NeurIPS Conference 2020 Conference Paper

Revisiting Parameter Sharing for Automatic Neural Channel Number Search

• Jiaxing Wang
• Haoli Bai
• Jiaxiang Wu
• Xupeng Shi
• Junzhou Huang
• Irwin King
• Michael Lyu
• Jian Cheng

Recent advances in neural architecture search inspire many channel number search algorithms~(CNS) for convolutional neural networks. To improve searching efficiency, parameter sharing is widely applied, which reuses parameters among different channel configurations. Nevertheless, it is unclear how parameter sharing affects the searching process. In this paper, we aim at providing a better understanding and exploitation of parameter sharing for CNS. Specifically, we propose affine parameter sharing~(APS) as a general formulation to unify and quantitatively analyze existing channel search algorithms. It is found that with parameter sharing, weight updates of one architecture can simultaneously benefit other candidates. However, it also results in less confidence in choosing good architectures. We thus propose a new strategy of parameter sharing towards a better balance between training efficiency and architecture discrimination. Extensive analysis and experiments demonstrate the superiority of the proposed strategy in channel configuration against many state-of-the-art counterparts on benchmark datasets.

ICML Conference 2019 Conference Paper

RaFM: Rank-Aware Factorization Machines

• Xiaoshuang Chen
• Yin Zheng
• Jiaxing Wang
• Wenye Ma
• Junzhou Huang

Fatorization machines (FM) are a popular model class to learn pairwise interactions by a low-rank approximation. Different from existing FM-based approaches which use a fixed rank for all features, this paper proposes a Rank-Aware FM (RaFM) model which adopts pairwise interactions from embeddings with different ranks. The proposed model achieves a better performance on real-world datasets where different features have significantly varying frequencies of occurrences. Moreover, we prove that the RaFM model can be stored, evaluated, and trained as efficiently as one single FM, and under some reasonable conditions it can be even significantly more efficient than FM. RaFM improves the performance of FMs in both regression tasks and classification tasks while incurring less computational burden, therefore also has attractive potential in industrial applications.

EAAI Journal 2019 Journal Article

Workflow difference detection based on basis paths

• Bin Cao
• Feng Hong
• Jiaxing Wang
• Jing Fan
• Mingqi Lv