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Xiaohui Gao

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3 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Jacobian-Based Interpretation of Nonlinear Neural Encoding Model

Haoran Yang
cheng yue
Mengfei Zuo
Yiheng Liu
Peiyang Li
Xiaohui Gao

In recent years, the alignment between artificial neural network (ANN) embeddings and blood oxygenation level dependent (BOLD) responses in functional magnetic resonance imaging (fMRI) via neural encoding models has significantly advanced research on neural representation mechanisms and interpretability in the brain. However, these approaches remain limited in characterizing the brain’s inherently nonlinear response properties. To address this, we propose the Jacobian-based Nonlinearity Evaluation (JNE), an interpretability metric for nonlinear neural encoding models. JNE quantifies nonlinearity by statistically measuring the dispersion of local linear mappings (Jacobians) from model representations to predicted BOLD responses, thereby approximating the nonlinearity of BOLD signals. Centered on proposing JNE as a novel interpretability metric, we validated its effectiveness through controlled simulation experiments on various activation functions and network architectures, and further verified it on real fMRI data, demonstrating a hierarchical progression of nonlinear characteristics from primary to higher-order visual cortices, consistent with established cortical organization. We further extended JNE with Sample-Specificity (JNE-SS), revealing stimulus-selective nonlinear response patterns in functionally specialized brain regions. As the first interpretability metric for quantifying nonlinear responses, JNE provides new insights into brain information processing. Code available at https: //github. com/Gaitxh/JNE.

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AAAI Conference 2025 Conference Paper

LCGC: Learning from Consistency Gradient Conflicting for Class-Imbalanced Semi-Supervised Debiasing

Weiwei Xing
Yue Cheng
Hongzhu Yi
Xiaohui Gao
Xiang Wei
Xiaoyu Guo
Yumin Zhang
Xinyu Pang

Classifiers often learn to be biased corresponding to the class-imbalanced dataset under the semi-supervised learning (SSL) set. While previous work tries to appropriately re-balance the classifiers by subtracting a class-irrelevant image's logit, we further utilize a cheaper form of consistency gradients, which can be widely applicable to various class-imbalanced SSL (CISSL) models. We theoretically analyze that the process of refining pseudo-labels with a baseline image (solid color image without any patterns) in the basic SSL algorithm implicitly utilizes integrated gradient flow training, which can improve the attribution ability. Based on the analysis, we propose a consistently conflicting gradient-based debiasing scheme dubbed LCGC, by encouraging biased class predictions during training. We intentionally update the pseudo-labels whose gradient conflicts with the debiased logits, which is represented as the optimization direction offered by the over-imbalanced classifier predictions. Then, we debias the predictions by subtraction the baseline image logits during testing. Extensive experiments demonstrate that our method can significantly improve the prediction accuracy of existing CISSL models on public benchmarks.

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ICRA Conference 2004 Conference Paper

Calibrating Human Hand for Teleoperating the HIT/DLR Hand

Haiying Hu
Xiaohui Gao
Jiawei Li
Jie Wang
Hong Liu 0002

Using human action to guide robot execution can greatly reduce the planning complexity. We calibrate a human hand model and map its motion to a four-finger dexterous robot hand. The parameters of human hand model are determined by open-loop kinematic calibration method based on a vision system. We analyze the kinematic difference between the human hand and dexterous robot hand, and present a modified fingertip mapping to solve the partial overlap of the fingertip workspaces. 3D graphic simulation and manipulation experiments show that the accuracy of the human hand model and the mapping method are sufficiently precise for teleoperation tasks.

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