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Ya Wang

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13 papers

2 author rows

TMLR Journal 2026 Journal Article

Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks

Ya Wang
Adrian Paschke

Concept Bottleneck Models (CBMs) enhance interpretability by projecting learned features into a human-understandable concept space. Recent approaches leverage vision-language models to generate concept embeddings, reducing the need for manual concept annotations. However, these models suffer from a critical limitation: as the number of concepts approaches the embedding dimension, information leakage increases, enabling the model to exploit spurious or semantically irrelevant correlations and undermining interpretability. In this work, we propose Concept Flow Models (CFMs), which replace the flat bottleneck with a hierarchical, concept-driven decision tree. Each internal node in the hierarchy focuses on a localized subset of discriminative concepts, progressively narrowing the prediction scope. Our framework automatically constructs decision hierarchies from visual embeddings, distributes semantic concepts at each hierarchy level, and trains differentiable concept weights through probabilistic tree traversal. Extensive experiments on diverse benchmarks demonstrate that CFMs match the predictive performance of flat CBMs, while substantially reducing effective concept usage and information leakage. Furthermore, CFMs yield stepwise decision flows that enable transparent and auditable model reasoning.

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YNIMG Journal 2026 Journal Article

Neural correlates of autobiographical memory deficits across psychiatric disorders: A systematic review and meta-analysis

Xu-chen Yu
Wan-ting Ran
Gui-fang Chen
Tao Chen
Ji-fang Cui
Ya Wang
Raymond C.K. Chan

Dysfunction of autobiographical memory (AM) is one of the core markers of psychiatric disorders such as major depressive disorder and schizophrenia. However, it remains unclear whether there is a common neural basis underlying AM impairment across psychiatric patients. In this study, a systematic review and meta-analysis using both Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) and Activation Likelihood Estimation (ALE) approaches were conducted to examine brain activation differences between psychiatric populations and healthy controls during AM. A computerized search was performed using the databases Web of Science, PubMed, APA PsycInfo and EBSCO to identify relevant studies published from inception to 31 October 2025. Twenty-four studies (1385 participants) were identified for the qualitative synthesis and 12 studies (547 participants) for the meta-analysis. The qualitative analysis revealed widespread abnormalities in psychiatric patients in both activation and functional connectivity (FC) across default mode network, salience and attentional network, control network and visual network. Meta-analysis results indicated that patients with psychiatric disorders exhibited hyperactivations in the cingulate cortex, and subsequent meta-analytic connectivity modeling (MACM) analysis demonstrated its widespread co-activation with large-scale functional networks. These findings suggest the network-level dysfunction across psychiatric disorders during AM process and provide insights for future clinical research.

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JMLR Journal 2026 Journal Article

Two-way Node Popularity Model for Directed and Bipartite Networks

Bing-Yi Jing
Ting Li
Jiangzhou Wang
Ya Wang

There has been increasing research attention on community detection in directed and bipartite networks. However, these studies often fail to consider the popularity of nodes in different communities, which is a common phenomenon in real-world networks. To address this issue, we propose a new probabilistic framework called the Two-Way Node Popularity Model (TNPM). The TNPM also accommodates edges from different distributions within a general sub-Gaussian family. We introduce the Delete-One-Method (DOM) for model fitting and community structure identification, and provide a comprehensive theoretical analysis with novel technical skills dealing with sub-Gaussian generalization. Additionally, we propose the Two-Stage Divided Cosine Algorithm (TSDC) to handle large-scale networks more efficiently. Our proposed methods offer multi-folded advantages in terms of estimation accuracy and computational efficiency, as demonstrated through extensive numerical studies. We apply our methods to two real-world applications, uncovering interesting findings. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2026. ( edit, beta )

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NeurIPS Conference 2025 Conference Paper

HybridNorm: Towards Stable and Efficient Transformer Training via Hybrid Normalization

Zhijian Zhuo
Yutao Zeng
Ya Wang
Sijun Zhang
Xiaoqing Li
Jian Yang
zhou Xun
Jinwen Ma

Transformers have become the de facto architecture for a wide range of machine learning tasks, particularly in large language models (LLMs). Despite their remarkable performance, many challenges remain in training deep transformer networks, especially regarding the position of the layer normalization. While Pre-Norm structures facilitate more stable training owing to their stronger identity path, they often lead to suboptimal performance compared to Post-Norm. In this paper, we propose HybridNorm, a simple yet effective hybrid normalization strategy that integrates the advantages of both Pre-Norm and Post-Norm. Specifically, HybridNorm employs QKV normalization within the attention mechanism and Post-Norm in the feed-forward network (FFN) of each transformer block. We provide both theoretical insights and empirical evidence to demonstrate that HybridNorm improves the gradient flow and the model robustness. Extensive experiments on large-scale transformer models, including both dense and sparse variants, show that HybridNorm consistently outperforms both Pre-Norm and Post-Norm approaches across multiple benchmarks. These findings highlight the potential of HybridNorm as a more stable and effective technique for improving the training and performance of deep transformer models. Code is available at https: //github. com/BryceZhuo/HybridNorm.

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ICML Conference 2025 Conference Paper

Over-Tokenized Transformer: Vocabulary is Generally Worth Scaling

Hongzhi Huang
Defa Zhu
Banggu Wu
Yutao Zeng
Ya Wang
Qiyang Min
Xun Zhou

Tokenization is a fundamental component of large language models (LLMs), yet its influence on model scaling and performance is not fully explored. In this paper, we introduce Over-Tokenized Transformers, a novel framework that decouples input and output vocabularies to improve language modeling performance. Specifically, our approach scales up input vocabularies to leverage multi-gram tokens. Through extensive experiments, we uncover a log-linear relationship between input vocabulary size and training loss, demonstrating that larger input vocabularies consistently enhance model performance, regardless of model size. Using a large input vocabulary, we achieve performance comparable to double-sized baselines with no additional cost. Our findings highlight the importance of tokenization in scaling laws and provide practical insight for tokenizer design, paving the way for more efficient and powerful LLMs.

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IJCAI Conference 2025 Conference Paper

Visual Perturbation and Adaptive Hard Negative Contrastive Learning for Compositional Reasoning in Vision-Language Models

Xin Huang
Ruibin Li
Tong Jia
Wei Zheng
Ya Wang

Vision-Language Models (VLMs) are essential for multimodal tasks, especially compositional reasoning (CR) tasks, which require distinguishing fine-grained semantic differences between visual and textual embeddings. However, existing methods primarily fine-tune the model by generating text-based hard negative samples, neglecting the importance of image-based negative samples, which results in insufficient training of the visual encoder and ultimately impacts the overall performance of the model. Moreover, negative samples are typically treated uniformly, without considering their difficulty levels, and the alignment of positive samples is insufficient, which leads to challenges in aligning difficult sample pairs. To address these issues, we propose Adaptive Hard Negative Perturbation Learning (AHNPL). AHNPL translates text-based hard negatives into the visual domain to generate semantically disturbed image-based negatives for training the model, thereby enhancing its overall performance. AHNPL also introduces a contrastive learning approach using a multimodal hard negative loss to improve the model's discrimination of hard negatives within each modality and a dynamic margin loss that adjusts the contrastive margin according to sample difficulty to enhance the distinction of challenging sample pairs. Experiments on three public datasets demonstrate that our method effectively boosts VLMs' performance on complex CR tasks. The source code is available at https: //github. com/nynu-BDAI/AHNPL.

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YNIMG Journal 2023 Journal Article

An attention-based context-informed deep framework for infant brain subcortical segmentation

Liangjun Chen
Zhengwang Wu
Fenqiang Zhao
Ya Wang
Weili Lin
Li Wang
Gang Li

Precise segmentation of subcortical structures from infant brain magnetic resonance (MR) images plays an essential role in studying early subcortical structural and functional developmental patterns and diagnosis of related brain disorders. However, due to the dynamic appearance changes, low tissue contrast, and tiny subcortical size in infant brain MR images, infant subcortical segmentation is a challenging task. In this paper, we propose a context-guided, attention-based, coarse-to-fine deep framework to precisely segment the infant subcortical structures. At the coarse stage, we aim to directly predict the signed distance maps (SDMs) from multi-modal intensity images, including T1w, T2w, and the ratio of T1w and T2w images, with an SDM-Unet, which can leverage the spatial context information, including the structural position information and the shape information of the target structure, to generate high-quality SDMs. At the fine stage, the predicted SDMs, which encode spatial-context information of each subcortical structure, are integrated with the multi-modal intensity images as the input to a multi-source and multi-path attention Unet (M2A-Unet) for achieving refined segmentation. Both the 3D spatial and channel attention blocks are added to guide the M2A-Unet to focus more on the important subregions and channels. We additionally incorporate the inner and outer subcortical boundaries as extra labels to help precisely estimate the ambiguous boundaries. We validate our method on an infant MR image dataset and on an unrelated neonatal MR image dataset. Compared to eleven state-of-the-art methods, the proposed framework consistently achieves higher segmentation accuracy in both qualitative and quantitative evaluations of infant MR images and also exhibits good generalizability in the neonatal dataset.

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YNIMG Journal 2022 Journal Article

A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort

Liangjun Chen
Zhengwang Wu
Dan Hu
Ya Wang
Fenqiang Zhao
Tao Zhong
Weili Lin
Li Wang

Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain MR images, 4D atlases densely covering the dynamic brain development during infancy are still scarce. Few existing ones generally have fuzzy tissue contrast and low spatiotemporal resolution, leading to degraded accuracy of atlas-based normalization and subsequent analyses. To address this issue, in this paper, we construct a 4D structural MRI atlas for infant brains based on the UNC/UMN Baby Connectome Project (BCP) dataset, which features a high spatial resolution, extensive age-range coverage, and densely sampled time points. Specifically, 542 longitudinal T1w and T2w scans from 240 typically developing infants up to 26-month of age were utilized for our atlas construction. To improve the co-registration accuracy of the infant brain images, which typically exhibit dynamic appearance with low tissue contrast, we employed the state-of-the-art registration method and leveraged our generated reliable brain tissue probability maps in addition to the intensity images to improve the alignment of individual images. To achieve consistent region labeling on both infant and adult brain images for facilitating region-based analysis across ages, we mapped the widely used Desikan cortical parcellation onto our atlas by following an age-decreasing mapping manner. Meanwhile, the typical subcortical structures were manually delineated to facilitate the studies related to the subcortex. Compared with the existing infant brain atlases, our 4D atlas has much higher spatiotemporal resolution and preserves more structural details, and thus can boost accuracy in neurodevelopmental analysis during infancy.

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YNIMG Journal 2022 Journal Article

Longitudinal brain atlases of early developing cynomolgus macaques from birth to 48 months of age

Tao Zhong
Jingkuan Wei
Kunhua Wu
Liangjun Chen
Fenqiang Zhao
Yuchen Pei
Ya Wang
Hongjiang Zhang

Longitudinal brain imaging atlases with densely sampled time-points and ancillary anatomical information are of fundamental importance in studying early developmental characteristics of human and non-human primate brains during infancy, which feature extremely dynamic imaging appearance, brain shape and size. However, for non-human primates, which are highly valuable animal models for understanding human brains, the existing brain atlases are mainly developed based on adults or adolescents, denoting a notable lack of temporally densely-sampled atlases covering the dynamic early brain development. To fill this critical gap, in this paper, we construct a comprehensive set of longitudinal brain atlases and associated tissue probability maps (gray matter, white matter, and cerebrospinal fluid) with totally 12 time-points from birth to 4 years of age (i.e., 1, 2, 3, 4, 5, 6, 9, 12, 18, 24, 36, and 48 months of age) based on 175 longitudinal structural MRI scans from 39 typically-developing cynomolgus macaques, by leveraging state-of-the-art computational techniques tailored for early developing brains. Furthermore, to facilitate region-based analysis using our atlases, we also provide two popular hierarchy parcellations, i.e., cortical hierarchy maps (6 levels) and subcortical hierarchy maps (6 levels), on our longitudinal macaque brain atlases. These early developing atlases, which have the densest time-points during infancy (to the best of our knowledge), will greatly facilitate the studies of macaque brain development.

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AAAI Conference 2020 Conference Paper

Multi-Label Classification with Label Graph Superimposing

Ya Wang
Dongliang He
Fu Li
Xiang Long
Zhichao Zhou
Jinwen Ma
Shilei Wen

Images or videos always contain multiple objects or actions. Multi-label recognition has been witnessed to achieve pretty performance attribute to the rapid development of deep learning technologies. Recently, graph convolution network (GCN) is leveraged to boost the performance of multi-label recognition. However, what is the best way for label correlation modeling and how feature learning can be improved with label system awareness are still unclear. In this paper, we propose a label graph superimposing framework to improve the conventional GCN+CNN framework developed for multi-label recognition in the following two aspects. Firstly, we model the label correlations by superimposing label graph built from statistical co-occurrence information into the graph constructed from knowledge priors of labels, and then multilayer graph convolutions are applied on the ﬁnal superimposed graph for label embedding abstraction. Secondly, we propose to leverage embedding of the whole label system for better representation learning. In detail, lateral connections between GCN and CNN are added at shallow, middle and deep layers to inject information of label system into backbone CNN for label-awareness in the feature learning process. Extensive experiments are carried out on MS- COCO and Charades datasets, showing that our proposed solution can greatly improve the recognition performance and achieves new state-of-the-art recognition performance.

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IROS Conference 2019 Conference Paper

Accelerated Visual Inertial Navigation via Fragmented Structure Updates

Yehonathan Litman
Ya Wang
Ji Liu

Tightly coupled Visual-Inertial Navigation System (VINS) implementations have proven their superiority due to their ability to jointly optimize all state variables. However, this joint optimization is considered as a computational bottleneck within the system, and thus many traditional VINS can only be implemented on platforms containing powerful processors. In this work, we show a significant reduction in the computational burden of optimization can be achieved through the use of fragments; a set of co-visible feature points from across several different cameras efficiently split, categorized into groups, and then analyzed using a machine-learning inspired frequent-pattern growth algorithm. Furthermore, we use a reduced continuous representation during preintegration for better accuracy while requiring less computational resources. We validate our algorithm in datasets, showing that the derivation is not only more accurate, but requires significantly less computational resources. When tested on a Raspberry Pi, our implementation was able to track the system’s state in nearly 20 frames per second using only a single CPU core. Testing on another low power module shows a power draw of around 800 mW. Due to run-time considerations the Raspberry Pi was only competitive in regards to other optimization methodologies, but our algorithm displays remarkable accuracy on a more powerful platform.

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ICRA Conference 2017 Conference Paper

Design and implementation of a quadrotor tail-sitter VTOL UAV

Ximin Lyu
Haowei Gu
Ya Wang
Zexiang Li 0001
Shaojie Shen
Fu Zhang 0002

We present the design and implementation of a quadrotor tail-sitter Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV). The VTOL UAV combines the advantage of a quadrotor, vertical take-off and landing and hovering at a stationary point, with that of a fixed-wing, efficient level flight. We describe our vehicle design with special considerations on fully autonomous operation in a real outdoor environment where the wind is present. The designed quadrotor tail-sitter UAV has insignificant vibration level and achieves stable hovering and landing performance when a cross wind is present. Wind tunnel test is conducted to characterize the full envelope aerodynamics of the aircraft, based on which a flight controller is designed, implemented and tested. MATLAB simulation is presented and shows that our vehicle can achieve a continuous transition from hover flight to level flight. Finally, both indoor and outdoor flight experiments are conducted to verify the performance of our vehicle and the designed controller.

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YNICL Journal 2015 Journal Article

Prefrontal cortex connectivity dysfunction in performing the Fist–Edge–Palm task in patients with first-episode schizophrenia and non-psychotic first-degree relatives

Raymond C.K. Chan
Jia Huang
Qing Zhao
Ya Wang
Yun-yao Lai
Nan Hong
David H.K. Shum
Eric F.C. Cheung

Neurological soft signs have been considered one of the promising neurological endophenotypes for schizophrenia. However, most previous studies have employed clinical rating data only. The present study aimed to examine the neurobiological basis of one of the typical motor coordination signs, the Fist-Edge-Palm (FEP) task, in patients with first-episode schizophrenia and their non-psychotic first degree relatives. Thirteen patients with first-episode schizophrenia, 14 non-psychotic first-degree relatives and 14 healthy controls were recruited. All of them were instructed to perform the FEP task in a 3 T GE Machine. Psychophysiological interaction (PPI) analysis was used to evaluate the functional connectivity between the sensorimotor cortex and frontal regions when participants performed the FEP task compared to simple motor tasks. In the contrast of palm-tapping (PT) vs. rest, activation of the left frontal-parietal region was lowest in the schizophrenia group, intermediate in the relative group and highest in the healthy control group. In the contrast of FEP vs. PT, patients with schizophrenia did not show areas of significant activation, while relatives and healthy controls showed significant activation of the left middle frontal gyrus. Moreover, with the increase in task complexity, significant functional connectivity was observed between the sensorimotor cortex and the right frontal gyrus in healthy controls but not in patients with first episode schizophrenia. These findings suggest that activity of the left frontal-parietal and frontal regions may be neurofunctional correlates of neurological soft signs, which in turn may be a potential endophenotype of schizophrenia. Moreover, the right frontal gyrus may play a specific role in the execution of the FEP task in schizophrenia spectrum disorders.

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