Portrait de Yang Wang

Yang Wang

Membre académique associé
yang.wang@concordia.ca
Professeur agrégé, Concordia University, Département d'informatique et de génie logiciel
Sujets de recherche
Apprentissage automatique appliqué
Apprentissage profond
Vision par ordinateur
Site web
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Étudiants actuels

Publications

A four-amino-acid YRIN insertion in ftsI reduced ceftiofur susceptibility in a canine Escherichia coli isolate
Xukun Dang
Zeshi Chen
Rina Bai
Junyao Jiang
Weishuai Zhai
Shizhen Ma
Lu Liu
Dejun Liu
Zhaofei Xia
Zhangqi Shen
Yang Wang
Siyu Chen
Abstract Ceftiofur resistance in canine clinical Escherichia coli is usually associated with extended-spectrum β-lactamases (ESBLs) or AmpC… (voir plus) β-lactamases. However, some isolates display elevated ceftiofur minimum inhibitory concentrations (MICs) without carrying these known resistance genes. In this study, we identified a phenotype-genotype discordant canine clinical E. coli isolate named 231255, with a ceftiofur MIC of 8 μg/mL. Routine resistance gene screening detected only bla TEM-1 and a chromosomal bla EC variant, bla EC-1149 , which could not adequately explain the elevated ceftiofur MIC. To investigate the underlying mechanism, a genomic library was constructed from genomic DNA of isolate 231255 and screened on ceftiofur-containing plates. Positive clones revealed one candidate determinant: an altered ftsI fragment encoding penicillin-binding protein 3 (PBP3) with a four-amino-acid YRIN insertion downstream of residue P333, previously identified in human E. coli . Previous studies have shown that this type of YRIN/YRIK insertion alone can reduce susceptibility to PBP3-targeting β-lactams, particularly aztreonam and ceftazidime. Functional validation showed that recombinant plasmids carrying the altered ftsI consistently increased the ceftiofur MIC to 4 μg/mL in different recipient backgrounds. These findings provide experimental evidence that ftsI /PBP3 alteration can elevate ceftiofur MIC in canine E. coli . Notably, the isolate belonged to ST410, and phylogenetic analysis indicated that it was not confined to a dog-associated background but instead clustered within a broader lineage shared across multiple sources, highlighting the need for potential dissemination of this mechanism and its associated resistant lineages at the human-companion animal interface.
2026-06-22
One Health Advances (publié)
doi.org
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
Di Wu
Huan Liu
Zhixiang Chi
Yuanhao Yu
Konstantinos N. Plataniotis
Yang Wang
The rapid advancements in using neural networks as implicit data representations have attracted significant interest in developing machine l… (voir plus)earning methods that analyze and process the weight spaces of other neural networks. However, efficiently handling these high-dimensional weight spaces remains challenging. Existing methods often overlook the sequential nature of layer-by-layer processing in neural network inference. In this work, we propose a novel approach using dynamic graphs to represent neural network parameters, capturing the temporal dynamics of inference. Our Dynamic Neural Graph Encoder (DNG-Encoder) processes these graphs, preserving the sequential nature of neural processing. Additionally, we also leverage DNG-Encoder to develop INR2JLS (Implicit Neural Representation to Joint Latent Space) for facilitate downstream applications, such as classifying Implicit Neural Representations (INRs). Our approach demonstrates significant improvements across multiple tasks, surpassing the state-of-the-art INR classification accuracy by approximately 10\% on the CIFAR-100-INR. Our code is available at https://github.com/dddiowww/DNG.
2026-06-09
Transactions on Machine Learning Research (accepté)
openreview.net
openreview.net
AES: Curing Optimizer Blindness in Long-Tailed Recognition via State-Aware Correction
Fanfu Wang
Jiachang Zhan
Zhiheng Gong
Pengkun Wang
Yang Wang
Long-tailed recognition fundamentally suffers from optimizer blindness where the optimization process mistakenly conflates the magnitude of … (voir plus)gradient accumulation with the scarcity of semantic information. Existing strategies relying on static frequency-based priors fail to correct this bias and result in state blindness regarding supervision and micro-level blindness regarding parameter updates. To address these limitations, we propose the AES framework to establish a dynamic and state-aware correction system across the entire learning lifecycle. We specifically introduce Adaptive Residual Supervision loss to act as a real-time reality check for supervision completeness via precision shielding. We also propose Entropy-aware PCGrad to resolve parameter-level conflicts by quantifying task specificity through gradient entropy. Additionally, we devise Sample-level Conflict Arbitrated Fusion to serve as a dynamic inference arbiter that routes predictions based on instance difficulty. Extensive experiments on CIFAR-100-LT, ImageNet-LT, and iNaturalist 2018 demonstrate that our method consistently achieves state-of-the-art performance by effectively balancing head-class stability and tail-class discrimination. Code is available at [here](https://github.com/wangff0101/AES)
2026-04-29
International Conference on Machine Learning (accepté)
openreview.net
openreview.net
Crisp: A Spectral-Based Interaction Strategy for Multivariate Time Series Forecasting
Binwu Wang
Gaoyun Lin
Jiaming Ma
Qihe Huang
Zhengyang Zhou
Xu Wang
Pengkun Wang
Yang Wang
Multivariate time series (MTS) forecasting critically depends on modeling inter-variable dependencies, yet existing paradigms face a trade-o… (voir plus)ff: channel-isolation strategies can suffer from information fragmentation in strongly coupled systems, whereas channel-interaction methods often introduce spurious interactions among irrelevant variables. To address this challenge, we propose Coherent Resonance Interaction with Spectral Priors (Crisp). Crisp adopts the principle that effective information exchange should occur only between variables with compatible oscillatory patterns. Concretely, we derive spectral priors in the frequency domain to construct dynamic resonance topologies. With a differentiable, adaptive, and strictly sparse blocking mechanism, Crisp forces attention weights for spectrally inconsistent neighbors to be exactly zero. In addition, we introduce a spectral-gated feature filtering module to refine variable representations using intrinsic spectral characteristics. Extensive experiments demonstrate that Crisp significantly outperforms 20+ baselines. Our code is available at Anonymous GitHub.
2026-04-29
International Conference on Machine Learning (accepté)
openreview.net
openreview.net
Generalization in Online Reinforcement Learning for Mobile Agents
Li Gu
Zihuan Jiang
Zhixiang Chi
Huan Liu
Ziqiang Wang
Yuanhao Yu
Glen Berseth
Yang Wang
Graphical user interface (GUI)-based mobile agents automate digital tasks on mobile devices by interpreting natural-language instructions an… (voir plus)d interacting with the screen. While recent methods apply reinforcement learning (RL) to train vision-language-model(VLM) agents in interactive environments with a primary focus on performance, generalization remains underexplored due to the lack of standardized benchmarks and open-source RL systems. In this work, we formalize the problem as a Contextual Markov Decision Process (CMDP) and introduce \textbf{AndroidWorld-Generalization}, a benchmark with three increasingly challenging regimes for evaluating zero-shot generalization to unseen task instances, templates, and applications. We further propose an RL training system that integrates Group Relative Policy Optimization (GRPO) with a scalable rollout collection system, consisting of containerized infrastructure and asynchronous execution % , and error recovery to support reliable and efficient training. Experiments on AndroidWorld-Generalization show that RL enables a 7B-parameter VLM agent to surpass supervised fine-tuning baselines, yielding a 26.1\% improvement on unseen instances but only limited gains on unseen templates (15.7\%) and apps (8.3\%), underscoring the challenges of generalization. As a preliminary step, we demonstrate that few-shot adaptation at test-time improves performance on unseen apps, motivating future research in this direction. To support reproducibility and fair comparison, we open-source the full RL training system, including the environment, task suite, models, prompt configurations, and the underlying infrastructure \footnote{https://github.com/zihuanjiang/AndroidWorld-Generalization}.
2026-03-07
arXiv (prépublication)
doi.org
arxiv.org
PointMAC: Meta-Learned Adaptation for Robust Test-Time Point Cloud Completion
Linlian Jiang
Rui Ma
Li Gu
Ziqiang Wang
Xinxin Zuo
Yang Wang
Point cloud completion is essential for robust 3D perception in safety-critical applications such as robotics and augmented reality. However… (voir plus), existing models perform static inference and rely heavily on inductive biases learned during training, limiting their ability to adapt to novel structural patterns and sensor-induced distortions at test time. To address this limitation, we propose PointMAC, a meta-learned framework for robust test-time adaptation in point cloud completion. It enables sample-specific refinement without requiring additional supervision. Our method optimizes the completion model under two self-supervised auxiliary objectives that simulate structural and sensor-level incompleteness. A meta-auxiliary learning strategy based on Model-Agnostic Meta-Learning (MAML) ensures that adaptation driven by auxiliary objectives is consistently aligned with the primary completion task. During inference, we adapt the shared encoder on-the-fly by optimizing auxiliary losses, with the decoder kept fixed. To further stabilize adaptation, we introduce Adaptive
2025-12-02
Conference on Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net
MetaWriter: Personalized Handwritten Text Recognition Using Meta-Learned Prompt Tuning
Wenhao Gu
Li Gu
Ching Yee Suen
Yang Wang
Recent advancements in handwritten text recognition (HTR) have enabled the effective conversion of handwritten text to digital formats. Howe… (voir plus)ver, achieving robust recognition across diverse writing styles remains challenging. Traditional HTR methods lack writer-specific personalization at test time due to limitations in model architecture and training strategies. Existing attempts to bridge this gap, through gradient-based meta-learning, still require labeled examples and suffer from parameter-inefficient fine-tuning, leading to substantial computational and memory overhead. To overcome these challenges, we propose an efficient framework that formulates personalization as prompt tuning, incorporating an auxiliary image reconstruction task with a self-supervised loss to guide prompt adaptation with unlabeled test-time examples. To ensure self-supervised loss effectively minimizes text recognition error, we leverage meta-learning to learn the optimal initialization of the prompts. As a result, our method allows the model to efficiently capture unique writing styles by updating less than 1% of its parameters and eliminating the need for time-intensive annotation processes. We validate our approach on the RIMES and IAM Handwriting Database benchmarks, where it consistently outperforms previous state-of-the-art methods while using 20x fewer parameters. We believe this represents a significant advancement in personalized handwritten text recognition, paving the way for more reliable and practical deployment in resource-constrained scenarios.
2025-06-12
IEEE/CVF Conference on Computer Vision and Pattern Recognition (Accept)
doi.org
arxiv.org
Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation
Zhixiang Chi
Li Gu
Huan Liu
Ziqiang Wang
Yanan Wu
Yang Wang
Konstantinos N Plataniotis
Few-shot Test-Time Domain Adaptation focuses on adapting a model at test time to a specific domain using only a few unlabeled examples, addr… (voir plus)essing domain shift. Prior methods leverage CLIP's strong out-of-distribution (OOD) abilities by generating domain-specific prompts to guide its generalized, frozen features. However, since downstream datasets are not explicitly seen by CLIP, solely depending on the feature space knowledge is constrained by CLIP's prior knowledge. Notably, when using a less robust backbone like ViT-B/16, performance significantly drops on challenging real-world benchmarks. Departing from the state-of-the-art of inheriting the intrinsic OOD capability of CLIP, this work introduces learning directly on the input space to complement the dataset-specific knowledge for frozen CLIP. Specifically, an independent side branch is attached in parallel with CLIP and enforced to learn exclusive knowledge via revert attention. To better capture the dataset-specific label semantics for downstream adaptation, we propose to enhance the inter-dispersion among text features via greedy text ensemble and refinement. The text and visual features are then progressively fused in a domain-aware manner by a generated domain prompt to adapt toward a specific domain. Extensive experiments show our method's superiority on 5 large-scale benchmarks (WILDS and DomainNet), notably improving over smaller networks like ViT-B/16 with gains of \textbf{+5.1} in F1 for iWildCam and \textbf{+3.1\%} in WC Acc for FMoW.
2025-04-22
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net