Qingyang Mao
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View article: Development and validation of an interpretable machine learning model using routine laboratory biomarkers to stratify severe pneumonia risk in young children
Development and validation of an interpretable machine learning model using routine laboratory biomarkers to stratify severe pneumonia risk in young children Open
This interpretable CatBoost model accurately stratifies pediatric severe pneumonia risk using routine laboratory data. Clinical implementation via the web tool may facilitate early intervention in resource-limited settings, though extensiv…
View article: TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoning
TableMind: An Autonomous Programmatic Agent for Tool-Augmented Table Reasoning Open
Table reasoning requires models to jointly perform comprehensive semantic understanding and precise numerical operations. Although recent large language model (LLM)-based methods have achieved promising results, most of them still rely on …
View article: Visual Autoregressive Modeling for Instruction-Guided Image Editing
Visual Autoregressive Modeling for Instruction-Guided Image Editing Open
Recent advances in diffusion models have brought remarkable visual fidelity to instruction-guided image editing. However, their global denoising process inherently entangles the edited region with the entire image context, leading to unint…
View article: Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables
Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables Open
With the widespread application of multimodal large language models in scientific intelligence, there is an urgent need for more challenging evaluation benchmarks to assess their ability to understand complex scientific data. Scientific ta…
View article: Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner
Enhancing Table Recognition with Vision LLMs: A Benchmark and Neighbor-Guided Toolchain Reasoner Open
Pre-trained foundation models have recently made significant progress in table-related tasks such as table understanding and reasoning. However, recognizing the structure and content of unstructured tables using Vision Large Language Model…
View article: PoTable: Towards Systematic Thinking via Stage-oriented Plan-then-Execute Reasoning on Tables
PoTable: Towards Systematic Thinking via Stage-oriented Plan-then-Execute Reasoning on Tables Open
In recent years, table reasoning has garnered substantial research interest, particularly its integration with Large Language Models (LLMs) which revolutionize natural language applications. Existing typical LLM-based studies realize step-…
View article: TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Models
TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Models Open
Large language models (LLMs) have demonstrated their effectiveness in multivariate time series classification (MTSC). Effective adaptation of LLMs for MTSC necessitates informative data representations. Existing LLM-based methods directly …
View article: DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting
DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting Open
Multivariate time series forecasting plays a crucial role in various real-world applications. Significant efforts have been made to integrate advanced network architectures and training strategies that enhance the capture of temporal depen…
View article: Promoting Machine Abilities of Discovering and Utilizing Knowledge in a Unified Zero-Shot Learning Paradigm
Promoting Machine Abilities of Discovering and Utilizing Knowledge in a Unified Zero-Shot Learning Paradigm Open
Knowledge discovery and utilization are two essential cognitive processes that enable humans to understand the world and extract new insights from their surroundings. These processes have motivated machine learning studies, particularly ze…
View article: DV-FSR: A Dual-View Target Attack Framework for Federated Sequential Recommendation
DV-FSR: A Dual-View Target Attack Framework for Federated Sequential Recommendation Open
Federated recommendation (FedRec) preserves user privacy by enabling decentralized training of personalized models, but this architecture is inherently vulnerable to adversarial attacks. Significant research has been conducted on targeted …