1 citations · 1 across the 8 of their papers we have counts for
8 papers
PhysioBench: A Unified Benchmark for Physiological Signal Question Answering
Mengxuan Li, Junfa Chen, Jinze Xia +5
Physiological signals support diverse clinical and monitoring tasks, yet existing physiological signal foundation models typically require task-specific adaptation for each task. N…
CoANeRV: Coordinate-Aware Token-Space Neural Video Representation
Jialong Guo, Ke Liu, Mengxuan Li +2
Neural representations for videos (NeRV) have shown strong reconstruction fidelity by storing video-specific information in network weights. However, existing formulations typicall…
Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies
Ke Liu, Mengxuan Li, Yanyi Bao +4
Medical diagnosis and treatment are dynamic processes in which patient states evolve over time and clinical interventions alter future outcomes. Although current medical AI can det…
ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Data
Mengxuan Li, Ke Liu, Jialong Guo +3
Healthcare data frequently contain a substantial proportion of missing values, necessitating effective time series imputation to support downstream disease diagnosis tasks. However…
MetaNeRV: Meta Neural Representations for Videos with Spatial-Temporal Guidance
Jialong Guo, Ke liu, Jiangchao Yao +3
Neural Representations for Videos (NeRV) has emerged as a promising implicit neural representation (INR) approach for video analysis, which represents videos as neural networks wit…
TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection
Mengxuan Li, Ke Liu, Hongyang Chen +3
Time series anomaly detection aims to identify unusual patterns in data or deviations from systems' expected behavior. The reconstruction-based methods are the mainstream in this t…
Attention Beats Linear for Fast Implicit Neural Representation Generation
Shuyi Zhang, Ke Liu, Jingjun Gu +4
Implicit Neural Representation (INR) has gained increasing popularity as a data representation method, serving as a prerequisite for innovative generation models. Unlike gradient-b…
Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis
Ke Liu, Feng Liu, Haishuai Wang +3
(INRs) aim to learn a (i.e., a neural network) to represent an image, where the input and output of the fu…