7 papers · 1 filter
Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey
Liangwei Nathan Zheng, Wei Emma Zhang, Olaf Maennel +2
Mixture-of-Experts (MoE) presents a naturally compatible and scalable framework for multimodal learning, demonstrating strong adaptability across diverse modalities and tasks. Desp…
Lifting Manifolds to Mitigate Pseudo-Alignment in LLM4TS
Liangwei Nathan Zheng, Wenhao Liang, Wei Emma Zhang +3
Pseudo-Alignment is a pervasive challenge in many large language models for time series (LLM4TS) models, often causing them to underperform compared to linear models or randomly in…
Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate
Liangwei Nathan Zheng, Wei Emma Zhang, Mingyu Guo +2
Effectively managing missing modalities is a fundamental challenge in real-world multimodal learning scenarios, where data incompleteness often results from systematic collection e…
PostHoc FREE Calibrating on Kolmogorov Arnold Networks
Wenhao Liang, Wei Emma Zhang, Lin Yue +3
Kolmogorov Arnold Networks (KANs) are neural architectures inspired by the Kolmogorov Arnold representation theorem that leverage B Spline parameterizations for flexible, locally a…
Free-Knots Kolmogorov-Arnold Network: On the Analysis of Spline Knots and Advancing Stability
Liangwewi Nathan Zheng, Wei Emma Zhang, Lin Yue +3
Kolmogorov-Arnold Neural Networks (KANs) have gained significant attention in the machine learning community. However, their implementation often suffers from poor training stabili…
Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics
Liangwei Nathan Zheng, Zhengyang Li, Chang George Dong +5
Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Tim…