NPMixer improves multivariate time series forecasting accuracy by combining a data-adaptive wavelet decomposition with hierarchical neighboring patch mixing via MLPs and channel mixing on high-frequency components.
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3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
CoMole uses a motif-aware graph diffusion pipeline with RL to rank first in controllability on nine targets across materials and drug benchmarks while keeping validity above 0.94 without post-processing.
DreamerNLplus applies a mix of classification, regression, few-shot prompting, rules, and retrieval-augmented generation to model psychological states and changes from social media, placing in the top ranks on several CLPsych 2026 subtasks.
citing papers explorer
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NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting
NPMixer improves multivariate time series forecasting accuracy by combining a data-adaptive wavelet decomposition with hierarchical neighboring patch mixing via MLPs and channel mixing on high-frequency components.
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Controllable Molecular Generative Foundation Models
CoMole uses a motif-aware graph diffusion pipeline with RL to rank first in controllability on nine targets across materials and drug benchmarks while keeping validity above 0.94 without post-processing.
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DreamerNLplus: Interpretable Modeling of Mental Health Dynamics from Social Media Timelines using Hybrid Rule-Based and RAG Methods
DreamerNLplus applies a mix of classification, regression, few-shot prompting, rules, and retrieval-augmented generation to model psychological states and changes from social media, placing in the top ranks on several CLPsych 2026 subtasks.