LG-CoTrain, an LLM-guided co-training method, outperforms classical semi-supervised baselines for crisis tweet classification in low-resource settings with 5-25 labeled examples per class.
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7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 7representative citing papers
A multi-head attention model for Russian morphological tagging supports open dictionaries via subtoken splitting and reports 98-99% accuracy on grammatical categories while running efficiently on consumer hardware.
StateFlow extends VARNN with dual hidden and residual-memory states plus a chunk decoder and two-stage training to enable competitive long-horizon time series forecasting while retaining a compact recurrent design.
HyPOLE introduces a HyperLTL-guided framework for partial-observability MARL integrated with CTDE, claiming advantages over baselines on SMAC, MessySMAC, and WildFire.
Applies standard sentiment classifiers and topic modeling to a large AAM discussion corpus, identifies six clusters of public concern, and lists strategies to address them.
Hybrid neuromorphic-ANN models outperform standard deep learning on few-shot benchmarks and under occlusion/impulse noise via astrocytic modulation and spiking dynamics.
A graph neural network learns to simulate 1D sea ice floe collisions and trajectories using data assimilation on synthetic data.
citing papers explorer
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LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification
LG-CoTrain, an LLM-guided co-training method, outperforms classical semi-supervised baselines for crisis tweet classification in low-resource settings with 5-25 labeled examples per class.
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A Multi-head-based architecture for effective morphological tagging in Russian with open dictionary
A multi-head attention model for Russian morphological tagging supports open dictionaries via subtoken splitting and reports 98-99% accuracy on grammatical categories while running efficiently on consumer hardware.
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StateFlow: Dual-State Recurrent Modeling for Long-Horizon Time Series Forecasting
StateFlow extends VARNN with dual hidden and residual-memory states plus a chunk decoder and two-stage training to enable competitive long-horizon time series forecasting while retaining a compact recurrent design.
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HyPOLE: Hyperproperty-Guided Multi-Agent Reinforcement Learning under Partial Observation
HyPOLE introduces a HyperLTL-guided framework for partial-observability MARL integrated with CTDE, claiming advantages over baselines on SMAC, MessySMAC, and WildFire.
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From Sentiment to Actionable Insights: A Data-Driven Public Sentiment Analysis of Advanced Air Mobility
Applies standard sentiment classifiers and topic modeling to a large AAM discussion corpus, identifies six clusters of public concern, and lists strategies to address them.
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The Neuromorphic Supremacy
Hybrid neuromorphic-ANN models outperform standard deep learning on few-shot benchmarks and under occlusion/impulse noise via astrocytic modulation and spiking dynamics.
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Graph neural network for colliding particles with an application to sea ice floe modeling
A graph neural network learns to simulate 1D sea ice floe collisions and trajectories using data assimilation on synthetic data.