SO-TA replaces standard attention with optimal-transport alignment across vision, force/torque, and proprioception to improve diffusion-policy performance on real-robot insertion and wiping tasks.
Multimodal machine learning: A survey and taxonomy
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
ADAPT-MS achieves 0.66 RMSE unsupervised and 0.60 with 1000 labels on cross-platform MOOC satisfaction prediction by aligning representations and correcting platform biases.
TET-LLM predicts MOOC satisfaction early via temporal event transformers on behavior, LLM embeddings on text, and topic distributions, beating baselines at RMSE 0.82 and AUC 0.77 for 7-day forecasts.
A multi-domain distributed classifier for interference in O-RAN cuts latency by 9 times and computation by 11 times versus monolithic models with minimal accuracy loss.
citing papers explorer
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Spacetime Optimal-Transport Attention for Visuo-Haptic Imitation Learning of Contact-Rich Manipulation
SO-TA replaces standard attention with optimal-transport alignment across vision, force/torque, and proprioception to improve diffusion-policy performance on real-robot insertion and wiping tasks.
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Cross-Platform Domain Adaptation for Multi-Modal MOOC Learner Satisfaction Prediction
ADAPT-MS achieves 0.66 RMSE unsupervised and 0.60 with 1000 labels on cross-platform MOOC satisfaction prediction by aligning representations and correcting platform biases.
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Early-Warning Learner Satisfaction Forecasting in MOOCs via Temporal Event Transformers and LLM Text Embeddings
TET-LLM predicts MOOC satisfaction early via temporal event transformers on behavior, LLM embeddings on text, and topic distributions, beating baselines at RMSE 0.82 and AUC 0.77 for 7-day forecasts.
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Disaggregated multi-domain interference classification for O-RAN
A multi-domain distributed classifier for interference in O-RAN cuts latency by 9 times and computation by 11 times versus monolithic models with minimal accuracy loss.