CTQWformer fuses continuous-time quantum walks into a graph transformer and recurrent module to outperform standard GNNs and graph kernels on classification benchmarks.
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EKD trains lightweight NMT students progressively from a chain of teachers with rising capacity, achieving BLEU scores within 0.08 of the largest teacher on IWSLT-14.
MAG-Net integrates radar dynamics with satellite IR, WV, and BTD channels via dual-stream encoding and uncertainty-weighted decoding to raise CSI40 by 0.083 over prior baselines for intense convective events.
Light-ResKAN reaches 99.09% accuracy on MSTAR SAR images with 82.9 times fewer FLOPs and 163.78 times fewer parameters than VGG16 by combining KAN convolutions, Gram polynomials, and channel-wise parameter sharing.
TSNN matches time series entries to a training-derived memory bank to forecast traffic without any trainable parameters and achieves competitive accuracy on four real-world datasets.
Lightweight transformer predicts iconic gesture placement and intensity from text and emotion for robot co-speech, outperforming GPT-4o on BEAT2 without audio input.
SACF discretizes target direction and distance from audio-visual cues then applies conditioned fusion to improve navigation efficiency and generalization to unheard sounds.
TL-MAPPO enables safe decentralized EV VPP operation under limited visibility, cutting voltage violations by 45% and costs by 10% versus baselines on a 33-bus network.
PrismNet combines text and image modalities with time series via a PID-guided contrastive learning module to boost few-shot power load forecasting accuracy and provide interpretability.
citing papers explorer
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CTQWformer: A CTQW-based Transformer for Graph Classification
CTQWformer fuses continuous-time quantum walks into a graph transformer and recurrent module to outperform standard GNNs and graph kernels on classification benchmarks.
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Evolving Knowledge Distillation for Lightweight Neural Machine Translation
EKD trains lightweight NMT students progressively from a chain of teachers with rising capacity, achieving BLEU scores within 0.08 of the largest teacher on IWSLT-14.
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MAG-Net: Physics-Aware Multi-Modal Fusion of Geostationary Satellite and Radar for Severe Convective Precipitation Nowcasting
MAG-Net integrates radar dynamics with satellite IR, WV, and BTD channels via dual-stream encoding and uncertainty-weighted decoding to raise CSI40 by 0.083 over prior baselines for intense convective events.
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Light-ResKAN: A Parameter-Sharing Lightweight KAN with Gram Polynomials for Efficient SAR Image Recognition
Light-ResKAN reaches 99.09% accuracy on MSTAR SAR images with 82.9 times fewer FLOPs and 163.78 times fewer parameters than VGG16 by combining KAN convolutions, Gram polynomials, and channel-wise parameter sharing.
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TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting
TSNN matches time series entries to a training-derived memory bank to forecast traffic without any trainable parameters and achieves competitive accuracy on four real-world datasets.
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Efficient Emotion-Aware Iconic Gesture Prediction for Robot Co-Speech
Lightweight transformer predicts iconic gesture placement and intensity from text and emotion for robot co-speech, outperforming GPT-4o on BEAT2 without audio input.
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Spatial-Aware Conditioned Fusion for Audio-Visual Navigation
SACF discretizes target direction and distance from audio-visual cues then applies conditioned fusion to improve navigation efficiency and generalization to unheard sounds.
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Safe Decentralized Operation of EV Virtual Power Plant with Limited Network Visibility via Multi-Agent Reinforcement Learning
TL-MAPPO enables safe decentralized EV VPP operation under limited visibility, cutting voltage violations by 45% and costs by 10% versus baselines on a 33-bus network.
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PrismNet: Viewing Time Series Through a Multi-Modal Prism for Interpretable Power Load Forecasting
PrismNet combines text and image modalities with time series via a PID-guided contrastive learning module to boost few-shot power load forecasting accuracy and provide interpretability.
- LoKA: Low-precision Kernel Applications for Recommendation Models At Scale