Introduces OW-SED paradigm and WOOT framework with deformable attention for detecting known and unseen sound events in open-world settings.
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6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
Photonic accelerators hit a topology-driven Utilization Wall; symmetric grids improve utilization up to 6X and cut memory access over 40% versus linear layouts.
A generative learning model of rational inattention is introduced for travel choice, shown to correlate with the theory and reformulated as a generalized entropy-utility multinomial logit.
A temperature-scaled hybrid fusion of ResNet and trainable quantum circuit features reaches 87.82% accuracy on BreastMNIST, outperforming classical baselines.
The MHHTOF framework uses momentum-constrained heuristic optimization and residual DRL to achieve faster convergence, lower stable costs, and safer velocity profiles than baselines in visually impaired navigation scenarios.
An adapted U-Net model trained on mean-field phase diagrams accurately predicts Hamiltonian parameters for a cuprate superconductor when validated on Monte Carlo simulation data.
citing papers explorer
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Towards Open World Sound Event Detection
Introduces OW-SED paradigm and WOOT framework with deformable attention for detecting known and unseen sound events in open-world settings.
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Towards Topology-Aware Very Large-Scale Photonic AI Accelerators
Photonic accelerators hit a topology-driven Utilization Wall; symmetric grids improve utilization up to 6X and cut memory access over 40% versus linear layouts.
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Information processing constraints in travel behaviour modelling: A generative learning approach
A generative learning model of rational inattention is introduced for travel choice, shown to correlate with the theory and reformulated as a generalized entropy-utility multinomial logit.
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On the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification
A temperature-scaled hybrid fusion of ResNet and trainable quantum circuit features reaches 87.82% accuracy on BreastMNIST, outperforming classical baselines.
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Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios
The MHHTOF framework uses momentum-constrained heuristic optimization and residual DRL to achieve faster convergence, lower stable costs, and safer velocity profiles than baselines in visually impaired navigation scenarios.
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Predicting parameters of a model cuprate superconductor using machine learning
An adapted U-Net model trained on mean-field phase diagrams accurately predicts Hamiltonian parameters for a cuprate superconductor when validated on Monte Carlo simulation data.