GraphScan replaces geometric or coordinate-based scanning in Vision SSMs with learned local semantic graph routing, yielding SOTA results among such models on classification and segmentation tasks.
Efficientnet: Rethinking model scaling for convolutional neural networks
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Optimizing a single constant initial noise vector for frozen generative robot policies improves success rates on 38 of 43 tasks by up to 58% relative improvement.
A human-centered OOD spectrum based on perceptual difficulty shows vision-language models align best with human errors across regimes, with CNNs stronger on near-OOD and ViTs on far-OOD.
EgoWalk supplies 50 hours of real-world multimodal human navigation data in varied indoor/outdoor settings together with open pipelines that auto-generate language goal annotations and traversability masks.
A new neural network stabilizes features for rare chest X-ray diseases via momentum anchoring and multi-scale fusion on EfficientNet, achieving 0.8682 AUC on ChestX-ray14.
IMPACTX adds XAI-derived attention maps as a training constraint on standard CNNs, raising accuracy on CIFAR-10/100 and STL-10 while embedding feature attributions directly in the model.
Swish-T family adds Tanh bias to Swish activation, with Swish-T_C proposed as the main variant showing empirical gains on MNIST, CIFAR-10 and related datasets.
Supervised LDA restructuring of PCA-compressed embeddings raises silhouette separability from near zero to 0.197 in plant phenomics but yields mixed classical ML gains and persistent challenges for quantum kernel alignment under limited compute.
citing papers explorer
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Can Graphs Help Vision SSMs See Better?
GraphScan replaces geometric or coordinate-based scanning in Vision SSMs with learned local semantic graph routing, yielding SOTA results among such models on classification and segmentation tasks.
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You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector
Optimizing a single constant initial noise vector for frozen generative robot policies improves success rates on 38 of 43 tasks by up to 58% relative improvement.
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Do Machines Fail Like Humans? A Human-Centred Out-of-Distribution Spectrum for Mapping Error Alignment
A human-centered OOD spectrum based on perceptual difficulty shows vision-language models align best with human errors across regimes, with CNNs stronger on near-OOD and ViTs on far-OOD.
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EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild
EgoWalk supplies 50 hours of real-world multimodal human navigation data in varied indoor/outdoor settings together with open pipelines that auto-generate language goal annotations and traversability masks.
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Momentum-Anchored Multi-Scale Fusion Model for Long-Tailed Chest X-Ray Classification
A new neural network stabilizes features for rare chest X-ray diseases via momentum anchoring and multi-scale fusion on EfficientNet, achieving 0.8682 AUC on ChestX-ray14.
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IMPACTX: improving model performance by appropriately constraining the training with teacher explanations
IMPACTX adds XAI-derived attention maps as a training constraint on standard CNNs, raising accuracy on CIFAR-10/100 and STL-10 while embedding feature attributions directly in the model.
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Swish-T : Enhancing Swish Activation with Tanh Bias for Improved Neural Network Performance
Swish-T family adds Tanh bias to Swish activation, with Swish-T_C proposed as the main variant showing empirical gains on MNIST, CIFAR-10 and related datasets.
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Supervised Latent Restructuring for Small-Data Quantum Learning in Plant Phenomics
Supervised LDA restructuring of PCA-compressed embeddings raises silhouette separability from near zero to 0.197 in plant phenomics but yields mixed classical ML gains and persistent challenges for quantum kernel alignment under limited compute.