Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck
Neural Information Processing Systems
Previous works have broadly investigated the reason for the widespread of such adversarial examples.
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SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classification
Neural Information Processing Systems
WSI (bag) while only a slide label is given.
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- Information Technology > Sensing and Signal Processing > Image Processing (0.94)
- Information Technology > Communications > Mobile (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.46)
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- Information Technology > Artificial Intelligence > Vision (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.46)
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- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.46)
Spherization Layer: Representation Using Only Angles
Neural Information Processing Systems
Furthermore, this method can be applied to any network by replacing an existing layer.
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- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.70)
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- North America > Canada > Ontario > Toronto (0.14)
- North America > United States > Pennsylvania > Allegheny County > Pittsburgh (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
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