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.
AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation
Neural Information Processing Systems
Neural network architecture design requires making many crucial decisions.
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The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks
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Deep learning succeeds by doing hierarchical feature learning, yet tuning hyper-parameters (HP) such as initialization scales, learning rates etc., only give indirect
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Enhancing Graph Transformers with Hierarchical Distance Structural Encoding Y uankai Luo 1,3 Hongkang Li2 Lei Shi 1 Xiao-Ming Wu3 1
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MPNNs, graph transformers do not rely on fixed graph structure information.
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Pandora's Box: Towards Building Universal Attackers against Real-World Large Vision-Language Models
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Large Vision-Language Models (L VLMs) have demonstrated remarkable capabilities across a wide range of multimodal understanding tasks.
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