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Found 6 papers, 1 papers with code
SOInter: A Novel Deep Energy Based Interpretation Method for Explaining Structured Output Models
Because of the complex relationship between the computational path of output variables in structured models, a feature can affect the value of output through other ones.
Generative vs. Discriminative: Rethinking The Meta-Continual Learning
Deep neural networks have achieved human-level capabilities in various learning tasks.
Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds
This approach provides an insight into learning algorithms by considering the mutual information between the model and the training set.
Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks
Numerous neurophysiological studies have revealed that a large number of the primary visual cortex neurons operate in a regime called surround modulation.
A Gradient-Based Approach to Neural Networks Structure Learning
Designing the architecture of deep neural networks (DNNs) requires human expertise and is a cumbersome task.
MDL-CW: A Multimodal Deep Learning Framework With Cross Weights
In this paper, we propose a multimodal deep learning framework (MDL-CW) that exploits the cross weights between representation of modalities, and try to gradually learn interactions of the modalities in a deep network manner (from low to high level interactions).
