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Found 7 papers, 3 papers with code
Optimization dependent generalization bound for ReLU networks based on sensitivity in the tangent bundle
Recent advances in deep learning have given us some very promising results on the generalization ability of deep neural networks, however literature still lacks a comprehensive theory explaining why heavily over-parametrized models are able to generalize well while fitting the training data.
PAC bounds of continuous Linear Parameter-Varying systems related to neural ODEs
We consider the problem of learning Neural Ordinary Differential Equations (neural ODEs) within the context of Linear Parameter-Varying (LPV) systems in continuous-time.
Gradient representations in ReLU networks as similarity functions
Feed-forward networks can be interpreted as mappings with linear decision surfaces at the level of the last layer.
Quantum Inspired Adaptive Boosting
Building on the quantum ensemble based classifier algorithm of Schuld and Petruccione [arXiv:1704. 02146v1], we devise equivalent classical algorithms which show that this quantum ensemble method does not have advantage over classical algorithms.
Tangent Space Sensitivity and Distribution of Linear Regions in ReLU Networks
We derive several easily computable bounds and empirical measures for feed-forward fully connected ReLU (Rectified Linear Unit) networks and connect tangent sensitivity to the distribution of the activation regions in the input space realized by the network.
Tangent Space Separability in Feedforward Neural Networks
Hierarchical neural networks are exponentially more efficient than their corresponding "shallow" counterpart with the same expressive power, but involve huge number of parameters and require tedious amounts of training.
Expressive power of outer product manifolds on feed-forward neural networks
Hierarchical neural networks are exponentially more efficient than their corresponding "shallow" counterpart with the same expressive power, but involve huge number of parameters and require tedious amounts of training.
