Search Results for author:
Found 5 papers, 3 papers with code
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy
Large convolutional neural networks (CNN) can be difficult to train in the differentially private (DP) regime, since the optimization algorithms require a computationally expensive operation, known as the per-sample gradient clipping.
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity
In order to empower NAM with feature selection and improve the generalization, we propose the sparse neural additive models (SNAM) that employ the group sparsity regularization (e. g. Group LASSO), where each feature is learned by a sub-network whose trainable parameters are clustered as a group.
Transient Stability of Low-Inertia Power Systems with Inverter-Based Generation
This criterion is proved to be a sufficient stability condition for addressing the effects of the jumps and cosine damping coefficient on the system stability.
DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks
Recent years have witnessed strong empirical performance of over-parameterized neural networks on various tasks and many advances in the theory, e. g. the universal approximation and provable convergence to global minimum.
A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks
When equipped with efficient optimization algorithms, the over-parameterized neural networks have demonstrated high level of performance even though the loss function is non-convex and non-smooth.
