Antonio Orvieto
Publications by Year
Research Areas
Neural Networks and Applications, Stochastic Gradient Optimization Techniques, Sparse and Compressive Sensing Techniques, Generative Adversarial Networks and Image Synthesis, Model Reduction and Neural Networks
Most-Cited Works
- → Learning explanations that are hard to vary(2020)73 cited
- → Resurrecting Recurrent Neural Networks for Long Sequences(2023)43 cited
- → Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning(2023)33 cited
- A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization.(2020)
- → Recurrent neural networks: vanishing and exploding gradients are not the end of the story(2024)12 cited
- → Mean first exit times of Ornstein–Uhlenbeck processes in high-dimensional spaces(2023)11 cited
- → Shadowing Properties of Optimization Algorithms(2019)10 cited
- → Momentum Improves Optimization on Riemannian Manifolds(2020)9 cited
- → On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics(2023)9 cited
- → Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity(2021)8 cited