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Found 7 papers, 7 papers with code
Memory-Efficient Backpropagation through Large Linear Layers
Also, we investigate the variance of the gradient estimate induced by the randomized matrix multiplication.
Meta-Solver for Neural Ordinary Differential Equations
Moreover, we show that the right choice of solver parameterization can significantly affect neural ODEs models in terms of robustness to adversarial attacks.
Follow the bisector: a simple method for multi-objective optimization
This descent direction is based on the normalized gradients of the individual losses.
Towards Understanding Normalization in Neural ODEs
Normalization is an important and vastly investigated technique in deep learning.
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs
We propose a simple interpolation-based method for the efficient approximation of gradients in neural ODE models.
Preconditioning Kaczmarz method by sketching
Therefore, to reduce this complexity, we use random sketching and compare it with the Kaczmarz method without preconditioning.
Numerical Analysis
Deep Multigrid: learning prolongation and restriction matrices
This paper proposes the method to optimize restriction and prolongation operators in the two-grid method.
Numerical Analysis
