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Found 7 papers, 5 papers with code
LoTR: Low Tensor Rank Weight Adaptation
In this paper we generalize and extend an idea of low-rank adaptation (LoRA) of large language models (LLMs) based on Transformer architecture.
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.
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.
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks
Secondly, we propose analyzing the vulnerability of a neural network using active subspace and finding an additive universal adversarial attack vector that can misclassify a dataset with a high probability.
Reduced-Order Modeling of Deep Neural Networks
We introduce a new method for speeding up the inference of deep neural networks.
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
