30 citations · 190 across the 40 of their papers we have counts for
25 papers · 1 filter
Sinc Kolmogorov-Arnold network and its application for solving PDEs with singularities
Tianchi Yu, Jingwei Qiu, Jiang Yang +1
In this paper, we propose to use Sinc interpolation in the context of Kolmogorov-Arnold Networks, neural networks with learnable activation functions, which recently gained attenti…
Quantization Aware Factorization for Deep Neural Network Compression
Daria Cherniuk, Stanislav Abukhovich, Anh-Huy Phan +3
Tensor decomposition of convolutional and fully-connected layers is an effective way to reduce parameters and FLOP in neural networks. Due to memory and power consumption limitatio…
Survey on Large Scale Neural Network Training
Julia Gusak, Daria Cherniuk, Alena Shilova +8
Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training. Hence, many models do not fit one GPU de…
Few-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction
Georgii Novikov, Daniel Bershatsky, Julia Gusak +3
Memory footprint is one of the main limiting factors for large neural network training. In backpropagation, one needs to store the input to each operation in the computational grap…
Memory-Efficient Backpropagation through Large Linear Layers
Daniel Bershatsky, Aleksandr Mikhalev, Alexandr Katrutsa +3
In modern neural networks like Transformers, linear layers require significant memory to store activations during backward pass. This study proposes a memory reduction approach to…
Meta-Solver for Neural Ordinary Differential Equations
Julia Gusak, Alexandr Katrutsa, Talgat Daulbaev +2
A conventional approach to train neural ordinary differential equations (ODEs) is to fix an ODE solver and then learn the neural network's weights to optimize a target loss functio…