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Found 6 papers, 1 papers with code
Magic for the Age of Quantized DNNs
Recently, the number of parameters in DNNs has explosively increased, as exemplified by LLMs (Large Language Models), making inference on small-scale computers more difficult.
Convergences for Minimax Optimization Problems over Infinite-Dimensional Spaces Towards Stability in Adversarial Training
This instability problem comes from the difficulty of the minimax optimization, and there have been various approaches in GANs and UDAs to overcome this problem.
Spiking Synaptic Penalty: Appropriate Penalty Term for Energy-Efficient Spiking Neural Networks
Spiking neural networks (SNNs) are energy-efficient neural networks because of their spiking nature.
Theoretical Error Analysis of Entropy Approximation for Gaussian Mixture
Notably, Gal and Ghahramani [2016] proposed the approximate entropy that is the sum of the entropies of unimodal Gaussian distributions.
S$^3$NN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Spiking Neural Networks
To solve these problems, we propose a single-step spiking neural network (S$^3$NN), an energy-efficient neural network with low computational cost and high precision.
Spectral Pruning for Recurrent Neural Networks
Recurrent neural networks (RNNs) are a class of neural networks used in sequential tasks.
