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References (28)

[1]
Towards Fast LLM Fine-tuning through Zeroth-Order Optimization with Projected Gradient-Aligned Perturbations
2025Zhendong Mi, Qitao Tan et al.
[2]
Layered-Parameter Perturbation for Zeroth-Order Optimization of Optical Neural Networks
2025Hiroshi Sawada, Kazuo Aoyama et al.
[3]
QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models
2025Jiajun Zhou, Yifan Yang et al.
[4]
Optical neural networks: progress and challenges
2024Tingzhao Fu, Jianfa Zhang et al.
[5]
Hidden semi-Markov models with inhomogeneous state dwell-time distributions
2024Jan-Ole Koslik
[6]
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark
2024Yihua Zhang, Pingzhi Li et al.
[7]
An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization
2024Jincheng Cao, Ruichen Jiang et al.
[8]
SubZero: Random Subspace Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning
2024Ziming Yu, Pan Zhou et al.
[9]
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training
2023Aochuan Chen, Yimeng Zhang et al.
[10]
Training neural networks with end-to-end optical backpropagation
2023J. Spall, Xianxin Guo et al.
[11]
Fine-Tuning Language Models with Just Forward Passes
2023Sadhika Malladi, Tianyu Gao et al.
[12]
Hierarchical categorization learning is associated with representational changes in the dorsal striatum and posterior frontal and parietal cortex
2023Sebastian M. Frank, M. Maechler et al.
[13]
Meta-learning biologically plausible plasticity rules with random feedback pathways
2022Navid Shervani-Tabar, R. Rosenbaum
[14]
Scaling Forward Gradient With Local Losses
2022Mengye Ren, Simon Kornblith et al.
[15]
Learning and Representation of Hierarchical Concepts in Hippocampus and Prefrontal Cortex
2021S. Theves, D. Neville et al.
[16]
Electrophysiological signatures of hierarchical learning
2021Meng Liu, Wenshan Dong et al.
[17]
Efficient On-Chip Learning for Optical Neural Networks Through Power-Aware Sparse Zeroth-Order Optimization
2020Jiaqi Gu, Chenghao Feng et al.
[18]
Backpropagation and the brain
2020T. Lillicrap, Adam Santoro et al.
[19]
Backpropagation through nonlinear units for the all-optical training of neural networks
2019Xianxin Guo, T. Barrett et al.
[20]
Gaussian Error Linear Units (GELUs)
2016Dan Hendrycks, Kevin Gimpel

Showing 20 of 28 references

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