Self-distillation for randomized neural networks

Self-distillation for randomized neural networks

Knowledge distillation (KD) is a conventional method in the field of deep learning that enables the transfer of dark knowledge from a teacher model to a student model, consequently improving the performance of the student model. In randomized neural networks, due to the simple topology of network ar...

Saved in:

書目詳細資料
Main Authors:	Hu, Minghui, Gao, Ruobin, Suganthan, Ponnuthurai Nagaratnam
其他作者:	School of Electrical and Electronic Engineering
格式:	Article
語言:	English
出版:	2024
主題:	Engineering Knowledge distillation Random vector functional link
在線閱讀:	https://hdl.handle.net/10356/174318
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

相似書籍

Online dynamic ensemble deep random vector functional link neural network for forecasting
由: Gao, Ruobin, et al.
出版: (2024)

Representation learning using deep random vector functional link networks for clustering
由: Hu, Minghui, et al.
出版: (2022)

An enhanced ensemble deep random vector functional link network for driver fatigue recognition
由: Li, Ruilin, et al.
出版: (2024)

A spectral-ensemble deep random vector functional link network for passive brain–computer interface
由: Li, Ruilin, et al.
出版: (2024)

Stacked autoencoder based deep random vector functional link neural network for classification
由: Katuwal, Rakesh, et al.
出版: (2020)

Random vector functional link neural network based ensemble deep learning
由: Shi, Qiushi, et al.
出版: (2022)

Extended features based random vector functional link network for classification problem
由: Malik, Ashwani Kumar, et al.
出版: (2022)

Benchmarking feed-forward randomized neural networks for vessel trajectory prediction
由: Cheng, Ruke, et al.
出版: (2024)

Jointly optimized ensemble deep random vector functional link network for semi-supervised classification
由: Shi, Qiushi, et al.
出版: (2022)

Parsimonious random vector functional link network for data streams
由: Pratama, Mahardhika, et al.
出版: (2018)

New approach to daily and peak load predictions using a random vector functional-link network
由: Dash, P.K., et al.
出版: (2014)

Class-incremental learning on multivariate time series via shape-aligned temporal distillation
由: Qiao, Zhongzheng, et al.
出版: (2023)

Discriminator-enhanced knowledge-distillation networks
由: Li, Zhenping, et al.
出版: (2023)

Edge-computing-based knowledge distillation and multitask learning for partial discharge recognition
由: Ji, Jinsheng, et al.
出版: (2024)

An ensemble of decision trees with random vector functional link networks for multi-class classification
由: Katuwal, Rakesh, et al.
出版: (2020)

Solar Power Integration in Water (H2O) Distillation (SPIN-HD)
由: Carlos, Charlize F., et al.
出版: (2021)

Experimental evaluation of stochastic configuration networks: is SC algorithm inferior to hyper-parameter optimization method?
由: Hu, Minghui, et al.
出版: (2022)

FedTKD: a trustworthy heterogeneous federated learning based on adaptive knowledge distillation
由: Chen, Leiming, et al.
出版: (2024)

On-the-fly knowledge distillation model for sentence embedding
由: Zhu, Xuchun
出版: (2024)

Computation-efficient knowledge distillation via uncertainty-aware mixup
由: Xu, Guodong, et al.
出版: (2023)

Multimodal distillation for egocentric video understanding
由: Peng, Han
出版: (2024)

One-class knowledge distillation for face presentation attack detection
由: Li, Zhi, et al.
出版: (2023)

On the origins of randomization-based feedforward neural networks
由: Suganthan, Ponnuthurai Nagaratnam, et al.
出版: (2022)

LORA: Link obfuscation by randomization in graphs
由: Xiao, Q., et al.
出版: (2013)

Visual-to-EEG cross-modal knowledge distillation for continuous emotion recognition
由: Zhang, Su, et al.
出版: (2022)

Effect of humic-acid fouling on membrane distillation
由: Tan, Yong Zen, et al.
出版: (2017)

Comparison of different distillation methods for the isolation of anise oil and TLC analysis of analgesic drugs
由: Calixihan, Maria Corazon O., et al.
出版: (1989)

Distilled Siamese networks for visual tracking
由: SHEN, Jianbing, et al.
出版: (2022)

Significant wave height forecasting using hybrid ensemble deep randomized networks with neurons pruning
由: Gao, Ruobin, et al.
出版: (2023)

Application of reactive distillation for production of ethyl tertiary butyl ether from ethanol and tertiary butanol
由: Darin Wongwattanasat
出版: (2006)

Ensemble deep random vector functional link neural network for regression
由: Hu, Minghui, et al.
出版: (2023)

Understanding oily wastewater treatment via membrane distillation
由: Han, Le, et al.
出版: (2017)

Personalized federated learning with dynamic clustering and model distillation
由: Bao, Junyan
出版: (2025)

Reactive batch distillation of ethyl acetate : dynamic simulation and experimental validation
由: Khanittha Silajaru
出版: (2012)

Online learning using deep random vector functional link network
由: Shiva, Sreenivasan, et al.
出版: (2024)

Computer design and rating calculations of distillation column for multicomponent system
由: Dy, Nelson, et al.
出版: (1983)

Random vector functional link neural network based ensemble deep learning for short-term load forecasting
由: Gao, Ruobin, et al.
出版: (2023)

Knowledge Distillation with Relative Representations for Image Representation Learning
由: Ramos, Patrick, et al.
出版: (2023)

Pharmaceutical wastewater treatment using direct contact membrane distillation
由: Sim, Lee Nuang, et al.
出版: (2025)

Inter-region affinity distillation for road marking segmentation
由: Hou, Yuenan, et al.
出版: (2022)