Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Neural architecture search survey: A computer vision perspective
JS Kang, JK Kang, JJ Kim, KW Jeon, HJ Chung… - Sensors, 2023 - mdpi.com
In recent years, deep learning (DL) has been widely studied using various methods across
the globe, especially with respect to training methods and network structures, proving highly …
the globe, especially with respect to training methods and network structures, proving highly …
Transforming large-size to lightweight deep neural networks for IoT applications
Deep Neural Networks (DNNs) have gained unprecedented popularity due to their high-
order performance and automated feature extraction capability. This has encouraged …
order performance and automated feature extraction capability. This has encouraged …
Memorization without overfitting: Analyzing the training dynamics of large language models
Despite their wide adoption, the underlying training and memorization dynamics of very
large language models is not well understood. We empirically study exact memorization in …
large language models is not well understood. We empirically study exact memorization in …
Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …
reduce the size of neural networks by selectively pruning components. Similarly to their …
The lottery ticket hypothesis for pre-trained bert networks
In natural language processing (NLP), enormous pre-trained models like BERT have
become the standard starting point for training on a range of downstream tasks, and similar …
become the standard starting point for training on a range of downstream tasks, and similar …
Linear mode connectivity and the lottery ticket hypothesis
We study whether a neural network optimizes to the same, linearly connected minimum
under different samples of SGD noise (eg, random data order and augmentation). We find …
under different samples of SGD noise (eg, random data order and augmentation). We find …
Where to begin? on the impact of pre-training and initialization in federated learning
Accelerating dataset distillation via model augmentation
Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but
efficient synthetic training datasets from large ones. Existing DD methods based on gradient …
efficient synthetic training datasets from large ones. Existing DD methods based on gradient …
Understanding the role of training regimes in continual learning
Catastrophic forgetting affects the training of neural networks, limiting their ability to learn
multiple tasks sequentially. From the perspective of the well established plasticity-stability …
multiple tasks sequentially. From the perspective of the well established plasticity-stability …