- Mixup|Augmented Meta|Learning for Sample|Efficient Fine|Tuning ...🔍
- mixup|augmented meta|learning for sample|efficient fine|tuning of ...🔍
- Jingbang|Chen/mixup|meta|protein|simulators🔍
- Mixup|Augmented Meta|Learning for Sample|Efficient ...🔍
- Decoupled Mixup for Data|efficient Learning🔍
- Data augmentation with Mixup🔍
- Effectiveness of Data Augmentation for Parameter Efficient Tuning ...🔍
- On Mixup Training🔍
mixup|augmented meta|learning for sample|efficient fine|tuning of ...
Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning ...
Title:Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators ... Abstract:Molecular dynamics simulations have ...
mixup-augmented meta-learning for sample-efficient fine-tuning of ...
MIXUP-AUGMENTED META-LEARNING FOR. SAMPLE-EFFICIENT FINE-TUNING OF PROTEIN SIMULATORS. Jingbang Chen1*, Yian Wang2*, Xingwei Qu 3, Shuangjia ...
Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning ...
Request PDF | Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators | Molecular dynamics simulations have ...
Jingbang-Chen/mixup-meta-protein-simulators - GitHub
mixup-meta-protein-simulators. ArXiv. Official code release for the paper "Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators" ...
Mixup-Augmented Meta-Learning for Sample-Efficient ... - BibSonomy
Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators. J. Chen, Y. Wang, X. Qu, S. Zheng, Y. Yang, H. Dong, and J. Fu. CoRR, ( ...
Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning ...
Connected Papers is a visual tool to help researchers and applied scientists find academic papers relevant to their field of work.
Decoupled Mixup for Data-efficient Learning - OpenReview
Summary Of The Paper: This paper proposes a new objective function for mixed sample data augmentation (MSDA). Technically, the proposed ...
Data augmentation with Mixup: Enhancing performance of a ...
Although deep learning holds great promise as a prognostic tool in psychiatry, a limitation of the method is that it requires large training sample sizes to ...
SMILE: Sample-to-feature MIxup for Efficient Transfer LEarning
To improve the performance of deep learning, mixup has been proposed to force the neural networks favoring simple linear behaviors ...
Effectiveness of Data Augmentation for Parameter Efficient Tuning ...
In this paper, we examine the effectiveness of several popular task-agnostic data augmentation techniques, i.e., EDA, Back Translation, and Mixup, when using ...
On Mixup Training: Improved Calibration and Predictive Uncertainty ...
Machine learning algorithms are replacing or expected to increasingly replace humans in decision- making pipelines. With the deployment of AI-based systems in ...
SMILE: Self-Distilled MIxup for Efficient Transfer LEarning - NASA/ADS
Ablation studies show that the vanilla sample-to-label mixup strategies could marginally increase the linearity in-between training samples but lack of ...
Convolution-Augmented Parameter-Efficient Fine-Tuning for Speech ...
supervised learning, and fine-tune it for the automatic speech recognition (ASR) task to examine how the proposed PEFT method impacts ...
Introduction. We summarize awesome mixup data augmentation methods for visual representation learning in various scenarios from 2018 to 2024. The list of ...
Augmented Memory: Sample-Efficient Generative Molecular Design ...
Notably, these applications of experience replay are for on-policy learning using the REINFORCE algorithm. In contrast, Yang et al. (73) used ...
Data Augmentation for Meta-Learning
Data augmentation has become an essential part of the training pipeline for image classifiers and related tasks, as it offers a simple and efficient way to ...
Augmenting a Large Language Model with Retrieval-Augmented ...
This stage involves learning general language patterns. Fine-tuning is adding more training to the pretrained model based on a smaller, specific ...
MixUp augmentation for image classification - Keras
... fine-tuning ... shape(images_one)[0] # Sample lambda and ... mixup does not work well when you are using Supervised Contrastive Learning ...
Meta-Learning with Memory-Augmented Neural Networks
(b) A successful strategy would involve the use of an external memory to store bound sample representation-class label information, which can then be retrieved ...
FeLMi : Few shot Learning with hard Mixup - ResearchGate
Inspired by the efficacy of pseudo-labeling, we further increase sample size by mixup and choose more informative samples by hard mixup-based sample selection.