- Mixup|Augmented Meta|Learning for Sample|Efficient Fine|Tuning ...🔍
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- Jingbang|Chen/mixup|meta|protein|simulators🔍
- Mixup|Augmented Meta|Learning for Sample|Efficient ...🔍
- Data augmentation with Mixup🔍
- Decoupled Mixup for Data|efficient Learning🔍
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Mixup|Augmented Meta|Learning for Sample|Efficient Fine|Tuning ...
Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning ...
In this paper, we explore and adapt the soft prompt-based learning method to molecular dynamics tasks. Our model can remarkably generalize to unseen and out-of ...
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.
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 ...
Decoupled Mixup for Data-efficient Learning - OpenReview
... sample. That is, although ... training efficiency of mixup training and the downstream task performance in various task environments.
Meta-Learning | Papers With Code
Meta-learning is a methodology considered with "learning to learn" machine learning ... Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein ...
AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning ... MS-DETR: Efficient DETR Training with Mixed Supervision Chuyang ...
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 ...
Augmentation on the same sample - Stack Overflow
deep-learning · data ... Can you explain the reason behind first train only on augmented dataset and then fine tune on the original samples ?
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 ...
Data Augmentation for Meta-Learning
During an episode of training, we sample a batch of tasks which may be, for example, five-way classification problems. In the inner loop, a model is fine-tuned ...
On Mixup Training: Improved Calibration and Predictive Uncertainty ...
... training data, but also in the vicinity of each training sample. The vicinal ... While mixup was originally suggested as a method to mostly improve performance on ...
Mixup data augmentation - fastai dev - fast.ai Course Forums
It's extremely efficient at regularizing models in computer vision, from what I've seen, allowing us to get our time to train CIFAR10 to 94% on ...
SMILE: Self-Distilled MIxup for Efficient Transfer LEarning - NASA/ADS
To improve the performance of deep learning, mixup has been proposed to ... (sample-to-feature mixup), in addition to the mixed labels. Specifically ...
Machine Learning Glossary - Google for Developers
augmented reality. #image. A technology that superimposes a computer ... Deep Learning Tuning Playbook. Bayesian neural network. A ...
Augmented Memory: Sample-Efficient Generative Molecular Design ...
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a ...
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 ...