A Survey on Making Deep Learning Models Smaller, Faster, and Better

https://arxiv.org/abs/2106.08962

Decoding-Time Controlled Text Generation with Experts and Anti-Experts

https://arxiv.org/abs/2105.03023

https://github.com/alisawuffles/DExperts

Graph Neural Networks for Natural Language Processing: A Survey

https://arxiv.org/abs/2106.06090

A Survey of Transformers

https://arxiv.org/abs/2106.04554

Pretrained Language Models for Text Generation: A Survey

https://arxiv.org/abs/2105.10311

A Survey of Data Augmentation Approaches for NLP

https://arxiv.org/abs/2105.03075

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

https://arxiv.org/abs/2104.13478

The NLP Cookbook: Modern Recipes for Transformer based Deep Learning Architectures

https://arxiv.org/abs/2104.10640

A Practical Survey on Faster and Lighter Transformers

https://arxiv.org/abs/2103.14636

Requirement Engineering Challenges for AI-intense Systems Development

https://arxiv.org/abs/2103.10270

Model Complexity of Deep Learning: A Survey

https://arxiv.org/abs/2103.05127

A Survey on Visual Transformer

https://arxiv.org/abs/2012.12556

A Comprehensive Survey on Graph Neural Networks

https://arxiv.org/abs/1901.00596

标签：Learning,arxiv,Machine,abs,Survey,https,Paper,org
来源： https://www.cnblogs.com/songyuejie/p/14912661.html