Add to ORKGState-of-the-art pretrained NLP models contain a hundred million to trillion parameters. Adapters provide a parameter-efficient alternative for the full finetuning in which we can only finetune lightweight neural network layers on top of pretrained weights. Adapter layers are initialized randomly. However, existing work uses the same adapter architecture—i.e., the same adapter layer on top of each layer of the pretrained model—for every dataset, regardless of the properties of the dataset or the amount of available training data. In this work, we introduce adaptable adapters that contain (1) learning different activation functions for different layers and different input data, and (2) a learnable switch to select and only use the beneficial...
Neural text matching models have been used in a range of applications such as question answering and...
It is today acknowledged that neural network language models outperform backoff language models in a...
Pretrained Transformers achieve state-of-the-art performance in various code-processing tasks but ma...
State-of-the-art pretrained NLP models contain a hundred million to trillion parameters. Adapters pr...
State-of-the-art pretrained NLP models contain a hundred million to trillion parameters. Adapters pr...
Transformer-based pre-trained models with millions of parameters require large storage. Recent appro...
The current modus operandi in NLP involves downloading and fine-tuning pre-trained models consisting...
Pre-trained language models received extensive attention in recent years. However, it is still chall...
Massively pre-trained transformer models such as BERT have gained great success in many downstream N...
International audienceAdapter modules were recently introduced as an efficient alternative to fine-t...
Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) are useful for many practical t...
Combining structured information with language models is a standing problem in NLP. Building on prev...
In this paper we present a novel method for adaptation of a multi-layer perceptron neural network (...
Current state-of-the-art results in computer vision depend in part on fine-tuning large pre-trained ...
Fine-tuning the entire set of parameters of a large pretrained model has become the mainstream appro...
Neural text matching models have been used in a range of applications such as question answering and...
It is today acknowledged that neural network language models outperform backoff language models in a...
Pretrained Transformers achieve state-of-the-art performance in various code-processing tasks but ma...
State-of-the-art pretrained NLP models contain a hundred million to trillion parameters. Adapters pr...
State-of-the-art pretrained NLP models contain a hundred million to trillion parameters. Adapters pr...
Transformer-based pre-trained models with millions of parameters require large storage. Recent appro...
The current modus operandi in NLP involves downloading and fine-tuning pre-trained models consisting...
Pre-trained language models received extensive attention in recent years. However, it is still chall...
Massively pre-trained transformer models such as BERT have gained great success in many downstream N...
International audienceAdapter modules were recently introduced as an efficient alternative to fine-t...
Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) are useful for many practical t...
Combining structured information with language models is a standing problem in NLP. Building on prev...
In this paper we present a novel method for adaptation of a multi-layer perceptron neural network (...
Current state-of-the-art results in computer vision depend in part on fine-tuning large pre-trained ...
Fine-tuning the entire set of parameters of a large pretrained model has become the mainstream appro...
Neural text matching models have been used in a range of applications such as question answering and...
It is today acknowledged that neural network language models outperform backoff language models in a...
Pretrained Transformers achieve state-of-the-art performance in various code-processing tasks but ma...