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Attention mechanism in neural networks: where it comes and where it goes
D Soydaner - Neural Computing and Applications, 2022 - Springer
A long time ago in the machine learning literature, the idea of incorporating a mechanism
inspired by the human visual system into neural networks was introduced. This idea is …
inspired by the human visual system into neural networks was introduced. This idea is …
Transformers in time-series analysis: A tutorial
Transformer architectures have widespread applications, particularly in Natural Language
Processing and Computer Vision. Recently, Transformers have been employed in various …
Processing and Computer Vision. Recently, Transformers have been employed in various …
Efficient large language models: A survey
Deepnet: Scaling transformers to 1,000 layers
In this paper, we propose a simple yet effective method to stabilize extremely deep
Transformers. Specifically, we introduce a new normalization function (DeepNorm) to modify …
Transformers. Specifically, we introduce a new normalization function (DeepNorm) to modify …
Stabilizing transformer training by preventing attention entropy collapse
Training stability is of great importance to Transformers. In this work, we investigate the
training dynamics of Transformers by examining the evolution of the attention layers. In …
training dynamics of Transformers by examining the evolution of the attention layers. In …
Cerebras-gpt: Open compute-optimal language models trained on the cerebras wafer-scale cluster
We study recent research advances that improve large language models through efficient
pre-training and scaling, and open datasets and tools. We combine these advances to …
pre-training and scaling, and open datasets and tools. We combine these advances to …
BENDR: Using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
Deep neural networks (DNNs) used for brain–computer interface (BCI) classification are
commonly expected to learn general features when trained across a variety of contexts, such …
commonly expected to learn general features when trained across a variety of contexts, such …