Over the past six years, artificial intelligence has been significantly influenced by 12 foundational research papers. One ...

Abstract: Automatic modulation classification (AMC) is one of the fundamental technologies in adaptive communication systems, supporting various tasks such as spectrum surveillance and cognitive radio ...

Abstract: Accelerated magnetic resonance imaging (MRI) re-construction is a challenging and ill-posed inverse problem due to severe k-space undersampling. In this paper, we propose ReconFormer-EDR, a ...

Just like with real cars, My Winter Car‘s vehicles have a unique Vehicle Identification Number tied to them that can provide information on their history. Why does this matter, you ask? In My Winter ...

We break down the Encoder architecture in Transformers, layer by layer! If you've ever wondered how models like BERT and GPT process text, this is your ultimate guide. We look at the entire design of ...

I want to train pretrain a sentence transformer using TSDAE. We have previously used all-MiniLM-L6-v2 as a checkpoint where we finetuned with MultipleNegativeRankingLoss with the main downstream task ...

In this paper, we present a new tracking architecture with an encoder-decoder transformer as the key component. The encoder models the global spatio-temporal feature dependencies between target ...

As a sequence labeling task, we always use transformer encoder (like BERT) to solve the NER problem, for example, the author in Doc2EDAG use BERT as the first step backbone. However, in the paper, it ...