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Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
A survey of techniques for optimizing transformer inference
Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …
transformer neural networks. The family of transformer networks, including Bidirectional …
[PDF][PDF] Mamba: Linear-time sequence modeling with selective state spaces
Foundation models, now powering most of the exciting applications in deep learning, are
almost universally based on the Transformer architecture and its core attention module …
almost universally based on the Transformer architecture and its core attention module …
Transformers are ssms: Generalized models and efficient algorithms through structured state space duality
While Transformers have been the main architecture behind deep learning's success in
language modeling, state-space models (SSMs) such as Mamba have recently been shown …
language modeling, state-space models (SSMs) such as Mamba have recently been shown …
Flatten transformer: Vision transformer using focused linear attention
The quadratic computation complexity of self-attention has been a persistent challenge
when applying Transformer models to vision tasks. Linear attention, on the other hand, offers …
when applying Transformer models to vision tasks. Linear attention, on the other hand, offers …
Spike-driven transformer
Abstract Spiking Neural Networks (SNNs) provide an energy-efficient deep learning option
due to their unique spike-based event-driven (ie, spike-driven) paradigm. In this paper, we …
due to their unique spike-based event-driven (ie, spike-driven) paradigm. In this paper, we …
Demystify mamba in vision: A linear attention perspective
Mamba is an effective state space model with linear computation complexity. It has recently
shown impressive efficiency in dealing with high-resolution inputs across various vision …
shown impressive efficiency in dealing with high-resolution inputs across various vision …
Spikformer: When spiking neural network meets transformer
We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the
self-attention mechanism. The former offers an energy-efficient and event-driven paradigm …
self-attention mechanism. The former offers an energy-efficient and event-driven paradigm …
Mb-taylorformer: Multi-branch efficient transformer expanded by taylor formula for image dehazing
In recent years, Transformer networks are beginning to replace pure convolutional neural
networks (CNNs) in the field of computer vision due to their global receptive field and …
networks (CNNs) in the field of computer vision due to their global receptive field and …
Structure-aware transformer for graph representation learning
The Transformer architecture has gained growing attention in graph representation learning
recently, as it naturally overcomes several limitations of graph neural networks (GNNs) by …
recently, as it naturally overcomes several limitations of graph neural networks (GNNs) by …