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20 mag 2024 · To address these challenges, we propose a multi-dimension transformer with attention-based filtering (MDT-AF), which redesigns the patch embedding and self-attention mechanism for medical image segmentation.
5 mag 2024 · As a case study, we apply this framework to analyze the widely-adopted Flash Attention optimization. We find that Flash Attention sees roughly an order of magnitude more numeric deviation as compared to Baseline Attention at BF16 when measured during an isolated forward pass.
13 mag 2024 · Therefore, we propose a method based on a self-attention mechanism to focus on the relationships between attention heads, which can improve the performance of models represented by self-attention for short-text classification tasks. In addition, we designed a text augmentation template based on prompt learning with embedded labels.
22 mag 2024 · Attention as an RNN. Leo Feng, Frederick Tung, Hossein Hajimirsadeghi, Mohamed Osama Ahmed, Yoshua Bengio, Greg Mori. The advent of Transformers marked a significant breakthrough in sequence modelling, providing a highly performant architecture capable of leveraging GPU parallelism. However, Transformers are computationally expensive at ...
3 giorni fa · For illustration I used a dimension of 3 (generally it ranges from 64–128). This is standard transformer architecture input. Step 2. Key -> Key’ (transpose) is computed, and multiplied with Query to give QK’ which is N*N. This contains the attention of each token with the rest of the tokens. Below diagram shows the relationship as well.
4 giorni fa · Sparse attention modules. How to use sparse attention with DeepSpeed launcher. How to use individual kernels. How to config sparsity structures. How to support new user defined sparsity structures. In this tutorial we describe how to use DeepSpeed Sparse Attention (SA) and its building-block kernels.
15 mag 2024 · To tackle this issue, we first introduce a new attention dimension, i.e., depth, in addition to existing attentions such as channel-attention, spatial-attention, branch-attention, and self-attention. We present a novel selective depth attention network to treat multi-scale objects symmetrically in various vision tasks.
