deformers' work
变形者的工作
training deformers
训练变形者
using deformers
使用变形器
new deformers
新的变形器
advanced deformers
高级变形器
researchers are developing new types of deformable neural networks, or deformers.
研究人员正在开发新型的变形神经网络，也就是变形器。
the use of deformers in protein folding prediction is showing great promise.
变形器在蛋白质折叠预测中的应用显示出巨大的潜力。
we need to fine-tune the deformer architecture for optimal performance.
我们需要对变形器架构进行微调以获得最佳性能。
deformers can effectively model long-range dependencies in sequential data.
变形器可以有效地对序列数据中的长距离依赖关系进行建模。
the self-attention mechanism is a key component of many modern deformers.
自注意力机制是许多现代变形器的关键组成部分。
compared to transformers, deformers often require less training data.
与transformer相比，变形器通常需要更少的数据进行训练。
we are exploring the application of deformers to image recognition tasks.
我们正在探索将变形器应用于图像识别任务。
the initial results with the new deformer model are very encouraging.
对新型变形器模型的初步结果非常令人鼓舞。
deformers offer a potential solution for handling variable-length sequences.
变形器提供了一种处理可变长度序列的潜在解决方案。
the team is investigating different deformer configurations for this problem.
团队正在研究解决此问题的不同变形器配置。
scaling up the deformer model presents significant computational challenges.
扩展变形器模型带来了巨大的计算挑战。
deformers' work
变形者的工作
training deformers
训练变形者
using deformers
使用变形器
new deformers
新的变形器
advanced deformers
高级变形器
researchers are developing new types of deformable neural networks, or deformers.
研究人员正在开发新型的变形神经网络，也就是变形器。
the use of deformers in protein folding prediction is showing great promise.
变形器在蛋白质折叠预测中的应用显示出巨大的潜力。
we need to fine-tune the deformer architecture for optimal performance.
我们需要对变形器架构进行微调以获得最佳性能。
deformers can effectively model long-range dependencies in sequential data.
变形器可以有效地对序列数据中的长距离依赖关系进行建模。
the self-attention mechanism is a key component of many modern deformers.
自注意力机制是许多现代变形器的关键组成部分。
compared to transformers, deformers often require less training data.
与transformer相比，变形器通常需要更少的数据进行训练。
we are exploring the application of deformers to image recognition tasks.
我们正在探索将变形器应用于图像识别任务。
the initial results with the new deformer model are very encouraging.
对新型变形器模型的初步结果非常令人鼓舞。
deformers offer a potential solution for handling variable-length sequences.
变形器提供了一种处理可变长度序列的潜在解决方案。
the team is investigating different deformer configurations for this problem.
团队正在研究解决此问题的不同变形器配置。
scaling up the deformer model presents significant computational challenges.
扩展变形器模型带来了巨大的计算挑战。
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