Summary Composable Function-Preserving Expansions for Transformer Architectures arxiv.org
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This paper proposes six function-preserving expansions for transformers as a solution to the expensive and time-consuming process of training neural networks, which often requires starting from scratch when increasing network scale.
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Slide Presentation (11 slides)
Key Points
- Training state-of-the-art neural networks is computationally expensive and time-consuming.
- Increasing the scale of a neural network hinders the transfer of knowledge from smaller models.
- The paper proposes six composable function-preserving expansions for transformer architectures.
- The document discusses different transformations that can be applied to expand and modify the dimensions of a Transformer architecture.
- The summary includes references to various research papers and technical reports related to transformer architectures and neural networks.
Summaries
23 word summary
Training neural networks is expensive and time-consuming. Increasing network scale often requires starting from scratch. This paper proposes six function-preserving expansions for transformers.
38 word summary
Training state-of-the-art neural networks is computationally expensive and time-consuming. Increasing the scale of a neural network usually requires starting from scratch and randomly initializing all parameters, hindering knowledge transfer. This paper proposes six composable function-preserving expansions for transformer
350 word summary
Training state-of-the-art neural networks is computationally expensive and time-consuming. Increasing the scale of a neural network usually requires starting from scratch and randomly initializing all parameters, which hinders the transfer of knowledge from smaller models. This paper proposes six composable
The text excerpt discusses composable function-preserving expansions for transformer architectures. It introduces the equations for the feed-forward layers and the multi-head attention (MHA) component. The size of the internal dimension of the MLP component is denoted as "p
The document discusses different transformations that can be applied to expand and modify the dimensions of a Transformer architecture. The first transformation discussed is the MLP expansion, which involves increasing the dimension of the internal representation of the MLP. This can be done by applying specific parameter
The paper discusses different transformations that can be applied to Transformer architectures to expand their functionality while preserving the overall function of the model. The first transformation discussed is called "head addition," which allows for the addition of new attention heads to maintain a consistent number of
The document discusses six transformations that can be applied to a transformer model to increase its scale. These transformations include increasing the size of the MLP internal representation, the number of attention heads, the size of the attention heads output representation, the size of the attention
The summary includes references to various research papers and technical reports related to transformer architectures and neural networks. These papers discuss topics such as scaling vision transformers, growing neural networks using gradient information, deep sparse rectifier neural networks, Gaussian error linear units (GEL
The summarized text is as follows:
The document includes references to various papers and preprints related to transformer architectures and language models. One paper discusses staged training for transformer language models, while another focuses on learning to grow pretrained models for efficient transformer training. Another
The summary discusses the application of composable function-preserving expansions for transformer architectures. The text excerpt includes equations and proofs related to the expansion and layer addition in transformer models. The main ideas presented are:
- The expansion equations demonstrate the transformation of the input