Interpretation of the Transformer and Improvement of the Extractor

• URL: http://arxiv.org/abs/2311.12678v1
• Date: Tue, 21 Nov 2023 15:36:20 GMT
• Title: Interpretation of the Transformer and Improvement of the Extractor
• Authors: Zhe Chen
• Abstract summary: It has been over six years since the Transformer architecture was put forward. Surprisingly, the vanilla Transformer architecture is still widely used today. The lack of deep understanding and comprehensive interpretation of the Transformer architecture makes it more challenging to improve the Transformer architecture.
• Score: 3.9693969407364427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It has been over six years since the Transformer architecture was put forward. Surprisingly, the vanilla Transformer architecture is still widely used today. One reason is that the lack of deep understanding and comprehensive interpretation of the Transformer architecture makes it more challenging to improve the Transformer architecture. In this paper, we first interpret the Transformer architecture comprehensively in plain words based on our understanding and experiences. The interpretations are further proved and verified. These interpretations also cover the Extractor, a family of drop-in replacements for the multi-head self-attention in the Transformer architecture. Then, we propose an improvement on a type of the Extractor that outperforms the self-attention, without introducing additional trainable parameters. Experimental results demonstrate that the improved Extractor performs even better, showing a way to improve the Transformer architecture.

Related papers

• Disentangling Visual Transformers: Patch-level Interpretability for Image Classification [2.899118947717404]
  We propose Hindered Transformer (HiT), a novel interpretable by design architecture inspired by visual transformers. HiT can be interpreted as a linear combination of patch-level information. We show that the advantages of our approach in terms of explicability come with a reasonable trade-off in performance.
  arXiv  Detail & Related papers  (2025-02-24T14:30:29Z)
• Enhancing Transformers for Generalizable First-Order Logical Entailment [51.04944136538266]
  This paper investigates the generalizable first-order logical reasoning ability of transformers with their parameterized knowledge. The first-order reasoning capability of transformers is assessed through their ability to perform first-order logical entailment. We propose a more sophisticated, logic-aware architecture, TEGA, to enhance the capability for generalizable first-order logical entailment in transformers.
  arXiv  Detail & Related papers  (2025-01-01T07:05:32Z)
• What Does It Mean to Be a Transformer? Insights from a Theoretical Hessian Analysis [8.008567379796666]
  The Transformer architecture has inarguably revolutionized deep learning. At its core, the attention block differs in form and functionality from most other architectural components in deep learning. The root causes behind these outward manifestations, and the precise mechanisms that govern them, remain poorly understood.
  arXiv  Detail & Related papers  (2024-10-14T18:15:02Z)
• Body Transformer: Leveraging Robot Embodiment for Policy Learning [51.531793239586165]
  Body Transformer (BoT) is an architecture that leverages the robot embodiment by providing an inductive bias that guides the learning process. We represent the robot body as a graph of sensors and actuators, and rely on masked attention to pool information throughout the architecture. The resulting architecture outperforms the vanilla transformer, as well as the classical multilayer perceptron, in terms of task completion, scaling properties, and computational efficiency.
  arXiv  Detail & Related papers  (2024-08-12T17:31:28Z)
• On the Expressive Power of a Variant of the Looped Transformer [83.30272757948829]
  We design a novel transformer block, dubbed AlgoFormer, to empower transformers with algorithmic capabilities. The proposed AlgoFormer can achieve significantly higher in algorithm representation when using the same number of parameters. Some theoretical and empirical results are presented to show that the designed transformer has the potential to be smarter than human-designed algorithms.
  arXiv  Detail & Related papers  (2024-02-21T07:07:54Z)
• iTransformer: Inverted Transformers Are Effective for Time Series Forecasting [62.40166958002558]
  We propose iTransformer, which simply applies the attention and feed-forward network on the inverted dimensions. The iTransformer model achieves state-of-the-art on challenging real-world datasets.
  arXiv  Detail & Related papers  (2023-10-10T13:44:09Z)
• What Makes for Good Tokenizers in Vision Transformer? [62.44987486771936]
  transformers are capable of extracting their pairwise relationships using self-attention. What makes for a good tokenizer has not been well understood in computer vision. Modulation across Tokens (MoTo) incorporates inter-token modeling capability through normalization. Regularization objective TokenProp is embraced in the standard training regime.
  arXiv  Detail & Related papers  (2022-12-21T15:51:43Z)
• Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages [120.74406230847904]
  TP-Transformer augments the traditional Transformer architecture to include an additional component to represent structure. The second method imbues structure at the data level by segmenting the data with morphological tokenization. We find that each of these two approaches allows the network to achieve better performance, but this improvement is dependent on the size of the dataset.
  arXiv  Detail & Related papers  (2022-08-11T22:42:24Z)
• Transformers in Time-series Analysis: A Tutorial [0.0]
  Transformer architecture has widespread applications, particularly in Natural Language Processing and computer vision. This tutorial provides an overview of the Transformer architecture, its applications, and a collection of examples from recent research papers in time-series analysis.
  arXiv  Detail & Related papers  (2022-04-28T05:17:45Z)
• Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors [15.348047288817478]
  We propose to use dictionary learning to open up "black boxes" as linear superpositions of transformer factors. Through visualization, we demonstrate the hierarchical semantic structures captured by the transformer factors. We hope this visualization tool can bring further knowledge and a better understanding of how transformer networks work.
  arXiv  Detail & Related papers  (2021-03-29T20:51:33Z)
• Do Transformer Modifications Transfer Across Implementations and Applications? [52.09138231841911]
  We comprehensively evaluate many of these modifications in a shared experimental setting. We find that most modifications do not meaningfully improve performance. Most Transformer variants we found beneficial were either developed in the same that we used or are relatively minor changes.
  arXiv  Detail & Related papers  (2021-02-23T22:44:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.