Adaptive Computation with Elastic Input Sequence (2301.13195v2)

Abstract: Humans have the ability to adapt the type of information they use, the procedure they employ, and the amount of time they spend when solving problems. However, most standard neural networks have a fixed function type and computation budget regardless of the sample's nature or difficulty. Adaptivity is a powerful paradigm as it not only imbues practitioners with flexibility pertaining to the downstream usage of these models but can also serve as a powerful inductive bias for solving certain challenging classes of problems. In this work, we introduce a new approach called AdaTape, which allows for dynamic computation in neural networks through adaptive tape tokens. AdaTape utilizes an elastic input sequence by equipping an architecture with a dynamic read-and-write tape. Specifically, we adaptively generate input sequences using tape tokens obtained from a tape bank which can be either trainable or derived from input data. We examine the challenges and requirements to obtain dynamic sequence content and length, and propose the Adaptive Tape Reading (ATR) algorithm to achieve both goals. Through extensive experiments on image recognition tasks, we show that AdaTape can achieve better performance while maintaining the computational cost. To facilitate further research, we have released code at https://github.com/google-research/scenic.

• The paper presents AdaTape, which integrates adaptive computation with elastic input sequences to allocate resources dynamically based on sample difficulty.
• It introduces the Adaptive Tape Reading (ATR) algorithm to select variable tokens from a flexible tape bank, enhancing performance on tasks like image recognition.
• AdaTape outperforms standard transformers in tasks such as Parity, providing a scalable method that avoids the need for multiple model trainings.

Adaptive Computation with Elastic Input Sequence

The paper "Adaptive Computation with Elastic Input Sequence" introduces a novel method called AdaTape, which integrates adaptive computation into neural networks through elastic input sequences. The approach is designed to dynamically adjust computational resources based on the nature and difficulty of individual data samples, thus emulating certain aspects of human intelligence.

Core Contributions

AdaTape leverages adaptive tape tokens, facilitating a flexible computation strategy by incorporating dynamic sequence length and content variations. The primary mechanism behind this adaptability is the Adaptive Tape Reading (ATR) algorithm, which selects a variable number of tokens from a predefined tape bank. This tape bank can either consist of trainable vectors or be generated directly from input data, providing a dual approach to implementing adaptivity.

Performance and Evaluation

The paper provides evidence of AdaTape's efficacy through extensive experimentation with image recognition tasks. AdaTape consistently demonstrates improved performance compared to standard transformers while maintaining similar computational cost. This adaptability does not extend the computational budget during training but allows dynamic scaling of computational resources during inference.

In a synthetic setting, such as the Parity task, AdaTape outperforms traditional models like the standard Transformer and Universal Transformer, which are unable to solve this task satisfactorily. The adaptability of AdaTape's inductive bias proves crucial to its success in these scenarios.

Theoretical and Practical Implications

The implications of AdaTape are twofold. Theoretically, it contributes to our understanding of adaptive computation within neural networks, presenting a viable approach to incorporate input sequence variability. Practically, it offers a method to flex across multiple computation budgets, negating the necessity of training multiple distinct models for varying inference demands.

Future Directions

The development of AdaTape opens avenues for further exploration in adaptive neural architectures. Future work could explore the application of AdaTape to other domains beyond image recognition, such as natural language processing or time-series analysis. Additionally, enhancements to the ATR algorithm could further optimize the balance between performance and computational efficiency.

Overall, AdaTape represents a significant step forward in adaptive computation, offering insights that could influence future research and applications in adaptive neural network architectures.