Adaptive Computation: Enhancing Neural Networks with Dynamic Computation Budgets

Adaptive Computation: A Breakthrough in AI

Adaptive computation is a game-changer in the world of artificial intelligence (AI). Traditional neural networks have fixed computation power, meaning they spend the same amount of resources processing all inputs. But with adaptive computation, AI systems can adjust their behavior and computation capacity based on input complexity.

This flexibility has two major advantages. First, it provides an inductive bias that helps tackle complex tasks. For example, in arithmetic problems with varying depths, adaptive computation allows models to allocate different computational steps to different inputs. Second, adaptive computation offers practitioners the ability to tune the cost of inference. Models with dynamic computation budgets can allocate more resources to process new inputs, improving accuracy.

There are different ways to make neural networks adaptive. One method is conditional computation, where a subset of parameters is activated based on the input. Another approach involves dynamic computation budgets, which allocate computation resources based on the complexity of the input.

Recent research has shown the potential of adaptive computation in transformers, such as T5, GPT-3, PaLM, and ViT. By adjusting the computation budget, these models can improve performance on tasks where transformers struggle. The Adaptive Computation Time (ACT) algorithm and the Universal Transformer are examples of models that incorporate adaptive computation.

In our paper, “Adaptive Computation with Elastic Input Sequence,” we introduce a new model called AdaTape. AdaTape is a transformer-based architecture that uses adaptive computation in a unique way. It dynamically selects a variable-sized sequence of tape tokens for each input, based on its complexity.

AdaTape employs two approaches for generating tape tokens. In an input-driven bank, tokens are extracted from the input using a different point of view, such as a different image resolution. In a learnable bank, trainable vectors serve as tape tokens. This approach allows AdaTape to adjust its computation budget based on the complexity of each input, resulting in improved performance.

In experiments, AdaTape outperformed other baselines on challenging tasks like the parity task and image classification. Its lightweight recurrence mechanism enables better performance on unsolvable tasks for standard transformers. Additionally, AdaTape offers a favorable quality and cost tradeoff, surpassing alternative adaptive transformer models.

Adaptive computation is revolutionizing AI. It allows models to adjust their behavior and computation capacity based on input complexity, leading to better performance on various tasks. AdaTape, with its unique approach to adaptive computation, offers improved accuracy, efficiency, and flexibility.

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