Adaptive Computation: An Overview of AdaTape
Adaptive computation is a powerful concept in the field of artificial intelligence. It refers to the ability of a machine learning system to adjust its behavior according to changes in the environment. Unlike conventional neural networks, which have a fixed function and computation capacity, adaptive models can vary their computational budget based on the complexity of the input.
The significance of adaptive computation lies in two key aspects. Firstly, it provides an inductive bias that aids in solving challenging tasks. For example, by allowing different numbers of computational steps for different inputs, a model can effectively solve arithmetic problems that involve hierarchies of different depths. Secondly, adaptive computation offers flexibility in tuning the cost of inference. Models can allocate more computational resources to process new inputs, leading to improved performance.
There are different ways to make neural networks adaptive. One approach involves using different functions or computation budgets for different inputs. This is achieved through conditional computation, where a subset of parameters is selectively activated based on the input. Another method involves dynamic computation budgets, where the computation resources are allocated based on the complexity of the input. Recent research has shown that adaptive models outperform standard neural networks on various tasks, such as image classification and algorithmic tasks.
In our research paper titled “Adaptive Computation with Elastic Input Sequence,” we introduce a new model called AdaTape. It is a Transformer-based architecture that utilizes both adaptive function types and dynamic computation budgets. AdaTape employs a unique approach to adaptivity by using an adaptive tape reading mechanism to determine the number of tape tokens added to each input.
To create the tape tokens, AdaTape uses a “tape bank” that stores candidate tokens. There are two methods for creating the tape bank: an input-driven bank and a learnable bank. The input-driven bank extracts tokens from the input, allowing dynamic access to information from different perspectives. On the other hand, the learnable bank uses trainable vectors as tape tokens, providing flexibility to adjust the computation budget based on input complexity.
The selected tape tokens are then appended to the original input and processed by the transformer layers. AdaTape uses the same multi-head attention for all tokens but employs separate feed-forward networks for the input and tape tokens. This approach improves the model’s performance.
We evaluated AdaTape on challenging tasks like the parity task, which the standard Transformer struggles to solve. AdaTape demonstrated superior performance by incorporating a lightweight recurrence within its input selection mechanism. In terms of image classification, AdaTape outperforms other adaptive transformer baselines, offering a better balance between quality and cost tradeoff.
In conclusion, adaptive computation is a powerful concept in AI that allows models to adjust their behavior based on input complexity. AdaTape, our new model, combines adaptive function types and dynamic computation budgets to achieve superior performance in various tasks. Its unique approach to adaptivity makes it efficient and effective.