The Extrapolation Power of Implicit Models
This addresses the challenge of handling unseen data in machine learning, offering a robust solution for applications like out-of-distribution, geographical, and temporal shifts, though it appears incremental as it builds on existing implicit model concepts.
The paper tackles the problem of extrapolation to unobserved data, where traditional deep neural networks often fail, by investigating implicit deep learning models and finds that they consistently achieve significant performance advantages across various extrapolation scenarios without task-specific design.
In this paper, we investigate the extrapolation capabilities of implicit deep learning models in handling unobserved data, where traditional deep neural networks may falter. Implicit models, distinguished by their adaptability in layer depth and incorporation of feedback within their computational graph, are put to the test across various extrapolation scenarios: out-of-distribution, geographical, and temporal shifts. Our experiments consistently demonstrate significant performance advantage with implicit models. Unlike their non-implicit counterparts, which often rely on meticulous architectural design for each task, implicit models demonstrate the ability to learn complex model structures without the need for task-specific design, highlighting their robustness in handling unseen data.