The Extrapolation Power of Implicit Models
Juliette Decugis, Alicia Y. Tsai, Max Emerling, Ashwin Ganesh, Laurent, El Ghaoui
TL;DR
This paper explores the ability of implicit deep learning models to effectively extrapolate to unobserved data types, showing they outperform traditional models across various shift scenarios due to their adaptable structure.
Contribution
It introduces the investigation of implicit models' extrapolation capabilities and demonstrates their robustness and superior performance in handling unseen data without task-specific design.
Findings
Implicit models outperform traditional models in extrapolation tasks.
Implicit models adapt better to out-of-distribution, geographical, and temporal shifts.
They learn complex structures without task-specific architectural tuning.
Abstract
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.
Peer Reviews
Decision·Submitted to ICLR 2024
The authors work is a convincing experimental study showing the merits of implicit models on various tasks encompassing both regression and classification (max). It is presented in a clear way.
Since I am not very familiar with implicit models, a reminder on how implicit models are trained and / or how and why they might converge, would have been a nice addition to the paper. Moreover, most training details are not presented in the main text. Finally, I found rather difficult to evaluate the last section of the authors work.
- The authors study various applications to show that implicit models indeed have superior extrapolation power. - Analysis based on the closed loop feedback is a novel analysis that I haven't read earlier.
- _Architecture Extraction_ and _depth adaptability_ is not a novel contribution. Several publications on implicit modeling exploit this feature and have written about it. [1] [2] [3] - The main contribution of the paper is not clear -- is it just showing implicit models are better in extrapolating in some tasks? In this case, the title of the paper should be revised. Since multiple papers have showed that implicit models have better extrapolation properties [4]. - ImplicitRNN is not a novel de
This paper demonstrates the extrapolation capabilities of implicit models by applying them to a series of mathematical problems with data generated from underlying functions. This study further explores how implicit models perform extrapolation on real-world applications with noisy datasets, comparing their performance to non-implicit models. Both ablation studies and an analysis are included to highlight the adaptability of implicit models, the importance of close-loop feedback, and how feature
1 This paper studies the benefits of implicit models in terms of their extrapolation capabilities. However, it primarily describes this empirical finding and lacks a convincing analysis of its underlying causes. Specifically, this paper argues that the strong extrapolation capabilities of implicit models can mainly be attributed to two factors: the ability to adapt to varying depths and the inclusion of feedback in their computational graph. Nevertheless, the exact relationship between these two
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Taxonomy
TopicsSemantic Web and Ontologies