The Future of Artificial Intelligence

NeurIPS 2022 poster of the paper:

Dubey A., Radenovic F., Mahajan D., Interpretability via Polynomials, NeurIPS 2022, [arxiv]

which introduces an efficient architecture called Scalable Polynomial Additive Models (SPAM) aiming to balance high expressivity and interpretability. Interesting work that resembles more traditional ML and proposes an alternative to DNNs. 

Recently presented this paper at NeurIPS 2022 workshop on distribution shifts. We demonstrate the higher robustness of implicit models on out of distribution data as compared to classical deep learning architectures (MLP, LSTM, Transformers and Google's Neural Arithmetic Logic Units). We speculate that implicit models, unrestricted in their layer number, can adapt and grow for more complex data.