In this paper, we leveraged the fact that deep models, including both random forests and deep-nets, learn internal representations which are unions of polytopes with affine activation functions to conceptualize them both as partitioning rules of the feature space.
In a benchmark suite containing 72 classification problems (the OpenML-CC18 data suite), we illustrate that our approach, Stream Decision Forest (SDF), does not suffer from either of the aforementioned limitations.
2 code implementations • 31 Aug 2021 • Haoyin Xu, Kaleab A. Kinfu, Will LeVine, Sambit Panda, Jayanta Dey, Michael Ainsworth, Yu-Chung Peng, Madi Kusmanov, Florian Engert, Christopher M. White, Joshua T. Vogelstein, Carey E. Priebe
Empirically, we compare these two strategies on hundreds of tabular data settings, as well as several vision and auditory settings.
1 code implementation • 27 Apr 2020 • Joshua T. Vogelstein, Jayanta Dey, Hayden S. Helm, Will LeVine, Ronak D. Mehta, Ali Geisa, Haoyin Xu, Gido M. van de Ven, Emily Chang, Chenyu Gao, Weiwei Yang, Bryan Tower, Jonathan Larson, Christopher M. White, Carey E. Priebe
But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data.