When are Deep Networks really better than Decision Forests at small sample sizes, and how?

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.

Leveraging semantically similar queries for ranking via combining representations

no code implementations • 23 Jun 2021 • Hayden S. Helm, Marah Abdin, Benjamin D. Pedigo, Shweti Mahajan, Vince Lyzinski, Youngser Park, Amitabh Basu, Piali~Choudhury, Christopher M. White, Weiwei Yang, Carey E. Priebe

In modern ranking problems, different and disparate representations of the items to be ranked are often available.

Learning-To-Rank

Omnidirectional Transfer for Quasilinear Lifelong Learning

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.

Federated Learning Transfer Learning