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- Diogo Almeida, Nate Sauder
- ArXiv
- 2015

In machine learning, there is a fundamental trade-off between ease of optimization and expressive power. Neural Networks, in particular, have enormous expressive power and yet are notoriously challenging to train. The nature of that optimization challenge changes over the course of learning. Traditionally in deep learning, one makes a static trade-off… (More)

- Laura Deming, Sasha Targ, Nate Sauder, Diogo Almeida, Chun Jimmie Ye
- ArXiv
- 2016

Each human genome is a 3 billion base pair set of encoding instructions. Decoding the genome using deep learning fundamentally differs from most tasks, as we do not know the full structure of the data and therefore cannot design architectures to suit it. As such, architectures that fit the structure of genomics should be learned not prescribed. Here, we… (More)

- Nate Sauder
- 2013

This paper introduces the basic results of Algebraic Number Theory. Accordingly, having established the existence of integral bases and the result that ideals in Dedekind domains can be uniquely decomposed into prime ideals, we then give the relation between ramification index, residue class degree and the degree of the extension. Moreover, we also… (More)

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