36 citations · 57 across the 6 of their papers we have counts for
6 papers
The Nonlinearity Coefficient - A Practical Guide to Neural Architecture Design
George Philipp
In essence, a neural network is an arbitrary differentiable, parametrized function. Choosing a neural network architecture for any task is as complex as searching the space of thos…
The Nonlinearity Coefficient - Predicting Generalization in Deep Neural Networks
George Philipp, Jaime G. Carbonell
For a long time, designing neural architectures that exhibit high performance was considered a dark art that required expert hand-tuning. One of the few well-known guidelines for a…
Nonparametric Neural Networks
George Philipp, Jaime G. Carbonell
Automatically determining the optimal size of a neural network for a given task without prior information currently requires an expensive global search and training many networks f…
Stability Selection for Structured Variable Selection
George Philipp, Seunghak Lee, Eric P. Xing
In variable or graph selection problems, finding a right-sized model or controlling the number of false positives is notoriously difficult. Recently, a meta-algorithm called Stabil…
Interactions between Health Searchers and Search Engines
George Philipp, Ryen W. White
The Web is an important resource for understanding and diagnosing medical conditions. Based on exposure to online content, people may develop undue health concerns, believing that…
The exploding gradient problem demystified - definition, prevalence, impact, origin, tradeoffs, and solutions
George Philipp, Dawn Song, Jaime G. Carbonell
Whereas it is believed that techniques such as Adam, batch normalization and, more recently, SeLU nonlinearities "solve" the exploding gradient problem, we show that this is not th…