10 citations · 34 across the 12 of their papers we have counts for
4 papers · 1 filter
If dropout limits trainable depth, does critical initialisation still matter? A large-scale statistical analysis on ReLU networks
Arnu Pretorius, Elan van Biljon, Benjamin van Niekerk +6
Recent work in signal propagation theory has shown that dropout limits the depth to which information can propagate through a neural network. In this paper, we investigate the effe…
Estimation of Body Mass Index from Photographs using Deep Convolutional Neural Networks
Adam Pantanowitz, Emmanuel Cohen, Philippe Gradidge +4
Obesity is an important concern in public health, and Body Mass Index is one of the useful (and proliferant) measures. We use Convolutional Neural Networks to determine Body Mass I…
Learning Portable Representations for High-Level Planning
Steven James, Benjamin Rosman, George Konidaris
We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous environments. We show that these abstract representat…
Transfer Learning for Prosthetics Using Imitation Learning
Montaser Mohammedalamen, Waleed D. Khamies, Benjamin Rosman
In this paper, We Apply Reinforcement learning (RL) techniques to train a realistic biomechanical model to work with different people and on different walking environments. We benc…