Point cloud occlusion recovery with shallow feedforward neural networks

Feedforward Neural Networks

Here x is an input, y is a “label”, v ∈ Rd is a parameter vector, and f(x, y) ∈ Rd is a feature vector that corresponds to a representation of the pair (x, y). Log-linear models have the advantage that the feature vector f(x, y) can include essentially any features of the pair (x, y). However, these features are generally designed by hand, and in practice this is a limitation. It can be laborio...

Feedforward Neural Networks

Feedforward neural networks have been used to perform classifications and to learn functional mappings. This paper compares human performance to feedforward neural networks using back propagation in generating functional relationships from limited data. Many business judgments are made in situations where decision makers are required to infer relationships porn partial, incomplete, and conflict...

Evolving Neural Feedforward Networks

For many practical problem domains the use of neural networks has led to very satisfactory results. Nevertheless the choice of an appropriate, problem specific network architecture still remains a very poorly understood task. Given an actual problem, one can choose a few different architectures, train the chosen architectures a few times and finally select the architecturewith the best behaviou...

TDTO language modeling with feedforward neural networks

In this paper, we describe the use of feedforward neural networks to improve the term-distance term-occurrence (TDTO) language model, previously proposed in [1]−[3]. The main idea behind the TDTO model proposition is to model separately both position and occurrence information of words in the history-context to better estimate n-gram probabilities. Neural networks have been shown to offer a bet...

Feedforward neural networks with constrained weights