Search Results for author:
Found 7 papers, 1 papers with code
PathFinder: Discovering Decision Pathways in Deep Neural Networks
Explainability is becoming an increasingly important topic for deep neural networks.
Hierarchical Mixtures of Generators for Adversarial Learning
There is also a discriminator that is trained to discriminate such fake samples from true samples of the distribution; at the same time, the generator is trained to generate fakes that the discriminator cannot tell apart from the true samples.
Dropout Regularization in Hierarchical Mixture of Experts
Dropout is a very effective method in preventing overfitting and has become the go-to regularizer for multi-layer neural networks in recent years.
Continuously Constructive Deep Neural Networks
Traditionally, deep learning algorithms update the network weights whereas the network architecture is chosen manually, using a process of trial and error.
Distributed Decision Trees
Recently proposed budding tree is a decision tree algorithm in which every node is part internal node and part leaf.
Autoencoder Trees
We also see that the autoencoder tree captures hierarchical representations at different granularities of the data on its different levels and the leaves capture the localities in the input space.
Multivariate Comparison of Classification Algorithms
For example, error is the sum of false positives and false negatives and a univariate test on error cannot make a distinction between these two sources, but a 2-variate test can.
