no code implementations • 9 May 2019 • Orhan Ocal, Oguz H. Elibol, Gokce Keskin, Cory Stephenson, Anil Thomas, Kannan Ramchandran
Due to the use of a single encoder, our method can generalize to converting the voice of out-of-training speakers to speakers in the training dataset.
no code implementations • ICLR 2019 • Kamil Nar, Orhan Ocal, S. Shankar Sastry, Kannan Ramchandran
In this work, we study the binary classification of linearly separable datasets and show that linear classifiers could also have decision boundaries that lie close to their training dataset if cross-entropy loss is used for training.
no code implementations • 24 Jan 2019 • Kamil Nar, Orhan Ocal, S. Shankar Sastry, Kannan Ramchandran
We show that differential training can ensure a large margin between the decision boundary of the neural network and the points in the training dataset.