Neural Information Processing Systems http://nips.cc/

The visual properties of images from the first-person and third-person views are very different [34].

One approach to defend from adversarial perturbations is to mask gradients.

V oice cloning is a highly desired feature for personalized speech interfaces.

To this end, we use algorithms developed in the gradient-based multi-objective optimization literature. These algorithms are not directly applicable to large-scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks. We therefore propose an upper bound for the multi-objective loss and show that it can be optimized efficiently. We further prove that optimizing this upper bound yields a Pareto optimal solution under realistic assumptions.

Neural Information Processing Systems http://nips.cc/

Such methods are therefore not well suited for deep networks. In this work, we propose a novel loss function to impose a margin on any chosen set of layers of a deep network (including input and hidden layers).

Both the character and word piece models are developed for acoustic modeling, and the corresponding RNN based language models are used for beam search decoding.

We propose a general, theoretically justified mechanism for processing missing data by neural networks. Our idea is to replace typical neuron's response in the

Spiking neural networks (SNNs) are positioned to enable spatio-temporal information processing and ultra-low power event-driven neuromorphic hardware.