Since the provider's goal of maximizing revenue can only be achieved

Arecent lineofresearch hasshownthatgradient-based algorithms withrandom initialization can converge to the global minima of the training loss for overparameterized (i.e.,sufficiently wide)deepneuralnetworks. However,thecondition onthewidth oftheneural networktoensure theglobal convergence isvery stringent, which is often a high-degree polynomial in the training sample size n (e.g., O(n24)).

Through extensive experiments we show that some existing saliency methods are independent both of the model and of the data generating process.

Consider a standard decision making setting that includes a set of possible actions (decisions or policies), and a set of individuals who assign utilities to the actions.

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

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