Abstract: In one embodiment, a system includes an automatic speech recognition (ASR) module, a natural-language understanding (NLU) module, a dialog manager, one or more agents, an arbitrator, a delivery system, one or more processors, and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to receive a user input, process the user input using the ASR module, the NLU module, the dialog manager, one or more of the agents, the arbitrator, and the delivery system, and provide a response to the user input.

Abstract: Embodiments are disclosed for predicting a response (e.g., an answer responding to a question) using an end-to-end memory network model. A computing device according to some embodiments includes embedding matrices to convert knowledge entries and an inquiry into feature vectors including the input vector and memory vectors. The device further execute a hop operation to generate a probability vector based on an input vector and a first set of memory vectors using a continuous weighting function (e.g., softmax), and to generate an output vector as weighted combination of a second set of memory vectors using the elements of the probability vector as weights. The device can repeat the hop operation for multiple times, where the input vector for a hop operation depends on input and output vectors of previous hop operation(s). The device generates a predicted response based on at least the output of the last hop operation.

Abstract: In one embodiment, a method includes accessing a plurality of generative adversarial networks (GANs) that are each applied to a particular level k of a Laplacian pyramid. Each GAN may comprise a generative model Gk and a discriminative model Dk. At each level k, the generative model Gk may take as input a noise vector zk and may output a generated image {tilde over (h)}k. At each level k, the discriminative model Dk may take as input either the generated image {tilde over (h)}k or a real image hk, and may output a probability that the input was the real image hk. The method may further include generating a sample image ?k from the generated images {tilde over (h)}k, wherein the sample image is based on the probabilities outputted by each of the discriminative models Dk and the generated images {tilde over (h)}k. The method may further include providing the sample image ?k for display.

Abstract: In one embodiment, a method includes accessing a plurality of generative adversarial networks (GANs) that are each applied to a particular level k of a Laplacian pyramid. Each GAN may comprise a generative model Gk and a discriminative model Dk. At each level k, the generative model Gk may take as input a noise vector zk and may output a generated image {tilde over (h)}k. At each level k, the discriminative model Dk may take as input either the generated image {tilde over (h)}k or a real image hk, and may output a probability that the input was the real image hk. The method may further include generating a sample image ?k from the generated images {tilde over (h)}k, wherein the sample image is based on the probabilities outputted by each of the discriminative models Dk and the generated images {tilde over (h)}k. The method may further include providing the sample image ?k for display.

Abstract: Embodiments are disclosed for predicting a response (e.g., an answer responding to a question) using an end-to-end memory network model. A computing device according to some embodiments includes embedding matrices to convert knowledge entries and an inquiry into feature vectors including the input vector and memory vectors. The device further execute a hop operation to generate a probability vector based on an input vector and a first set of memory vectors using a continuous weighting function (e.g., softmax), and to generate an output vector as weighted combination of a second set of memory vectors using the elements of the probability vector as weights. The device can repeat the hop operation for multiple times, where the input vector for a hop operation depends on input and output vectors of previous hop operation(s). The device generates a predicted response based on at least the output of the last hop operation.