Facebook, Inc.

Arthur David Szlam

Robert D Fergus

Jason Edward Weston

Sainbayar Sukhbaatar

Soumith Chintala

Emily Denton

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.

End-to-end memory networks

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 Gand a discriminative model D. At each level k, the generative model Gmay take as input a noise vector zand may output a generated image {tilde over (h)}. At each level k, the discriminative model Dmay take as input either the generated image {tilde over (h)}or a real image h, and may output a probability that the input was the real image h. The method may further include generating a sample image &#x128;from the generated images {tilde over (h)}, wherein the sample image is based on the probabilities outputted by each of the discriminative models Dand the generated images {tilde over (h)}. The method may further include providing the sample image &#x128;for display.

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New York, NY, United States of America

Arthur David Szlam