Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an output sequence of discrete tokens using a diffusion model. In one aspect, a method includes generating, by using the diffusion model, a final latent representation of the sequence of discrete tokens that includes a determined value for each of a plurality of latent variables; applying a de-embedding matrix to the final latent representation of the output sequence of discrete tokens to generate a de-embedded final latent representation that includes, for each of the plurality of latent variables, a respective numeric score for each discrete token in a vocabulary of multiple discrete tokens; selecting, for each of the plurality of latent variables, a discrete token from among the multiple discrete tokens in the vocabulary that has a highest numeric score; and generating the output sequence of discrete tokens that includes the selected discrete tokens.

Abstract: A query processing system is described which receives a query input comprising an input token string and also at least one data item having a second, different modality, and generates a corresponding output token string.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model to perform a machine learning task. In one aspect, a method performed by one or more computer is described. The method includes: obtaining data defining a compute budget that characterizes an amount of computing resources allocated for training a machine learning model to perform a machine learning task; processing the data defining the compute budget using an allocation mapping, in accordance with a set of allocation mapping parameters, to generate an allocation tuple defining: (i) a target model size for the machine learning model, and (ii) a target amount of training data for training the machine learning model; instantiating the machine learning model, where the machine learning model has the target model size; and obtaining the target amount of training data for training the machine learning model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network having one or more conditional computation layers, where each conditional computation layer includes a gating sub-layer having multiple gating parameters and an expert sub-layer having multiple expert neural networks.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final output sequence. In one aspect, a method comprises: receiving a current output sequence comprising one or more current output segments; receiving a set of reference segments and a respective reference segment embedding of each reference segment that has been generated using an embedding neural network; for each current output segment: processing the current output segment using the embedding neural network to generate a current output segment embedding of the current output segment; and selecting k most similar reference segments to the current output segment using the reference segment embeddings and the current output segment embedding; and processing the current output sequence and the k most similar reference segments for each current output segment to generate an additional output segment that follows the current output sequence in the final output sequence.