Marks & Clerk GST

Methods, systems, and apparatus for reducing latency of configuration of image sensor and image signal processor. A computing system can include a machine learning (ML) processing engine that can process denoising diffusion ML models for execution on statically compiled ML accelerators. The system can determine that a partitioned graph representation, which includes connected subgraphs that each represent at least one layer of the neural network of the ML model, forms a directed acyclic graph. The system can insert cache nodes in the graph representation, where each cache node corresponds to a respective subgraph and is configured to cache output of the respective subgraph. The system can generate an execution dataflow graph including a plurality of iterations of the partitioned graph representation, where, at one or more iterations during model inference operations, the execution dataflow graph uses inputs from cache nodes and excludes execution of subgraphs corresponding to the cache nodes.

Methods and systems for fine-tuning a machine-learning model using a fusion-frame representation are provided. Model weights associated with a layer of a machine-learning model are updated using a projection of a block diagonal matrix into a fusion frame, with each block of the block diagonal matrix projected onto a respective subspace of the fusion frame. This reduces the number of parameters that need to be handled during fine-tuning, reducing demand on processor and memory resources, as it is not necessary to fine-tune all the weights, only the elements of the block diagonal matrix. The use of fusion frames also enables targeting of particular parameters or parameter sets using the subspaces of the fusion frame. Furthermore, embodiments described herein show how the fusion frame implementation lends itself to parallelisation of calculations associated with each respective subspace.

Methods, systems, and computer storage media are provided for training generative neural network systems, such as Large Language Models (LLMs) or Vision-Language Models (VLMs), using multiple reward models. The process involves generating one or more output sequences from a training input sequence and processing these examples using a plurality of different reward models to generate respective reward values. These reward values are combined to obtain an aggregated reward, which may be calculated as a product of the values, a weighted geometric mean, or a Nash score. The generative neural network is trained using an objective function determined using the aggregated reward, such as a cross-entropy loss or a contrastive objective function, often utilizing log score differences between the system and a reference model. These techniques allow for multi-objective alignment and are adapted for implementation on parallel processing computer systems.

This document describes systems and techniques for delivering patch partitions to local function managers of a computing device. For example, a system includes a plurality of local function managers in a computing device, each of the local function managers is configured to identify an instance for execution of one or more patch partitions, each of the patch partitions including executable code to direct a local function and generate a patch request for the one or more patch partitions. A global function controller is configured to access a group of patch partitions including each of the one or more patch partitions for each of the plurality of local function managers and to respond to receipt of the patch request by delivering one or more requested patch partitions to the local function manager generating the patch request.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating sequences of network outputs using a generative machine learning model following a learned generation order. In one aspect, a method comprises receiving a network input and generating a sequence of network outputs using a generative model, the generating comprising, at each of a plurality of iterations: identifying a set of candidate positions for the iteration; determining uncertainty scores for each of the set of candidate positions using an uncertainty scoring neural network; selecting a position of the sequence of network outputs from the set of candidate positions using the uncertainty scores; and generating the network output at the selected position of the sequence of network outputs using the generative model.

Provided are computer-implemented systems and methods which improve embedding generation model performance with respect to localized quality issues. In particular, the proposed techniques can operate to improve localized embedding model performance without incurring significant re-training costs and/or re-deployment costs associated with full model re-training.

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Example embodiments of the present disclosure provide for an example method including obtaining data associated with media channels. The example method includes obtaining, by a computing device of a first platform, first channel data associated with a first media channel of an entity, the first media channel being on a first platform. Additionally, the method can include obtaining second channel data associated with a second media channel of the entity, the second media channel being on a second platform. Moreover, the method can include processing, using a machine-learning model, the first channel data and the second channel data to determine an allocation of resources to the first media channel and the second media channel. Furthermore, the method can include generating and presenting a recommendation based on the determination.

A computer-implemented method comprising: receiving, by an application (108) executing on a user device (102) that is not an application store, a single content response from a content server (120), wherein the single content response comprises both digital content (112) referencing a second application that is not installed on the user device and application store data (116) for the second application; in response to receiving the single content response and when the application is an active application, providing, by the active application and in a user interface (110) of the active application, the digital content concurrently with an application store user interface (114) that includes (i) the application store data for the second application and (ii) an install element (118) for triggering installation of the second application on the user device from within the active application; detecting user interaction with the install element provided within the user interface of the active application; and in response to detecting the user interaction with the install element, triggering a download of the second application independent of launching an application for the application store.

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