Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting a neural network to perform a particular machine learning task while satisfying a set of constraints.

Abstract: A browser-based tool is disclosed for providing context-based assistance during web browsing. An example method involves receiving a prompt pertaining to main content displayed in a first display area, extracting content from the main content, receiving generated content based on the extracted content, and displaying the generated content in a second display area while the main content remains displayed in the first display area. This innovative approach streamlines the search process by providing users with relevant generated content based on the content they are currently viewing, thereby improving efficiency in navigating online information.

Abstract: A browser-based tool is disclosed for providing context-based assistance during web browsing. An example method involves receiving a contextual search request pertaining to main content displayed in a browser's display area, extracting content from the main content, receiving a contextual suggestion based on the extracted content, and displaying the contextual suggestion in a designated contextual search area within the browser. This innovative approach streamlines the search process by providing users with relevant suggestions based on the content they are currently viewing, thereby improving efficiency in navigating online information.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using neural networks that include one or more conditional convolutional layers. A conditional convolutional layer has a plurality of kernels and determines a respective input-dependent weight for each of the plurality of kernels and generates an input-dependent kernel by computing a weighted sum of the plurality of kernels in accordance with the respective input-dependent weights.

Abstract: Provided are neural architecture search techniques that have improved computational efficiency via performance of an initial constraint evaluation and improved gradient update approach. Further, the proposed approaches provide significant improvements for certain modalities of input data, such as tabular datasets.

Abstract: Methods, systems, and apparatus, including computer-readable media, are described for processing an input image using a convolutional neural network (CNN). The CNN includes a sequence of layer blocks. Each of a first subset of the layer blocks in the sequence is configured to perform operations that include: i) receiving an input feature map for the layer block, ii) generating an expanded feature map from the input feature map using a group convolution, and iii) generating a reduced feature map from the expanded feature map. The input feature map is an h w feature map with c1 channels. The expanded feature map is an h w feature map with c2 channels, whereas the reduced feature map is an h w feature map with c1 channels. C2 is greater than c1. An output feature map is generated for the layer block from the reduced feature map.

Abstract: Methods, systems, and apparatus, including computer-readable media, are described for processing an input image using integrated circuit that implements a convolutional neural network with a group convolution layer. The processing includes determining a mapping of partitions along a channel dimension of an input feature map to multiply accumulate cells (MACs) in a computational unit of the circuit and applying a group convolution to the input feature map. Applying the group convolution includes, for each partition: providing weights for the group convolution layer to a subset of MACs based on the mapping; providing, via an input bus of the circuit, an input of the feature map to each MAC in the subset; and computing, at each MAC in the subset, a product using the input and a weight for the group convolution layer. An output feature map is generated for the group convolution layer based on an accumulation of products.

Abstract: A browser-based tool is disclosed for providing context-based assistance during web browsing. An example method involves receiving a contextual search request pertaining to main content displayed in a browser's display area, extracting content from the main content, receiving a contextual suggestion based on the extracted content, and displaying the contextual suggestion in a designated contextual search area within the browser. This innovative approach streamlines the search process by providing users with relevant suggestions based on the content they are currently viewing, thereby improving efficiency in navigating online information.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting a neural network to perform a particular machine learning task while satisfying a set of constraints.

Abstract: Systems and methods of the present disclosure are directed to a method for generating a machine-learned multitask model configured to perform tasks. The method can include obtaining a machine-learned multitask search model comprising candidate nodes. The method can include obtaining tasks and machine-learned task controller models associated with the tasks. As an example, for a task, the method can include using the task controller model to route a subset of the candidate nodes in a machine-learned task submodel for the corresponding task. The method can include inputting task input data to the task submodel to obtain a task output. The method can include generating, using the task output, a feedback value based on an objective function. The method can include adjusting parameters of the task controller model based on the feedback value.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting a neural network to perform a particular machine learning task while satisfying a set of constraints.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting a neural network to perform a particular machine learning task while satisfying a set of constraints.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting a neural network to perform a particular machine learning task while satisfying a set of constraints.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using neural networks that include one or more conditional convolutional layers. A conditional convolutional layer has a plurality of kernels and determines a respective input-dependent weight for each of the plurality of kernels and generates an input-dependent kernel by computing a weighted sum of the plurality of kernels in accordance with the respective input-dependent weights.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting a neural network to perform a particular machine learning task while satisfying a set of constraints.