Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for image processing using deep neural networks. One of the methods includes receiving data characterizing an input image; processing the data characterizing the input image using a deep neural network to generate an alternative representation of the input image, wherein the deep neural network comprises a plurality of subnetworks, wherein the subnetworks are arranged in a sequence from lowest to highest, and wherein processing the data characterizing the input image using the deep neural network comprises processing the data through each of the subnetworks in the sequence; and processing the alternative representation of the input image through an output layer to generate an output from the input image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for image processing using deep neural networks. One of the methods includes receiving data characterizing an input image; processing the data characterizing the input image using a deep neural network to generate an alternative representation of the input image, wherein the deep neural network comprises a plurality of subnetworks, wherein the subnetworks are arranged in a sequence from lowest to highest, and wherein processing the data characterizing the input image using the deep neural network comprises processing the data through each of the subnetworks in the sequence; and processing the alternative representation of the input image through an output layer to generate an output from the input image.

Abstract: Systems and methods of identifying entities are disclosed. In particular, one or more images that depict an entity can be identified from a plurality of images. One or more candidate entity profiles can be determined from an entity directory based at least in part on the one or more images that depict the entity. The one or more images that depict the entity and the one or more candidate entity profiles can be provided as input to a machine learning model. One or more outputs of the machine learning model can be generated. Each output can include a match score associated with an image that depicts the entity and at least one candidate entity profile. The entity directory can be updated based at least in part on the one or more generated outputs of the machine learning model.

Abstract: A neural network system that includes: multiple subnetworks that includes: a first subnetwork including multiple first modules, each first module including: a pass-through convolutional layer configured to process the subnetwork input for the first subnetwork to generate a pass-through output; an average pooling stack of neural network layers that collectively processes the subnetwork input for the first subnetwork to generate an average pooling output; a first stack of convolutional neural network layers configured to collectively process the subnetwork input for the first subnetwork to generate a first stack output; a second stack of convolutional neural network layers that are configured to collectively process the subnetwork input for the first subnetwork to generate a second stack output; and a concatenation layer configured to concatenate the pass-through output, the average pooling output, the first stack output, and the second stack output to generate a first module output for the first module.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for image processing using deep neural networks. One of the methods includes receiving data characterizing an input image; processing the data characterizing the input image using a deep neural network to generate an alternative representation of the input image, wherein the deep neural network comprises a plurality of subnetworks, wherein the subnetworks are arranged in a sequence from lowest to highest, and wherein processing the data characterizing the input image using the deep neural network comprises processing the data through each of the subnetworks in the sequence; and processing the alternative representation of the input image through an output layer to generate an output from the input image.

Abstract: Aspects of the present disclosure relate to a method includes training a deep neural network using training images and data identifying one or more business storefront locations in the training images. The deep neural network outputs tight bounding boxes on each image. At the deep neural network, a first image may be received. The first image may be evaluated using the deep neural network. Bounding boxes may then be generated identifying business storefront locations in the first image.

Abstract: Aspects of the present disclosure relate to a method includes training a deep neural network using training images and data identifying one or more business storefront locations in the training images. The deep neural network outputs tight bounding boxes on each image. At the deep neural network, a first image may be received. The first image may be evaluated using the deep neural network. Bounding boxes may then be generated identifying business storefront locations in the first image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to detect object in images. One of the methods includes receiving a training image and object location data for the training image; providing the training image to a neural network and obtaining bounding box data for the training image from the neural network, wherein the bounding box data comprises data defining a plurality of candidate bounding boxes in the training image and a respective confidence score for each candidate bounding box in the training image; determining an optimal set of assignments using the object location data for the training image and the bounding box data for the training image, wherein the optimal set of assignments assigns a respective candidate bounding box to each of the object locations; and training the neural network on the training image using the optimal set of assignments.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to detect object in images. One of the methods includes receiving a training image and object location data for the training image; providing the training image to a neural network and obtaining bounding box data for the training image from the neural network, wherein the bounding box data comprises data defining a plurality of candidate bounding boxes in the training image and a respective confidence score for each candidate bounding box in the training image; determining an optimal set of assignments using the object location data for the training image and the bounding box data for the training image, wherein the optimal set of assignments assigns a respective candidate bounding box to each of the object locations; and training the neural network on the training image using the optimal set of assignments.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for image processing using deep neural networks. One of the methods includes receiving data characterizing an input image; processing the data characterizing the input image using a deep neural network to generate an alternative representation of the input image, wherein the deep neural network comprises a plurality of subnetworks, wherein the subnetworks are arranged in a sequence from lowest to highest, and wherein processing the data characterizing the input image using the deep neural network comprises processing the data through each of the subnetworks in the sequence; and processing the alternative representation of the input image through an output layer to generate an output from the input image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting objects in images. One of the methods includes receiving an input image. A full object mask is generated by providing the input image to a first deep neural network object detector that produces a full object mask for an object of a particular object type depicted in the input image. A partial object mask is generated by providing the input image to a second deep neural network object detector that produces a partial object mask for a portion of the object of the particular object type depicted in the input image. A bounding box is determined for the object in the image using the full object mask and the partial object mask.

Abstract: Aspects of the present disclosure relate generally to model fitting. A target model having a large number of inputs is fit using a performance model having relatively few inputs. The performance model is learned during the fitting process. Optimal optimization parameters including a sample size, a damping factor, and an iteration count are selected for an optimization round. A random subset of data is sampled based on the selected sample size. The optimization round is conducted using the iteration count and the sampled data to produce optimized parameters. The performance model is updated based on the performance of the optimization round. The parameters of the target model are then updated based on the damping factor and the parameters computed by the optimization round. The aforementioned steps are performed in a loop in order to obtain optimized parameters and fit of the data to the target model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting objects in images. One of the methods includes receiving an input image. A full object mask is generated by providing the input image to a first deep neural network object detector that produces a full object mask for an object of a particular object type depicted in the input image. A partial object mask is generated by providing the input image to a second deep neural network object detector that produces a partial object mask for a portion of the object of the particular object type depicted in the input image. A bounding box is determined for the object in the image using the full object mask and the partial object mask.

Abstract: Aspects of the present disclosure relate generally to model fitting. A target model having a large number of inputs is fit using a performance model having relatively few inputs. The performance model is learned during the fitting process. Optimal optimization parameters including a sample size, a damping factor, and an iteration count are selected for an optimization round. A random subset of data is sampled based on the selected sample size. The optimization round is conducted using the iteration count and the sampled data to produce optimized parameters. The performance model is updated based on the performance of the optimization round. The parameters of the target model are then updated based on the damping factor and the parameters computed by the optimization round. The aforementioned steps are performed in a loop in order to obtain optimized parameters and fit of the data to the target model.

Abstract: Disclosed are method, system, and computer program product for a method and system for a fast and stable placement/floorplanning method that gives consistent and good quality results. Various embodiments of the present invention provide a method and system for approximate placement of various standard cells, macro-blocks, and I/O pads for the design of integrated circuits by approximating the final shapes of the objects of interest by one or more probability distribution functions over the areas for the objects of interest with improved runtime and very good stability. These probability distributions are gradually localized to final shapes satisfying the placement constraints and optimizing an objective function.

Abstract: Disclosed are method, system, and computer program product for a method and system for a fast and stable placement/floorplanning method that gives consistent and good quality results. Various embodiments of the present invention provide a method and system for approximate placement of various standard cells, macro-blocks, and I/O pads for the design of integrated circuits by approximating the final shapes of the objects of interest by one or more probability distribution functions over the areas for the objects of interest with improved runtime and very good stability. These probability distributions are gradually localized to final shapes satisfying the placement constraints and optimizing an objective function.

Abstract: Disclosed are a method, system, and computer program product for implementing incremental placement for an electronic design while predicting and minimizing a perturbation impact arising from incremental placement of electronic components. In some embodiments, an initial placement of an electronic design is identified, an abstract flow is computed, target locations of various electronic components to be placed are identified, a relative ordering of electronic components is determined, and the placement is then legalized. Furthermore, in various embodiments, the method, system, or computer program product starts with an initial placement of an electronic design and derives a legal placement by using an incremental placement technique while minimizing the perturbation impact or an total quadratic movement of instances.

Abstract: Disclosed are method, system, and computer program product for a method and system for a fast and stable placement/floorplanning method that gives consistent and good quality results. Various embodiments of the present invention provide a method and system for approximate placement of various standard cells, macro-blocks, and I/O pads for the design of integrated circuits by approximating the final shapes of the objects of interest by one or more probability distribution functions over the areas for the objects of interest with improved runtime and very good stability. These probability distributions are gradually localized to final shapes satisfying the placement constraints and optimizing an objective function.

Abstract: Disclosed are methods, systems, and computer program products for performing grid morphing technique for computing a spreading of objects over an area such that the final locations of the objects are distributed over the area and such that the final locations of the objects are minimally perturbed from their initial starting locations and the density of objects meets certain constraints. The minimization of perturbation, or stability, of the approaches disclosed, is the key feature which is the principal benefit of the techniques disclosed. The methods described herein may be used as part of a tool for placement or floorplanning of logic gates or larger macroblocks for the design of an integrated circuit.

Abstract: Disclosed are a method, system, and computer program product for implementing incremental placement for an electronic design while predicting and minimizing the perturbation impact arising from incremental placement of electronic components. In some embodiments, an initial placement of an electronic design is identified, the abstract flow is computed, the target locations of various electronic components to be placed are identified, the relative ordering of electronic components are determined, and the placement is then legalized. Furthermore, in various embodiments, the method, system, or computer program product starts with an initial placement of an electronic design and derives a legal placement by using the incremental placement technique while minimizing the perturbation impact or the total quadratic movement of instances.