Patents by Inventor Dilip Krishnan
Dilip Krishnan has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11900075Abstract: In some implementations, a device may generate, based at least in part on a first set of inputs, a serverless software development environment associated with a set of cloud resources. The device may generate, based at least in part on a first machine learning model, a technology stack recommendation having a set of associated tools for performing a software development task. The device may instantiate the selected technology stack in the serverless software development environment and generate a set of applications based at least in part on executing the set of tools. The device may deploy the set of applications in one or more serverless application environments. The device may use machine learning to observe deployed applications, detect hidden anomalies, and perform root-cause analysis, thereby providing a lean and sustainable serverless environment.Type: GrantFiled: March 31, 2022Date of Patent: February 13, 2024Assignee: Accenture Global Solutions LimitedInventors: Rajendra Prasad Tanniru, Aditi Kulkarni, Koushik M. Vijayaraghavan, Vijeth Srinivas Hegde, Ravindra Kabbinale, Sreenath Kothavoor, Amrutha Pervody Bhat, Meghana B Srinath, Ravi Kiran Singh, Dilip Krishnan, Naveen Raj K P, Sumanth Channegowda, Vinay Chamarthi, Lakshmi Srinivasan, Santhosh Mv
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Publication number: 20230315397Abstract: In some implementations, a device may generate, based at least in part on a first set of inputs, a serverless software development environment associated with a set of cloud resources. The device may generate, based at least in part on a first machine learning model, a technology stack recommendation having a set of associated tools for performing a software development task. The device may instantiate the selected technology stack in the serverless software development environment and generate a set of applications based at least in part on executing the set of tools. The device may deploy the set of applications in one or more serverless application environments. The device may use machine learning to observe deployed applications, detect hidden anomalies, and perform root-cause analysis, thereby providing a lean and sustainable serverless environment.Type: ApplicationFiled: March 31, 2022Publication date: October 5, 2023Inventors: Rajendra PRASAD TANNIRU, Aditi KULKARNI, Koushik M. VIJAYARAGHAVAN, Vijeth SRINIVAS HEGDE, Ravindra KABBINALE, Sreenath KOTHAVOOR, Amrutha PERVODY BHAT, Meghana B SRINATH, Ravi Kiran SINGH, Dilip KRISHNAN, Naveen RAJ K P, Sumanth CHANNEGOWDA, Vinay CHAMARTHI, Lakshmi SRINIVASAN, Santhosh MV
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Patent number: 11775830Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes processing each training input using the neural network and in accordance with the current values of the network parameters to generate a network output for the training input; computing a respective loss for each of the training inputs by evaluating a loss function; identifying, from a plurality of possible perturbations, a maximally non-linear perturbation; and determining an update to the current values of the parameters of the neural network by performing an iteration of a neural network training procedure to decrease the respective losses for the training inputs and to decrease the non-linearity of the loss function for the identified maximally non-linear perturbation.Type: GrantFiled: December 12, 2022Date of Patent: October 3, 2023Assignee: DeepMind Technologies LimitedInventors: Chongli Qin, Sven Adrian Gowal, Soham De, Robert Stanforth, James Martens, Krishnamurthy Dvijotham, Dilip Krishnan, Alhussein Fawzi
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Publication number: 20230252286Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes processing each training input using the neural network and in accordance with the current values of the network parameters to generate a network output for the training input; computing a respective loss for each of the training inputs by evaluating a loss function; identifying, from a plurality of possible perturbations, a maximally non-linear perturbation; and determining an update to the current values of the parameters of the neural network by performing an iteration of a neural network training procedure to decrease the respective losses for the training inputs and to decrease the non-linearity of the loss function for the identified maximally non-linear perturbation.Type: ApplicationFiled: December 12, 2022Publication date: August 10, 2023Inventors: Chongli Qin, Sven Adrian Gowal, Soham De, Robert Stanforth, James Martens, Krishnamurthy Dvijotham, Dilip Krishnan, Alhussein Fawzi
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Publication number: 20230244392Abstract: Techniques are provided for input/output operations per second (lOPS) and throughput monitoring for dynamic and/or optimal resource allocation. These techniques provide automated monitoring of resources, such as memory and processor utilization by a container accessing a volume. The automated monitoring is performed in order to generate and execute intelligent recommendations for improved resource utilization. Resource allocations can be scaled up to meet l/O load demand and satisfy service level agreements (SLAs). Resource allocations can be scaled down or adjusted to conserve resources, such as by consolidating containers or pods hosted in multiple virtual machines into a single virtual machine and decommissioning virtual machines no longer hosting containers or pods.Type: ApplicationFiled: January 28, 2022Publication date: August 3, 2023Inventors: Priyanka Jain, Srikanth Venkatesh Goutham, Dilip Krishnan, Suruchi Kumari, Rama Kant Pathak, Arun Pandey, Venkata Manikanta Reddy Mopuri
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Publication number: 20230153629Abstract: The present disclosure provides an improved training methodology that enables supervised contrastive learning to be simultaneously performed across multiple positive and negative training examples. In particular, example aspects of the present disclosure are directed to an improved, supervised version of the batch contrastive loss, which has been shown to be very effective at learning powerful representations in the self-supervised setting Thus, the proposed techniques adapt contrastive learning to the fully supervised setting and also enable learning to occur simultaneously across multiple positive examples.Type: ApplicationFiled: April 12, 2021Publication date: May 18, 2023Inventors: Dilip Krishnan, Prannay Khosla, Piotr Teterwak, Aaron Yehuda Sarna, Aaron Joseph Maschinot, Ce Liu, Philip John Isola, Yonglong Tian, Chen Wang
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Patent number: 11526755Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes processing each training input using the neural network and in accordance with the current values of the network parameters to generate a network output for the training input; computing a respective loss for each of the training inputs by evaluating a loss function; identifying, from a plurality of possible perturbations, a maximally non-linear perturbation; and determining an update to the current values of the parameters of the neural network by performing an iteration of a neural network training procedure to decrease the respective losses for the training inputs and to decrease the non-linearity of the loss function for the identified maximally non-linear perturbation.Type: GrantFiled: May 22, 2020Date of Patent: December 13, 2022Assignee: DeepMind Technologies LimitedInventors: Chongli Qin, Sven Adrian Gowal, Soham De, Robert Stanforth, James Martens, Krishnamurthy Dvijotham, Dilip Krishnan, Alhussein Fawzi
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Publication number: 20220270402Abstract: The present disclosure provides systems and methods that perform face reconstruction based on an image of a face. In particular, one example system of the present disclosure combines a machine-learned image recognition model with a face modeler that uses a morphable model of a human's facial appearance. The image recognition model can be a deep learning model that generates an embedding in response to receipt of an image (e.g., an uncontrolled image of a face). The example system can further include a small, lightweight, translation model structurally positioned between the image recognition model and the face modeler. The translation model can be a machine-learned model that is trained to receive the embedding generated by the image recognition model and, in response, output a plurality of facial modeling parameter values usable by the face modeler to generate a model of the face.Type: ApplicationFiled: May 16, 2022Publication date: August 25, 2022Inventors: Forrester H. Cole, Dilip Krishnan, William T. Freeman, David Benjamin Belanger
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Patent number: 11361531Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using an image processing neural network system. One of the system includes a shared encoder neural network implemented by one or more computers, wherein the shared encoder neural network is configured to: receive an input image from a target domain; and process the input image to generate a shared feature representation of features of the input image that are shared between images from the target domain and images from a source domain different from the target domain; and a classifier neural network implemented by the one or more computers, wherein the classifier neural network is configured to: receive the shared feature representation; and process the shared feature representation to generate a network output for the input image that characterizes the input image.Type: GrantFiled: April 5, 2021Date of Patent: June 14, 2022Assignee: Google LLCInventors: Konstantinos Bousmalis, Nathan Silberman, Dilip Krishnan, George Trigeorgis, Dumitru Erhan
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Patent number: 11347975Abstract: The present disclosure provides an improved training methodology that enables supervised contrastive learning to be simultaneously performed across multiple positive and negative training examples. In particular, example aspects of the present disclosure are directed to an improved, supervised version of the batch contrastive loss, which has been shown to be very effective at learning powerful representations in the self-supervised setting. Thus, the proposed techniques adapt contrastive learning to the fully supervised setting and also enable learning to occur simultaneously across multiple positive examples.Type: GrantFiled: April 21, 2021Date of Patent: May 31, 2022Assignee: GOOGLE LLCInventors: Dilip Krishnan, Prannay Khosla, Piotr Teterwak, Aaron Yehuda Sarna, Aaron Joseph Maschinot, Ce Liu, Phillip John Isola, Yonglong Tian, Chen Wang
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Patent number: 11335120Abstract: The present disclosure provides systems and methods that perform face reconstruction based on an image of a face. In particular, one example system of the present disclosure combines a machine-learned image recognition model with a face modeler that uses a morphable model of a human's facial appearance. The image recognition model can be a deep learning model that generates an embedding in response to receipt of an image (e.g., an uncontrolled image of a face). The example system can further include a small, lightweight, translation model structurally positioned between the image recognition model and the face modeler. The translation model can be a machine-learned model that is trained to receive the embedding generated by the image recognition model and, in response, output a plurality of facial modeling parameter values usable by the face modeler to generate a model of the face.Type: GrantFiled: April 24, 2020Date of Patent: May 17, 2022Assignee: GOOGLE LLCInventors: Forrester H. Cole, Dilip Krishnan, William T. Freeman, David Benjamin Belanger
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Publication number: 20220148299Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating realistic extensions of images. In one aspect, a method comprises providing an input that comprises a provided image to a generative neural network having a plurality of generative neural network parameters. The generative neural network processes the input in accordance with trained values of the plurality of generative neural network parameters to generate an extended image. The extended image has (i) more rows, more columns, or both than the provided image, and (ii) is predicted to be a realistic extension of the provided image. The generative neural network is trained using an adversarial loss objective function.Type: ApplicationFiled: July 19, 2019Publication date: May 12, 2022Inventors: Mikael Pierre Bonnevie, Aaron Maschinot, Aaron Sarna, Shuchao Bi, Jingbin Wang, Michael Spencer Krainin, Wenchao Tong, Dilip Krishnan, Haifeng Gong, Ce Liu, Hossein Talebi, Raanan Sayag, Piotr Teterwak
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Publication number: 20210326660Abstract: The present disclosure provides an improved training methodology that enables supervised contrastive learning to be simultaneously performed across multiple positive and negative training examples. In particular, example aspects of the present disclosure are directed to an improved, supervised version of the batch contrastive loss, which has been shown to be very effective at learning powerful representations in the self-supervised setting. Thus, the proposed techniques adapt contrastive learning to the fully supervised setting and also enable learning to occur simultaneously across multiple positive examples.Type: ApplicationFiled: April 21, 2021Publication date: October 21, 2021Inventors: Dilip Krishnan, Prannay Khosla, Piotr Teterwak, Aaron Yehuda Sarna, Aaron Joseph Maschinot, Ce Liu, Phillip John Isola, Yonglong Tian, Chen Wang
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Patent number: 11151423Abstract: Insects can be localized and classified using a predictive model. To begin, image data is obtained that corresponds to the insects. Using a predictive model, samples of the image data are evaluated to determine whether the image portions include an insect and, if so, into what category the insect should be classified (e.g., male/female, species A/species B, etc.).Type: GrantFiled: October 27, 2017Date of Patent: October 19, 2021Assignee: VERILY LIFE SCIENCES LLCInventors: Tiantian Zha, Yaniv Ovadia, Daniel Newburger, Dilip Krishnan, Josh Livni, Mark Desnoyer
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Publication number: 20210224573Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using an image processing neural network system. One of the system includes a shared encoder neural network implemented by one or more computers, wherein the shared encoder neural network is configured to: receive an input image from a target domain; and process the input image to generate a shared feature representation of features of the input image that are shared between images from the target domain and images from a source domain different from the target domain; and a classifier neural network implemented by the one or more computers, wherein the classifier neural network is configured to: receive the shared feature representation; and process the shared feature representation to generate a network output for the input image that characterizes the input image.Type: ApplicationFiled: April 5, 2021Publication date: July 22, 2021Inventors: Konstantinos Bousmalis, Nathan Silberman, Dilip Krishnan, George Trigeorgis, Dumitru Erhan
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Patent number: 10991074Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using an image processing neural network system. One of the systems includes a domain transformation neural network implemented by one or more computers, wherein the domain transformation neural network is configured to: receive an input image from a source domain; and process a network input comprising the input image from the source domain to generate a transformed image that is a transformation of the input image from the source domain to a target domain that is different from the source domain.Type: GrantFiled: June 14, 2019Date of Patent: April 27, 2021Assignee: Google LLCInventors: Konstantinos Bousmalis, Nathan Silberman, David Martin Dohan, Dumitru Erhan, Dilip Krishnan
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Patent number: 10970589Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using an image processing neural network system. One of the system includes a shared encoder neural network implemented by one or more computers, wherein the shared encoder neural network is configured to: receive an input image from a target domain; and process the input image to generate a shared feature representation of features of the input image that are shared between images from the target domain and images from a source domain different from the target domain; and a classifier neural network implemented by the one or more computers, wherein the classifier neural network is configured to: receive the shared feature representation; and process the shared feature representation to generate a network output for the input image that characterizes the input image.Type: GrantFiled: July 28, 2016Date of Patent: April 6, 2021Assignee: Google LLCInventors: Konstantinos Bousmalis, Nathan Silberman, Dilip Krishnan, George Trigeorgis, Dumitru Erhan
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Patent number: 10853987Abstract: A system and method for generating cartoon images from photos are described. The method includes receiving an image of a user, determining a template for a cartoon avatar, determining an attribute needed for the template, processing the image with a classifier trained for classifying the attribute included in the image, determining a label generated by the classifier for the attribute, determining a cartoon asset for the attribute based on the label, and rendering the cartoon avatar personifying the user using the cartoon asset.Type: GrantFiled: December 3, 2019Date of Patent: December 1, 2020Assignee: Google LLCInventors: Aaron Sarna, Dilip Krishnan, Forrester Cole, Inbar Mosseri
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Publication number: 20200349668Abstract: Insects can be classified into a category (e.g., sex category, species category, size category, etc.) using a variety of different classification approaches including, for example, an industrial vision classifier and/or a machine learning classifier. At least some classification approaches may be used in real-time to make real-time decisions and others can be used to validate earlier-made real-time decisions.Type: ApplicationFiled: April 27, 2020Publication date: November 5, 2020Applicant: Verily Life Sciences LLCInventors: Mark Desnoyer, Victor Criswell, Josh Livni, Yaniv Ovadia, Peter Massaro, Nigel Snoad, Dilip Krishnan, Yi Han, Tiantian Zha
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Publication number: 20200257891Abstract: The present disclosure provides systems and methods that perform face reconstruction based on an image of a face. In particular, one example system of the present disclosure combines a machine-learned image recognition model with a face modeler that uses a morphable model of a human's facial appearance. The image recognition model can be a deep learning model that generates an embedding in response to receipt of an image (e.g., an uncontrolled image of a face). The example system can further include a small, lightweight, translation model structurally positioned between the image recognition model and the face modeler. The translation model can be a machine-learned model that is trained to receive the embedding generated by the image recognition model and, in response, output a plurality of facial modeling parameter values usable by the face modeler to generate a model of the face.Type: ApplicationFiled: April 24, 2020Publication date: August 13, 2020Inventors: Forrester H. Cole, Dilip Krishnan, William T. Freeman, David Benjamin Belanger